Time delay determination method and device, equipment, storage medium and computer program product

Abstract

The invention discloses a time delay determination method and device, equipment, a storage medium and a computer program product. The method comprises the steps that a location management function (LMF) receives first data; the first data comprises measurement data related to an uplink relative time of arrival (UL-RTOA); determining time delay by using the first data and the second data; the second data comprises the position data of the TRP and the position data of the UE. By adopting the scheme provided by the embodiment of the invention, the time delay determination is not influenced by the hardware reason of the base station, so that the time delay deviation is reduced, the time delay precision and reliability are improved, and the positioning precision and availability of the UL-TDOA positioning method are further improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, apparatus, device, storage medium, and computer program product for determining a time delay. Background Art

[0002] At present, in the related technology of the fifth-generation mobile communication technology (5G, 5th-Generation Mobile Communication Technology) new air interface (NR, New Redio) uplink arrival time difference (UL-TDOA, Uplink Time Difference of Arrival) positioning method, since the actual uplink relative arrival time (UL-RTOA, Uplink Relative Time of Arrival) in the base station is measured on the indoor baseband processing unit (BBU, Building Baseband Unit) side, the UL-RTOA measurement includes the processing delay mainly based on the radio frequency receiving channel from the transmission-reception point (TRP, BBU) to the BBU. Due to hardware reasons, the radio frequency receiving channel delays from different TRPs to the BBU are generally different. Therefore, the TDOA relative measurement value formed by the UL-RTOA measurement value reported by the base station has a delay deviation between the base station radio frequency receiving channels. This deviation makes the accuracy and reliability of the delay between the base station radio frequency receiving channels low, thereby resulting in limited positioning accuracy and insufficient availability of the UL-TDOA positioning method. Summary of the Invention

[0003] To solve the technical problems existing in the related art, the embodiments of the present application provide a method, apparatus, device, storage medium and computer program product for determining a time delay.

[0004] To achieve the above objectives, the technical solution of the embodiment of the present application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for determining a delay, the method comprising:

[0006] A location management function (LMF) receives first data, wherein the first data includes measurement data related to the UL-RTOA;

[0007] The delay is determined using the first data and the second data; the second data includes the location data of the TRP and the location data of the user equipment (UE).

[0008] In a second aspect, an embodiment of the present application provides a delay determination device, the device comprising:

[0009] A first receiving unit is configured to receive first data, wherein the first data includes measurement data related to UL-RTOA;

[0010] The first determination unit is used to determine the delay using the first data and the second data; the second data includes the location data of the TRP and the location data of the UE.

[0011] In a third aspect, an embodiment of the present application further provides a delay determination device, comprising: a processor and a memory for storing a computer program capable of running on the processor;

[0012] The processor is configured to execute the steps of the delay determination method described in the embodiment of the present application when running the computer program.

[0013] In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the delay determination method described in the embodiment of the present application are implemented.

[0014] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the delay determination method described in the embodiment of the present application.

[0015] The delay determination method, apparatus, device, storage medium, and computer program product provided in the embodiments of the present application include: an LMF receiving first data; the first data including measurement data related to UL-RTOA; the delay is determined using the first data and the second data; the second data including the location data of the TRP and the location data of the UE. By adopting the technical solution of the embodiments of the present application, by using the measurement data related to UL-RTOA, the location data of the TRP, and the location data of the UE as input data for delay determination, the delay determination is not affected by base station hardware, thereby reducing delay deviation, improving the accuracy and reliability of the delay, and further improving the positioning accuracy and availability of the UL-TDOA positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 1 ;

[0017] Figure 2 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 2 ;

[0018] Figure 3 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 3 ;

[0019] Figure 4 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 4 ;

[0020] Figure 5 This is a timing diagram of estimating the relative radio frequency channel delay 91-92 of the beacon UE1 according to an embodiment of the present application;

[0021] Figure 6 This is a timing diagram of estimating the relative radio frequency channel delay 91-92 of the beacon UE2 in an embodiment of the present application;

[0022] Figure 7 This is a schematic diagram of the structure of the delay determination device according to an embodiment of the present application;

[0023] Figure 8 This is a schematic diagram of the hardware structure of the delay determination device according to an embodiment of the present application;

[0024] Figure 9 Schematic diagram of the structure of the delay determination system according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0027] In the related art, the wireless signal transmission, reception and processing process of the 5G NR UL-TDOA positioning method is that the UE sends an uplink sounding reference signal (UL-SRS) for positioning, and then the TRPs receive the UL-SRS. The UL-SRS on different TRP sides are processed by different RF receiving channels and then transmitted to the BBU side for channel estimation and UL-RTOA measurement. The relative air interface delay corresponding to the relative UL-RTOA on the TRP side is valid data for UL-TDOA geometric positioning solution. Since the actual UL-RTOA in the base station is measured on the BBU side, the UL-RTOA measurement includes the processing delay from the TRP to the BBU, which is mainly based on the RF receiving channel. Due to hardware reasons, the RF receiving channel delays from different TRPs to the BBU are generally different. Therefore, the TDOA relative measurement value formed by the UL-RTOA measurement value reported by the base station has a delay deviation between the base station RF receiving channels.

[0028] This deviation has nothing to do with the UE position, but is related to the wireless side system. The size and magnitude of the deviation between the base station RF receiving channels corresponding to different TRPs are irregular. Under normal circumstances, the deviation has a slow time-varying property. However, when the cell undergoes adjustments such as reset and restart, or a failure occurs, the deviation will jump, that is, the delay state between the base station RF receiving channels will jump, or the delay measurement data between the base station RF receiving channels, such as TDOA measurement data, will have wild values. Both phenomena will reduce the accuracy and reliability of the delay estimation, resulting in limited positioning accuracy and insufficient availability of the UL-TDOA positioning method.

[0029] Based on this, an embodiment of the present application proposes a method for delay determination. In various embodiments of the present application, by using measurement data related to UL-RTOA, as well as the location data of TRP and the location data of UE as input data for delay determination, the delay determination is not affected by base station hardware reasons, thereby reducing delay deviation, improving the accuracy and reliability of delay, and thereby improving the positioning accuracy and availability of the UL-TDOA positioning method.

[0030] The embodiment of the present application provides a method for determining a time delay, which is applied to LMF. Figure 1 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 1 ,like Figure 1 As shown, the method includes:

[0031] Step 101: Receive first data; the first data includes measurement data related to UL-RTOA.

[0032] Step 102: Determine the delay using the first data and the second data; the second data includes the location data of the TRP and the location data of the UE.

[0033] Here, the delay determination method may also be referred to as a delay determination method for UL-TDOA positioning, or a delay determination method for 5G NR UL-TDOA positioning. The embodiment of the present application does not limit the name of the delay determination method. It should be noted that the delay determination method in the embodiment of the present application can be used for UL-TDOA indoor positioning or for UL-TDOA outdoor positioning.

[0034] Here, delay determination may also be referred to as delay estimation, or delay calculation. The embodiment of the present application does not limit the name of delay determination.

[0035] Here, the first data received by the LMF may be sent by the base station. In actual application, the LMF may directly receive the first data sent by the base station, or may receive the first data sent by the base station in a response manner.

[0036] Based on this, in one embodiment, the receiving of the first data includes:

[0037] Sending a first message to a base station; the first message includes configuration information of a reference signal for positioning and information required to perform UL-RTOA measurement;

[0038] Based on the first message, a second message sent by the base station is received; the second message includes the first data.

[0039] Here, the first message is used to request measurements related to UL-RTOA, and the first message may be a measurement request message, such as a New Radio Positioning Protocol Annex (NRPPa) measurement request (MEASUREMENT REQUEST) message. The first message includes configuration information of a reference signal for positioning and information required to perform UL-RTOA measurements; that is, the LMF provides the base station with configuration information of a reference signal for positioning and information required to perform UL-RTOA measurements in the first message, wherein the reference signal for positioning may be an SRS, specifically a UL-SRS; and the base station may be a serving base station, such as a serving next generation base station (gNB, the next Generation Node B).

[0040] Here, the LMF sends a first message, namely a measurement request message, to the base station. After receiving the first message, the base station can perform UL-RTOA measurement based on the configuration information of the reference signal for positioning contained in the first message and the information required to perform UL-RTOA measurement. Then, the base station sends a second message to the LMF. The second message can be a measurement response message, which carries the first data. In this way, the LMF can receive the first data through the base station response. The measurement response message can be, for example, an NRPPa measurement response (MEASUREMENT RESPONSE) message.

[0041] Here, the measurement data related to UL-RTOA can be referred to as UL-RTOA-related measurements, or reported UL-RTOA-related measurements, or UL-RTOA-related measurement reports, or UL-RTOA-related measurement values, or UL-RTOA measurement values, which can be expressed as UL-RTOAAMEASUREMENT in English. The embodiment of the present application does not limit the name of the measurement data related to UL-RTOA. Among them, the reported UL-RTOA-related measurements can be understood as UL-RTOA-related measurements reported by the base station to the LMF.

[0042] Here, the TRP can be understood as a transceiver antenna of a base station. In actual applications, there may be multiple TRPs. The UE is capable of at least transmitting a reference signal for positioning, for example, the UE is capable of at least transmitting a UL-SRS for positioning. The UE may be a beacon UE, and the number of the beacon UEs may be one or more.

[0043] In actual application, the embodiments of the present application put forward relevant requirements for UE, such as beacon UE, namely target selection requirements and target location principles.

[0044] Based on this, in one embodiment, the UE is one or more, and the one or more UEs meet the target selection requirements and target site selection principles; wherein any TRP forms a line-of-sight relationship with the one or more UEs; when there are multiple UEs, the line-of-sight TRP sets of adjacent UEs have an intersection.

[0045] Here, the UE is one or more, which can be referred to as the number of the UE being one or more.

[0046] Here, the target selection requirement may refer to selecting a 5G low-power terminal that meets the commercial communication protocol requirement 16 (R16, Release 16) or higher and supports SRS signal transmission. The SRS may be, for example, a Sounding Reference Signal-Positioning (SRS-Pos).

[0047] Here, the target site selection principles may include the following principles: 1) forming a line-of-sight relationship with as many TRPs as possible; 2) having a good receiving signal-to-noise ratio; 3) not being easily affected by interference from changes in the surrounding environment; 4) being easy to install, deploy, and maintain; 5) not affecting indoor business operations and production operations (this principle is for UL-TDOA indoor positioning).

[0048] Here, the planning requirements for UEs can be based on the distribution of TRPs (such as indoor TRPs), and according to the above-mentioned target selection requirements and target site selection principles for UEs, one or more UEs can be planned to cover all TRPs. It should be noted that any TRP among all TRPs forms a line-of-sight relationship with one or more UEs. When planning multiple UEs, it is necessary to meet the intersection of the line-of-sight TRP sets of adjacent UEs.

[0049] Here, the second data includes the location data of the TRP and the location data of the UE. Specifically, the second data may include the location data of multiple TRPs and the location data of one or more UEs. It should be noted that in actual application, the location data (i.e., the second data) can be acquired by defining a coordinate system, and after the second data is acquired, it is pre-stored on the LMF side.

[0050] Here, the location data acquisition process includes two parts: coordinate system definition and coordinate measurement. Taking UL-TDOA indoor positioning as an example, for the coordinate system definition part, the coordinate system can be defined indoors first, and the coordinate system origin O, two mutually perpendicular horizontal directions x and y, and the vertical direction h are selected to customize the local spatial rectangular coordinate system O-xyh. For the coordinate measurement part, high-precision coordinate measurement methods can be used indoors, such as the total station traverse measurement method, to calibrate the positions of all TRPs and UEs indoors, obtain the position data of multiple TRPs and the position data of one or more UEs, and store the obtained TRP position data and UE position data on the LMF side.

[0051] Here, the delay determined by LMF using the first data and the second data can also be called the delay related to TDOA, or can also be called the delay estimation related to TDOA, or can also be called the delay estimation related to TDOA, or can also be called the delay between base station RF receiving channels, or can also be called the delay estimation between base station RF receiving channels. The embodiment of the present application does not limit the name of the delay here.

[0052] Here, determining the delay using the first data and the second data can also be called determining the delay through the first data and the second data, or can also be called determining the delay based on the first data and the second data. The embodiment of the present application does not limit the description method of step 102.

[0053] In actual application, LMF can also realize the UE's positioning capability and obtain the configuration information of the UE's reference signal used for positioning through interaction with the UE and the base station.

[0054] Based on this, in one embodiment, the method further includes: acquiring the positioning capability of the UE and configuration information of a reference signal used for positioning of the UE;

[0055] The UE is positioned based on the positioning capability of the UE and configuration information of a reference signal of the UE used for positioning.

[0056] Here, the LMF may be to obtain the positioning capability of the UE and the configuration information of the reference signal for positioning of the UE before receiving the first data. For the acquisition of the positioning capability of the UE, it can be obtained by the LMF sending a fourth message to the UE, wherein the fourth message is used to request the acquisition of the positioning capability of the UE. Specifically, the LMF can use the Long Term Evolution (LTE) Positioning Protocol (LPP, LTE Positioning Protocol) capability transfer procedure to request the acquisition of the positioning capability of the UE. For the acquisition of the configuration information of the reference signal for positioning of the UE, it can be obtained by the LMF sending a fifth message to the base station, wherein the fifth message represents a positioning information request message, such as an NRPPa positioning information request (POSITIONING INFORMATION REQUEST) message, to request the acquisition of the configuration information of the reference signal for positioning of the UE.

[0057] In one embodiment, the method further includes: receiving a third message sent by the base station; the third message includes uplink information, and the uplink information carries configuration information of a reference signal used for positioning of the UE.

[0058] Here, the LMF may receive a third message sent by the base station after sending the fifth message to the UE. The third message represents a positioning information response message, such as an NRPPa positioning information response (POSITIONING INFORMATION RESPONSE) message, and provides uplink information to the LMF through the third message, wherein the uplink information carries configuration information of the reference signal for positioning of the UE.

[0059] In one embodiment, the reference signal used for positioning includes a semi-persistent or aperiodic reference signal, and the method further includes:

[0060] A sixth message is sent to the base station; the sixth message is used to request activation of the transmission of the reference signal used for positioning.

[0061] Here, the LMF may send a sixth message to the base station after receiving the third message sent by the base station. The sixth message may be a positioning activation request message, such as an NRPPa positioning activation request (POSITIONING ACTIVATION REQUEST) message, to request the base station to activate transmission of reference signals for positioning.

[0062] In actual application, after the base station activates the transmission of the reference signal used for positioning, it is also necessary to send a positioning activation response to the LMF.

[0063] Based on this, in one embodiment, the method further includes: receiving a seventh message sent by the base station; the seventh message represents a positioning activation response message.

[0064] Here, the seventh message may be a positioning activation response message, such as an NRPPa positioning activation response (POSITIONING ACTIVATION RESPONSE) message.

[0065] In actual application, after the LMF uses the first data and the second data to determine the delay, positioning deactivation can be achieved through the base station.

[0066] Based on this, in one embodiment, the method further includes: sending an eighth message to the base station; the eighth message is used to instruct the base station to deactivate positioning.

[0067] Here, the eighth message may be a positioning deactivation request message, such as an NRPPa positioning deactivation (POSITIONING DEACTIVATION) request message. The LMF requests the base station to perform positioning deactivation by sending the positioning deactivation request message to the base station.

[0068] In actual application, after determining the time delay using the first data and the second data, the LMF needs to store the determined time delay.

[0069] Based on this, in one embodiment, the method further includes: storing the time delay and the corresponding relative TRP identification (ID).

[0070] Here, after determining the delay, i.e., the delay between base station RF receive channels, the LMF can store the delay between base station RF receive channels and the corresponding relative TRP ID in the database. It should be noted that the newly stored delay (e.g., the delay stored in the database at time k+1) overwrites the previously stored delay (e.g., the delay stored in the database at time k) to avoid database memory overflow.

[0071] In actual application, the existing scheme does not determine the delay through the state estimation method, and there are problems with insufficient reliability and timeliness. The embodiment of the present application applies the means of Kalman filter state estimation to the online real-time determination of delay, that is, LMF can apply the Kalman filter algorithm model to the delay determination process. Therefore, before determining the delay, it is necessary to first construct a Kalman filter measurement equation model and a Kalman filter state equation model, which solves the problem of constructing the state equation model and measurement equation model for delay determination based on Kalman filtering.

[0072] Based on this, in one embodiment, determining the delay by using the first data and the second data includes:

[0073] Based on the first data and the second data, a first equation model is constructed; the first equation model represents a measurement equation model of a Kalman filter;

[0074] Determining a time delay based on the first equation model and the second equation model; the second equation model represents a state equation model of a Kalman filter;

[0075] In which, the delay determination is related to the adjustment of the first covariance matrix and the second covariance matrix, the first covariance matrix represents the measurement noise covariance matrix, and the measurement noise is the noise of the constructed base station RF receiving channel delay measurement value; the second covariance matrix represents the covariance matrix estimate of the first vector, the first vector represents the time prediction value of the state vector of the current epoch, and the state vector is composed of the necessary linearly independent delay parameters to be estimated corresponding to each UE.

[0076] Here, the measurement equation model of the Kalman filter specifically refers to the time delay measurement equation between the base station RF receiving channels; the state equation model of the Kalman filter specifically refers to the random walk process model, that is, the state equation model of the embodiment of the present application reflects the random walk process.

[0077] In actual application, before determining the time delay based on the first and second equation models, the LMF needs to construct a second equation model in addition to the first equation model, thereby using the first and second equation models as input data for the time delay determination. It should be noted that in the embodiment of the present application, it is not necessary to construct the second equation model each time the time delay is determined; instead, the second equation model can be constructed in advance.

[0078] Based on this, in one embodiment, the method further includes: constructing the second equation model. The construction process of the second equation model is described below.

[0079] Specifically, in one embodiment, constructing the second equation model includes:

[0080] Determine a first matrix, a second vector, and a third vector; the first matrix represents a state transfer matrix from the previous epoch of the current epoch to the current epoch, the second vector represents a process noise vector from the previous epoch of the current epoch to the current epoch, and the third vector represents an estimated value of the state vector of the previous epoch of the current epoch;

[0081] The second equation model is constructed based on the first matrix, the second vector and the third vector.

[0082] Here, a single beacon UE u For example, assuming UE uWith TRP1, TRP2, ..., TRP n A line-of-sight relationship can be formed, where n is the number of TRPs and n is a positive integer. The state vector X is composed of the necessary linearly independent base station radio frequency receiving channel delay parameters to be estimated and is an n-1 dimensional vector. There are many ways to construct the state vector X. For ease of description, the embodiment of the present application sets the state vector X to X=(s 2,1 s 3,1 …s n,1 ) T , where s 2,1 Indicates a single beacon UE u The delay parameter to be estimated between the base station RF receiving channels from TRP1 to TRP2, s 3,1 Indicates a single beacon UE u The delay parameter between the base station RF receiving channels from TRP1 to TRP3 is to be estimated, s n,1 Indicates a single beacon UE u From TRP1 to TRP n The delay parameter between base station RF receiving channels needs to be estimated.

[0083] Here, assuming that the current epoch can be represented by time k and the previous epoch can be represented by time k-1, the second equation model can be constructed using the following formula (1):

[0084]

[0085] in, Represents the estimated value of the state vector of the current epoch (corresponding to the fifth vector, which is a posterior estimate); Represents the estimated value of the state vector of the previous epoch of the current epoch (corresponding to the third vector); Φ k,k-1 represents the state transition matrix from time k-1 to time k (corresponding to the first matrix), Φ k,k-1 =I n-1 , I n-1 represents the identity matrix; w k represents the process noise vector from time k-1 to time k (corresponding to the second vector), w k The covariance matrix can be expressed as Q k , Q k =(q△t)·I n-1 , Q k is a non-negative definite matrix, where q represents the process noise power spectral density (in ns / Hz) and △t represents the sampling time interval.

[0086] The construction process of the first equation model is described below.

[0087] Specifically, in one embodiment, constructing a first equation model based on the first data and the second data includes:

[0088] Determine a base station radio frequency receive channel delay measurement value for a current epoch based on the first data and the second data;

[0089] The first equation model is constructed based on the base station radio frequency receiving channel delay measurement value and delay measurement noise of the current epoch.

[0090] In the embodiment of the present application, the delay measurement noise may specifically refer to the delay measurement noise between base station radio frequency receiving channels.

[0091] Here, in one embodiment, determining the base station radio frequency receiving channel inter-channel delay measurement value of the current epoch based on the first data and the second data includes:

[0092] Determining a TDOA relative ranging measurement equation based on the first data and the second data;

[0093] Based on the TDOA relative ranging measurement equation, determine the base station radio frequency receiving channel delay measurement value of the current epoch.

[0094] Here, determining the TDOA relative ranging measurement equation based on the first data and the second data includes: determining the TDOA relative ranging value based on the first data; and determining the TDOA relative ranging measurement equation based on the TDOA relative ranging value and the second data.

[0095] Here, determining the TDOA relative ranging value based on the first data includes: determining third data based on the first data; the third data includes a measurement data difference related to UL-RTOA;

[0096] The TDOA relative ranging value is determined based on the third data and a first light speed representing the speed of light in a vacuum.

[0097] Here, taking TRP1 as the reference TRP as an example, the TDOA relative ranging value can be determined by the following formula (2):

[0098]

[0099] Where i = 2, 3, ..., n; represents the TDOA relative ranging value; c represents the first light speed, specifically, c = 299792458 m / s is the speed of light in vacuum; T represents the UL-RTOA measurement value of TRP1 at time k, which can also be called the measurement data related to UL-RTOA of TRP1 at time k, and its unit is second; i k Indicates TRP i The UL-RTOA measurement value at time k, also known as TRP i The measurement data related to UL-RTOA at time k, in seconds; Represents the measurement data difference related to UL-RTOA (corresponding to the third data).

[0100] Here, after obtaining the TDOA relative ranging value, LMF can determine the TDOA relative ranging measurement equation using the following formula (3):

[0101]

[0102] Among them, single beacon UE u The coordinates of can be expressed as b=(x u ,y u ,h u ) T (corresponding to UE location data); TRP i The coordinates can be expressed as α i =(x i ,y i ,h i ) T ,i=1,2,…,n(corresponding to the position data of TRP); a represents the second speed of light, specifically, a=c*10 -9 , is the speed of light in vacuum in m / ns; represents the base station RF receiving channel delay at time k (i.e., the base station RF receiving channel delay at the current epoch); represents the measurement noise of the TDOA relative ranging value (in meters), which is modeled as zero-mean white noise, and its variance-covariance matrix D r It can be expressed by the following formula (4):

[0103]

[0104] in, TRP i The UL-RTOA measurement variance converted to meters for (i=1,2,…,n) can be determined based on the nominal accuracy.

[0105] Here, LMF can construct the inter-channel delay of the base station RF receiving channel at the current epoch based on the TDOA relative ranging measurement equation determined by the above formula (3), which can be expressed by the following formula (5):

[0106]

[0107] Where i = 2, 3, ..., n, The unit is ns.

[0108] After obtaining the inter-channel delay of the base station radio frequency receiving channel at the current epoch, the first equation model (corresponding to the measurement equation model of the Kalman filter, specifically also referred to as the inter-channel delay measurement equation of the base station radio frequency receiving channel) can be constructed using the following formula (6):

[0109]

[0110] Where i = 2, 3, ..., n, It represents the delay measurement noise (specifically, it can also be called the delay measurement noise between base station RF receiving channels), and its unit is ns. In practical applications, since a is less than 1, The relative error Zoomed in times, then

[0111] Here, after constructing the first equation model, the first equation model (i.e., the base station radio frequency receiving channel delay measurement equation) can also be converted into a matrix form, which can be specifically expressed by the following formula (7):

[0112] Z k =H k X k +e k (7)

[0113] in, represents the measurement vector; H k represents the design matrix, where H k =I n-1 , I n-1 represents the identity matrix; Represents the measurement noise, which can also be called the noise of the delay measurement value between the constructed base station RF receiving channels. Its corresponding covariance matrix is ​​R k (corresponding to the first covariance matrix, ie, the measurement noise covariance matrix).

[0114] Here, for the above w k and e k , the following conditions need to be met, and the specific conditions can be expressed by formula (8):

[0115]

[0116] Among them, δ kjIt should be noted that the meanings of other parameters in this formula can be understood by referring to the meanings of the same parameters mentioned above, and will not be repeated here.

[0117] In practical applications, the delay state between base station RF receive channels can jump when a cell undergoes adjustments such as reset and restart, or when a fault occurs, as well as the presence of outliers in TDOA measurement data. By designing a robustness factor (corresponding to the second factor) and an adaptive factor (corresponding to the first factor), a dual-factor model is used to adjust the predicted state covariance matrix (corresponding to the second covariance matrix) and the measurement noise covariance matrix (corresponding to the first covariance matrix), respectively. This achieves sensitive conversion to state jumps and effective suppression of measurement outliers, making the delay determination process unaffected by state jumps and measurement outliers, thereby improving delay accuracy and reliability. It can be seen that the delay determination process is related to the adjustment of the first covariance matrix and the second covariance matrix. The first equation model is related to the first covariance matrix, and the second equation model is related to the second covariance matrix. The second covariance matrix is ​​the estimated value of the covariance matrix of the first vector. Therefore, the first vector and the first covariance matrix can be determined first, and then the delay can be determined.

[0118] Based on this, in one embodiment, the time delay is a time delay related to TDOA; and determining the time delay based on the first equation model and the second equation model includes:

[0119] Determining the first vector based on a fourth vector using the second equation model; the fourth vector comprising an initial value of the state vector or an estimated value of the state vector of the previous epoch before the current epoch;

[0120] Determining the first covariance matrix through the first equation model;

[0121] The time delay is determined based on the first vector and the first covariance matrix.

[0122] Here, for the first equation model, the first covariance matrix is ​​determined (which can be used with R k For example, the measurement noise can be determined by the matrix form of the first equation model, and then the covariance matrix R corresponding to the measurement noise can be determined. k , where R k =D S , is a positive definite matrix, D S represents the prior variance-covariance matrix of the delay measurement value between the base station RF receiving channels. Specifically, D can be determined by the following formula (9): S :

[0123]

[0124] Among them, D rexpress The variance-covariance matrix of a represents the second speed of light. Specifically, a=c*10 -9 , is the speed of light in a vacuum in m / ns.

[0125] Here, the fourth vector can be time-predicted by the state equation model of the Kalman filter (i.e., the second equation model) to obtain the first vector. Specifically, the first vector can be obtained by the following formula (10):

[0126]

[0127] Among them, Φ k,k-1 Represents the state transition matrix from time k-1 to time k (corresponding to the first matrix); Represents the fourth vector, which can be the initial value of the state vector (corresponding to the situation where the previous epoch of the current epoch is the first epoch, at this time k = 1, for ), the fourth vector can also be the estimated value of the state vector of the previous epoch of the current epoch (corresponding to the case where the previous epoch of the current epoch is not the first epoch, in which case k>1), in which case the fourth vector is a posterior estimate, which has the same meaning as the third vector; Represents the first vector, which is the time prediction value of the state vector at the current epoch (i.e., time k).

[0128] Here, when k=1, for The covariance matrix estimate of is P0, where and P0 are collectively referred to as the initial value of the filter. Specifically, Represents the initial value of the state vector, and P0 represents the estimated value of the covariance matrix of the initial value of the state vector. In practical applications, The determination of and P0 is the premise for the operation of Kalman filtering, and the conditions shown in the following formula (11) must be met:

[0129]

[0130] During the filtering process and P k will be unbiased when the epoch before the current epoch is the first epoch, The initial values ​​of and P0 can be expressed as follows:

[0131]

[0132] In practical applications, the delay determination is not only related to the adjustment of the first covariance matrix, but also related to the adjustment of the second covariance matrix.

[0133] Based on this, in one embodiment, determining the time delay based on the first vector and the first covariance matrix includes:

[0134] determining the second covariance matrix;

[0135] The time delay is determined based on the first vector, the first covariance matrix, and the second covariance matrix.

[0136] Here, the second covariance matrix represents the estimated value of the covariance matrix of the first vector, which can be expressed as To represent; the first vector represents the time prediction value of the state vector of the current epoch, which can be expressed as Before explaining the process of determining the time delay, the second covariance matrix The determination process is described below.

[0137] In actual application, it can be The covariance matrix estimate of (corresponding to the estimated value of the covariance matrix of the fourth vector, that is, the third covariance matrix) to predict (corresponding to the second covariance matrix).

[0138] Based on this, in one embodiment, determining the second covariance matrix includes:

[0139] Determining a third covariance matrix; the third covariance matrix represents an estimated value of the covariance matrix of the fourth vector;

[0140] Based on the third covariance matrix, the second covariance matrix is ​​determined.

[0141] Specifically, the second covariance matrix can be determined by the following formula (13):

[0142]

[0143] Among them, Φ k,k-1 Represents the state transition matrix from time k-1 to time k (corresponding to the first matrix); Represents the fourth vector The estimated value of the covariance matrix of (corresponding to the third covariance matrix); represents the temporal prediction of the state vector covariance matrix.

[0144] In one embodiment, determining the time delay based on the first vector, the first covariance matrix, and the second covariance matrix includes:

[0145] Based on the first covariance matrix and the second covariance matrix, the first vector is measured and updated to obtain a fifth vector; the fifth vector represents an estimated value of the state vector of the current epoch;

[0146] Based on the fifth vector, the time delay is determined.

[0147] Here, in actual application, the fourth covariance matrix used to measure and update the first vector can be determined by the second covariance matrix, and the fifth covariance matrix used to measure and update the first vector can be determined by the first covariance matrix, and then the first vector is corrected based on the fourth covariance matrix and the fifth covariance matrix to obtain the fifth vector.

[0148] Based on this, in one embodiment, the step of measuring and updating the first vector based on the first covariance matrix and the second covariance matrix to obtain a fifth vector includes:

[0149] determining a fourth covariance matrix based on the second covariance matrix, and determining a fifth covariance matrix based on the first covariance matrix;

[0150] Based on the fourth covariance matrix and the fifth covariance matrix, the first vector is measured and updated to obtain the fifth vector.

[0151] Here, the fourth covariance matrix can be expressed as Indicates that the fifth covariance matrix can be expressed as Indicates that the fourth covariance matrix is Fifth covariance matrix The determination process is described below.

[0152] In practical applications, the two-factor model can be used to predict the state covariance matrix (corresponding to the second covariance matrix, i.e. ) and the measurement noise covariance matrix (corresponding to the first covariance matrix, i.e. R k ) is adjusted to obtain the corresponding fourth covariance matrix Fifth covariance matrix

[0153] Based on this, in one embodiment, determining a fourth covariance matrix based on the second covariance matrix, and determining a fifth covariance matrix based on the first covariance matrix, includes:

[0154] Determine a first factor and a second factor; the first factor represents an adaptive factor, and the second factor represents a robustness factor;

[0155] The second covariance matrix is ​​adjusted based on the first factor to obtain the fourth covariance matrix, and the first covariance matrix is ​​adjusted based on the second factor to obtain the fifth covariance matrix.

[0156] In actual application, before determining the first factor and the second factor, the method further includes: determining whether the first test statistic meets a first condition.

[0157] Specifically, determining whether the first test statistic satisfies a first condition includes:

[0158] When it is determined that the state vector has a jump or the delay measurement data has a measurement outlier, determining that the first test statistic satisfies a first condition;

[0159] When it is determined that the state vector does not have a jump and the delay measurement data does not have a measurement outlier, it is determined that the first test statistic does not meet the first condition.

[0160] Here, in actual application, the embodiment of the present application designs two factors: an adaptive factor and a robustness factor, and adjusts the predicted state covariance matrix (corresponding to the second covariance matrix, i.e. ), and adjust the measurement noise covariance matrix (corresponding to the first covariance matrix, i.e. R) by the robustness factor k ), realizing sensitive conversion of state jumps and effective suppression of measurement wild values, thereby improving the accuracy and reliability of delay.

[0161] Based on this, in one embodiment, determining the first factor and the second factor includes: determining the first factor and the second factor when it is determined that the first test statistic satisfies a first condition.

[0162] Here, in practical applications, state jumps and measurement outliers can be identified by constructing test statistics.

[0163] Based on this, in one embodiment, determining whether the state vector has a jump or whether the delay measurement data has a measurement wild value includes:

[0164] Determining a first test statistic; wherein the first test statistic represents a normalized innovation vector;

[0165] Based on the first test statistic, it is determined that there is a jump in the state vector or there is a measurement outlier in the delay measurement data.

[0166] Here, determining the first test statistic includes: determining a sixth vector and an eighth covariance matrix; the sixth vector represents the innovation vector, and the eighth covariance matrix represents the covariance matrix of the innovation vector;

[0167] Determine the first test statistic based on the ratio of the vector elements of the sixth vector to the diagonal elements of the eighth covariance matrix.

[0168] Here, the innovation vector can be represented by r k and the covariance matrix of the innovation vector can be represented by Assume that the vector elements of the innovation vector r k are r k,i , where i = 1, 2,..., n - 1; the diagonal elements of the covariance matrix of the innovation vector are Then the first test statistic can be determined by the following formula (14):

[0169]

[0170] where v k,i represents the first test statistic, i = 1, 2,..., n - 1; i represents the number of elements, and k represents the corresponding current epoch.

[0171] Here, in one embodiment, the determining that the state vector has a jump or the time-delay measurement data has a measurement outlier based on the first test statistic includes:

[0172] Compare the first test statistic with a first prior threshold to obtain a comparison result;

[0173] When the comparison result indicates that the first test statistic is greater than or equal to the first prior threshold, determine that the state vector has a jump or the time-delay measurement data has a measurement outlier.

[0174] Here, the first prior threshold can be represented by C1, where C1 is the measurement noise standard deviation threshold determined by the 3σ criterion, which is a prior threshold and can be determined according to experience and is not limited here. Specifically, when |v k,i | < C1, it is determined that there is no state jump or measurement outlier for the i-th element at time k; otherwise, it is determined that there is a state jump or measurement outlier for the i-th element at time k. [[ID=​​​​By selecting an appropriate test threshold, single-epoch-to-single-epoch recovery wild values ​​can be effectively identified, but single-epoch-to-multi-epoch recovery wild values, multi-epoch-to-single-epoch recovery wild values, multi-epoch-to-multi-epoch recovery wild values ​​and state jumps cannot be effectively distinguished. Therefore, this application proposes the use of two sets of differential measurement values ​​between epochs to further identify state jumps or measurement wild values.

[0176] Based on this, in one embodiment, determining the first factor includes:

[0177] When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the second condition, determining that the first factor is a first value; the first test statistic represents a normalized innovation vector, the second test statistic represents a statistic consisting of the first set of inter-epoch difference measurement values, and the third test statistic represents a statistic consisting of the second set of inter-epoch difference measurement values;

[0178] When the first test statistic satisfies a first condition and the second test statistic and the third test statistic satisfy a second condition, the first factor is determined to be a second value.

[0179] Here, specifically, the difference measurements between these two sets of epochs can be and Where m is greater than 1, and its typical value is 3. (corresponding to the second test statistic satisfying the second condition) and (corresponding to the third test statistic meeting the second condition), it is considered that the i-th element at time k has a state jump, and at this time, the amplification Otherwise, it is considered that the i-th element at time k has a measurement outlier, and R is enlarged. k The corresponding elements in .

[0180] Here, the first factor (i.e., the adaptive factor) can be determined by the following formula (15):

[0181]

[0182] Among them, the first value can be 10 8 (corresponding to the situation where the state vector jumps), the second value can be 1 (corresponding to the situation where the state vector does not jump). The embodiment of the present application does not specifically limit the values ​​of the first value and the second value. k,i |>=C1 means that the first test statistic satisfies the first condition, and It indicates that the second test statistic and the third test statistic meet the second condition. C2 represents the second prior threshold value, which can be determined based on experience and is not limited here.

[0183] In one embodiment, determining the second factor includes:

[0184] When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the third condition, determining the second factor to be the first value; the first test statistic represents the normalized innovation vector, the second test statistic represents the statistic composed of the first set of inter-epoch difference measurement values, and the third test statistic represents the statistic composed of the second set of inter-epoch difference measurement values;

[0185] When the first test statistic satisfies a first condition and the second test statistic and the third test statistic satisfy a third condition, the second factor is determined to be a second value.

[0186] Here, the second factor (i.e., robustness factor) can be determined by the following formula (16):

[0187]

[0188] Among them, the first value can be 10 8 (corresponding to the case where the delay measurement data has a measurement outlier), the second value can be 1 (corresponding to the case where the delay measurement data does not have a measurement outlier). The embodiment of the present application does not specifically limit the values ​​of the first value and the second value. k,i |>=C1 means that the first test statistic satisfies the first condition, or It indicates that the second test statistic and the third test statistic meet the third condition. C2 represents the second prior threshold value, which can be determined based on experience and is not limited here.

[0189] Here, in actual application, after determining the first factor and the second factor, the two-factor model can be used to and R k Make adjustments, Adjust to R k Adjust to Specifically, it can be adjusted by the following formulas (17) and (18):

[0190]

[0191] in, and They are and The (i-1,j-1) element of and R k and The (i-1,j-1) element of .

[0192] This application uses a two-factor model to calculate the second covariance matrix and the first covariance matrix R k Adjustments can achieve sensitive conversion of delay state jumps between base station RF receiving channels and effective suppression of wild values ​​of delay measurement between different types of base station RF receiving channels, solving the problem of insufficient accuracy and reliability of delay estimation between base station RF receiving channels when the delay state between base station RF receiving channels jumps when the cell undergoes adjustments such as reset and restart or a failure occurs, and when TDOA measurement data has wild values; that is, the solution of the present application can effectively identify and process state jumps and measurement wild values, and the estimation process is not affected by state jumps and measurement wild values, which can improve the accuracy and reliability of delay estimation between base station RF receiving channels.

[0193] In practical application, the embodiment of the present application can be implemented by setting an adjusted or updated Kalman gain matrix K k , with the help of K k Implement measurement update of the first vector.

[0194] Based on this, in one embodiment, the measuring and updating of the first vector based on the fourth covariance matrix and the fifth covariance matrix to obtain the fifth vector includes:

[0195] Determining a second matrix based on the fourth covariance matrix and the fifth covariance matrix; wherein the second matrix represents an updated Kalman gain matrix;

[0196] Based on the second matrix, the first vector is measured and updated to obtain the fifth vector.

[0197] Next, for the second matrix, the updated Kalman gain matrix K k The determination process is described below.

[0198] Specifically, in one embodiment, determining the second matrix based on the fourth covariance matrix and the fifth covariance matrix includes:

[0199] Determining a sixth covariance matrix based on the fourth covariance matrix; the sixth covariance matrix represents a cross-covariance matrix of the updated predicted state and the predicted measurement;

[0200] Determine a seventh covariance matrix based on the sixth covariance matrix and the fifth covariance matrix; the seventh covariance matrix represents the covariance matrix of the updated innovation vector;

[0201] The second matrix is ​​determined based on the sixth covariance matrix and the seventh covariance matrix.

[0202] Here, determining the sixth covariance matrix based on the fourth covariance matrix may include determining the sixth covariance matrix based on the product of the fourth covariance matrix and the third matrix. Specifically, the sixth covariance matrix may be determined by the following formula (19):

[0203]

[0204] in, represents the sixth covariance matrix, i.e., the cross-covariance matrix of the updated predicted state and predicted measurement; represents the fourth covariance matrix; H k represents the third matrix (i.e., the design matrix), where H k =I n-1 , I n-1 Represents the identity matrix.

[0205] Here, determining the seventh covariance matrix based on the sixth covariance matrix and the fifth covariance matrix may include: determining a fourth matrix based on the product of the third matrix and the sixth covariance matrix; and determining the seventh covariance matrix based on the sum of the fourth matrix and the fifth covariance matrix. Specifically, the seventh covariance matrix may be determined by the following formula (20):

[0206]

[0207] in, represents the seventh covariance matrix, that is, the covariance matrix of the updated innovation vector; H k represents the third matrix (i.e., the design matrix), where H k =I n-1 , I n-1 represents the identity matrix; represents the sixth covariance matrix, i.e., the cross-covariance matrix of the updated predicted state and predicted measurement; represents the fourth matrix; represents the fifth covariance matrix.

[0208] Here, after obtaining the sixth covariance matrix and the seventh covariance matrix, the second matrix can be determined by the following formula (21):

[0209]

[0210] Among them, K k represents the second matrix, i.e. the updated Kalman gain matrix; represents the sixth covariance matrix, i.e., the cross-covariance matrix of the updated predicted state and predicted measurement; Represents the seventh covariance matrix, that is, the covariance matrix of the updated innovation vector.

[0211] In actual application, after determining the second matrix, the first vector can be measured and updated based on the new information vector to obtain the fifth vector.

[0212] Based on this, in one embodiment, the measuring and updating of the first vector based on the second matrix to obtain the fifth vector includes:

[0213] Determine a seventh vector based on the product of the second matrix and a sixth vector; the sixth vector represents an innovation vector;

[0214] The fifth vector is determined based on a difference between the first vector and the seventh vector.

[0215] Specifically, the fifth vector can be determined by the following formula (22):

[0216]

[0217] in, represents the fifth vector, i.e. the estimated value of the state vector of the current epoch; K k represents the second matrix, i.e. the updated Kalman gain matrix; r k represents the sixth vector, namely the innovation vector; K k r k represents the seventh vector; Represents the first vector, which is the time prediction value of the state vector at the current epoch (i.e., time k).

[0218] In practical applications, when the standard Kalman filter is applied to the estimation of the time delay between the base station RF receiving channels, a fixed value is used for the process noise variance of the random walk process of the time delay state between the base station RF receiving channels, and the time-varying nature of the process noise variance is not taken into account, resulting in limited estimation accuracy. To solve this technical problem, the innovation of this application is to apply the improved Sage-Husa adaptive Kalman filter to the time-varying estimation of the process noise variance of the random walk process of the time delay state between the base station RF receiving channels, thereby improving the estimation accuracy of the time delay between the base station RF receiving channels, and compared with the standard Sage-Husa adaptive filter, the improved Sage-Husa adaptive Kalman filter has higher accuracy for time-varying estimation. The following describes the process of time-varying estimation of the process noise variance of the random walk process of the time delay state between the base station RF receiving channels.

[0219] Based on this, in one embodiment, after determining the fourth covariance matrix based on the second covariance matrix and determining the fifth covariance matrix based on the first covariance matrix, the method further includes:

[0220] Based on the fourth covariance matrix and the fifth covariance matrix, measuring and updating the fourth covariance matrix to obtain a ninth covariance matrix;

[0221] Based on the ninth covariance matrix, the tenth covariance matrix is ​​time-varyingly estimated to obtain an eleventh covariance matrix; the tenth covariance matrix represents the covariance matrix of the second vector, the second vector represents the process noise vector from the previous epoch of the current epoch to the current epoch, and the eleventh covariance matrix represents the estimated value of the process noise covariance matrix.

[0222] Here, the step of measuring and updating the fourth covariance matrix based on the fourth covariance matrix and the fifth covariance matrix to obtain a ninth covariance matrix may include:

[0223] Determining a second matrix based on the fourth covariance matrix and the fifth covariance matrix; wherein the second matrix represents an updated Kalman gain matrix;

[0224] Based on the second matrix and the seventh covariance matrix, the fourth covariance matrix is ​​measured and updated to obtain a ninth covariance matrix; the seventh covariance matrix represents the covariance matrix of the updated innovation vector.

[0225] It should be noted that the process of determining the second matrix based on the fourth covariance matrix and the fifth covariance matrix can be understood by referring to the above, and will not be repeated here.

[0226] Here, specifically, the ninth covariance matrix can be determined by the following formula (23):

[0227]

[0228] Among them, P k represents the ninth covariance matrix; represents the fourth covariance matrix; K k represents the second matrix, i.e. the updated Kalman gain matrix; Represents the seventh covariance matrix, that is, the covariance matrix of the updated innovation vector.

[0229] Here, in one embodiment, performing time-varying estimation on the tenth covariance matrix based on the ninth covariance matrix to obtain an eleventh covariance matrix includes:

[0230] Determine a third factor and a sixth vector; the third factor is obtained through the fourth factor, the fourth factor represents the forgetting factor, and the sixth vector represents the innovation vector;

[0231] Using Sage-Husa adaptive Kalman filtering, the tenth covariance matrix is ​​time-varyingly estimated based on the fourth covariance matrix, the fifth covariance matrix, the second matrix, the third factor and the sixth vector to obtain the eleventh covariance matrix.

[0232] Specifically, the third factor can be determined by the following formula (24):

[0233] α k =(1-b) / (1-b k+1 )(twenty four)

[0234] Among them, α k represents the third factor; b represents the fourth factor, namely the forgetting factor, and its value range is 0.95 to 0.99.

[0235] Here, specifically, the eleventh covariance matrix can be determined by the following formula (25):

[0236]

[0237] in, represents the eleventh covariance matrix; α k represents the third factor; represents the tenth covariance matrix; represents the fourth covariance matrix; represents the fifth covariance matrix; K k represents the second matrix, i.e. the updated Kalman gain matrix; r k Represents the sixth vector, namely the innovation vector.

[0238] It should be noted that the meanings of other parameters in the above formula (25) can be understood by referring to the meanings of the same parameters above, and will not be repeated here. Non-negative definite, take the absolute value of the diagonal elements of the second matrix on the right side of the above formula (25), and take the off-diagonal elements to be zero.

[0239] In practical applications, in order to ensure the ninth covariance matrix P k In one embodiment, after measuring and updating the fourth covariance matrix based on the fourth covariance matrix and the fifth covariance matrix to obtain a ninth covariance matrix, the method further includes:

[0240] Symmetry processing is performed on the ninth covariance matrix to obtain a twelfth covariance matrix.

[0241] Specifically, the twelfth covariance matrix can be determined by the following formula (26):

[0242]

[0243] Among them, P k ' represents the twelfth covariance matrix; P k represents the ninth covariance matrix.

[0244] The following describes the process of determining the delay from two aspects: a single UE and multiple UEs.

[0245] In actual application, in one embodiment, when there is a single UE, determining the delay based on the fifth vector includes:

[0246] Determine a single UE delay estimate based on a difference between any two vector elements in the fifth vector, a negative value of a vector element, and the vector element itself;

[0247] The delay estimation of the single UE is determined as the delay; the delay includes the delay estimation between the base station radio frequency receiving channels of all line-of-sight TRPs corresponding to the single UE.

[0248] Here, for a single UE, such as a single beacon UEu, when the base station RF receiving channel delay estimation of the beacon UEu is calculated through the previous steps (corresponding to the fifth vector mentioned above), specifically, it can be done by vector Subtracting any two vector elements in the , we can get the estimated delay between the base station RF receiving channels of all line-of-sight TRPs corresponding to the beacon UE u. It can be expressed by the following formula (27):

[0249]

[0250] In actual application, when there are multiple UEs, the delay determination is related to the delay estimation of each UE and the delay estimation of the non-shared line-of-sight TRP of the adjacent UEs.

[0251] Based on this, in one embodiment, when there are multiple UEs, determining the delay based on the fifth vector includes:

[0252] Determining a first delay based on the fifth vector, wherein the first delay represents a delay estimate of each UE;

[0253] The time delay is determined based on the first time delay.

[0254] Specifically, in one embodiment, determining the first delay based on the fifth vector includes:

[0255] determining the first time delay based on a difference between any two vector elements in the fifth vector, a negative value of a vector element, and the vector element itself;

[0256] Among them, the first delay includes the base station RF receiving channel delay estimation of all line-of-sight TRPs corresponding to each UE.

[0257] It should be noted that the process of determining the first delay can be understood by referring to the process of determining the delay estimation of a single beacon UEu above, which will not be repeated here.

[0258] Specifically, in one embodiment, determining the delay based on the first delay includes:

[0259] Determining a second delay based on the first delay; wherein the second delay represents a delay estimate of a non-shared line-of-sight TRP of an adjacent UE;

[0260] The delay is determined based on the first delay and the second delay.

[0261] Here, determining the second delay based on the first delay may include determining the second delay based on a difference in target vector elements in the first delay. Determining the delay based on the first delay and the second delay may include superimposing the first delay and the second delay to obtain the delay.

[0262] Here, for multiple UEs, such as adjacent beacon UEu and beacon UE v, when the base station RF receiving channel delay estimates of all corresponding line-of-sight TRPs of the adjacent beacon UEu and beacon UE v are calculated respectively through the previous steps, since the adjacent beacon UEu and beacon UE v need to satisfy the intersection of their corresponding line-of-sight TRP sets, assuming that TRP1 is the common line-of-sight TRP of beacon UEu and beacon UE v, the TRP of beacon UEu is m and TRP of beacon UE v n For non-shared line-of-sight TRP, the following formula (28) can be used to determine (Second delay):

[0263]

[0264] in, Represents any two target vector elements in the first delay.

[0265] It can be seen that the embodiment of the present application solves the problem of incomplete estimation of the delay between base station radio frequency receiving channels estimated based on multiple beacon UEs by calculating the delay between base station radio frequency receiving channels of all TRPs in indoor venues, and can improve the availability of the delay estimation between base station radio frequency receiving channels.

[0266] Accordingly, the embodiment of the present application further provides another method for determining time delay, which is applied to a base station. Figure 2This is a flowchart of the delay determination method of the embodiment of the present application. Figure 2 ,like Figure 2 As shown, the method includes:

[0267] Step 201: The base station sends first data to the LMF so that the LMF uses the first data and second data to determine the delay; the first data includes measurement data related to UL-RTOA, and the second data includes TRP location data and UE location data.

[0268] In actual application, the embodiment of the present application puts forward relevant requirements for UE, namely target selection requirements and target site selection principles.

[0269] Based on this, in one embodiment, the UE is one or more, and the one or more UEs meet the target selection requirements and target site selection principles; wherein any TRP forms a line-of-sight relationship with the one or more UEs; when there are multiple UEs, the line-of-sight TRP sets of adjacent UEs have an intersection.

[0270] Here, the target selection requirement may refer to the use of commercial 5G low-power terminals with R16 or higher versions that support SRS signal transmission. For example, SRS can be SRS-Pos. Target site selection principles may include the following: 1) Line-of-sight with as many TRPs as possible; 2) Good signal-to-noise ratio; 3) Not susceptible to interference from environmental changes; 4) Easy to install, deploy, and maintain; 5) No impact on indoor business operations and production operations (this principle applies to UL-TDOA indoor positioning).

[0271] Here, the second data includes the location data of the TRP and the location data of the UE. Specifically, the second data may include the location data of multiple TRPs and the location data of one or more UEs.

[0272] In actual application, the base station can send the first data directly to the LMF, or send the first data to the LMF in a response manner.

[0273] Based on this, in one embodiment, sending the first data to the LMF includes:

[0274] receiving a first message sent by the LMF; the first message including configuration information of a reference signal for positioning and information required to perform UL-RTOA measurement;

[0275] Based on the first message, a second message is sent to the LMF; the second message includes the first data.

[0276] Here, the UE is at least capable of sending a reference signal for positioning, for example, the UE is at least capable of sending a UL-SRS for positioning, wherein the UE may be a beacon UE, and the number of the beacon UEs may be one or more.

[0277] In one embodiment, the method further includes: sending a third message to the LMF; the third message includes uplink information, and the uplink information carries configuration information of a reference signal used for positioning of the UE.

[0278] In one embodiment, the method further includes: receiving a fifth message sent by the LMF; the fifth message represents a positioning information request message;

[0279] Based on the fifth message, resources of a reference signal for positioning are determined, and the UE is configured using the resources.

[0280] Here, after receiving the fifth message, the base station can determine the resources of the reference signal (such as UL-SRS) that can be used for positioning, and use the UL-SRS resources to configure the UE, where the UE can be a beacon UE and the UL-SRS resources can be a UL-SRS resource set.

[0281] In one embodiment, the reference signal used for positioning includes a semi-persistent or aperiodic reference signal, and the method further includes:

[0282] receiving a sixth message sent by the LMF; the sixth message being used to request activation of transmission of the reference signal for positioning;

[0283] Based on the sixth message, the transmission of the reference signal used for positioning is activated.

[0284] Here, the sixth message may be a positioning activation request message, such as an NRPPa positioning activation request (POSITIONING ACTIVATION REQUEST) message, to request the base station to activate transmission of a reference signal used for positioning.

[0285] In actual application, after the base station activates the transmission of the reference signal used for positioning, it is also necessary to send a positioning activation response to the LMF.

[0286] Based on this, in one embodiment, the method further includes: sending a seventh message to the LMF; the seventh message represents a positioning activation response message.

[0287] In actual application, after the LMF uses the first data and the second data to determine the delay, positioning deactivation can be achieved through the base station.

[0288] Based on this, in one embodiment, the method further includes: receiving an eighth message sent by the LMF; the eighth message is used to instruct the base station to deactivate positioning;

[0289] Positioning deactivation is performed based on the eighth message.

[0290] The embodiment of the present application also provides another method for determining the time delay, which is an interaction method between the base station and the LMF. Figure 3 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 3 ,like Figure 3 As shown, the method includes:

[0291] Step 301: The base station sends first data to the LMF; the first data includes measurement data related to UL-RTOA;

[0292] Step 302: The LMF receives first data;

[0293] Step 303: LMF uses the first data and the second data to determine the delay; the second data includes the location data of the TRP and the location data of the UE.

[0294] In an embodiment of the present application, the UE is one or more, and the one or more UEs meet the target selection requirements and target site selection principles; wherein any TRP forms a line-of-sight relationship with the one or more UEs; when there are multiple UEs, the line-of-sight TRP sets of adjacent UEs have an intersection.

[0295] It should be noted that the specific processing process of the base station and LMF to determine the delay has been described in detail above and will not be repeated here.

[0296] By adopting the technical solution of the embodiment of the present application, by using the measurement data related to UL-RTOA, as well as the location data of TRP and the location data of UE as input data for delay determination, the delay determination is not affected by the base station hardware reasons, thereby reducing the delay deviation, improving the accuracy and reliability of the delay, and further improving the positioning accuracy and availability of the UL-TDOA positioning method.

[0297] The present application is described below in conjunction with application examples.

[0298] The time delay determination method of the present application can be applied to 5G UL-TDOA indoor positioning. Therefore, the time delay determination method of the present application can also be called a base station RF receiving channel delay estimation method for 5G UL-TDOA indoor positioning.

[0299] The following first describes the solutions of related technologies:

[0300] Related technical solution 1 proposes a method and device for eliminating clock deviation between base stations, which can improve the UE positioning accuracy of the UL-TDOA / downlink (DL)-TDOA positioning technology solution.

[0301] Related technical solution two proposes a 5G NR positioning calibration method and system, which includes the following steps: measuring and calibrating the location information of a 5G terminal at a fixed position within the 5G positioning service area, and performing positioning calibration on the current position of the terminal to be positioned through a screened positioning algorithm based on the calibrated location information of the 5G terminal at the fixed position and the wireless measurement data uploaded by the 5G terminal at the fixed position; by first measuring and calibrating the position of the 5G terminal at the fixed position, calibrated location information and measurement data are provided, and used as prior information for subsequent positioning calibration to calibrate and improve the positioning accuracy of the 5G terminal to be positioned, thereby improving the calibration technology of 5G NR positioning accuracy and the indoor and outdoor positioning accuracy based on 5G technology.

[0302] Related technical solution three proposes a 5G LMF calibration implementation method, which includes the following steps: the LMF side receives the first UL-RTOA of the first SRS arriving at the first TRP and the second UL-RTOA of the second SRS arriving at the second TRP, and establishes a UL-reference signal time difference (RSTD), the LMF receives the identifier of the UE from the base station and determines that the UE is a wireless packet communication (PRU, Packet Radio Communications) based on the identifier, the LMF receives the position coordinates of the PRU from the base station, and determines the uplink real-time dynamic code phase differential (RTD, Real Time Differential) based on the UL-RSTD received from the PRU and the coordinates of the PRU, the uplink RTD is associated with the baseband symbol boundary difference between the first TRP and the second TRP at the time of reception and the difference between the receiving group delay at the first TRP and the receiving group delay at the second TRP, so as to achieve enhanced accuracy of UL-TDOA positioning.

[0303] The above-mentioned related technical solutions have the following technical problems:

[0304] (1) Currently, there is no solution for calculating the inter-channel delay of base station RF receiving signals based on the UL-TDOA process of the 3rd Generation Partnership Project (3GPP) R16 standard, and it cannot be directly applied to 5G UL-TDOA positioning.

[0305] (2) The existing solution does not provide a detailed explanation of clock deviation, and the calculation process for the delay between base station RF receiving channels is incomplete;

[0306] (3) Currently, there is no solution for online real-time estimation of the delay between base station RF receiving channels. At the same time, there is no proposal or solution to the problem of delay state jump between base station RF receiving channels and the phenomenon of wild value measurement of delay between base station RF receiving channels. Therefore, it cannot adapt to the complex situation of actual online estimation of delay between base station RF receiving channels.

[0307] Due to the significant deviation of 5G UL-TDOA relative ranging caused by the delay between base station RF receiving channels, the positioning accuracy of the 5G UL-TDOA indoor positioning method is limited, the positioning reliability is poor, and the positioning availability is insufficient. This technical problem has a great impact on the practical application of TDOA hyperbolic geometry solution in the 5G UL-TDOA indoor positioning method in engineering projects. To this end, this application proposes an online estimation scheme and algorithm for the delay between base station RF receiving channels based on beacon UE. The algorithm has good real-time performance and high estimation accuracy, and the estimated residual error can be less than 1ns. However, in the real-time estimation process, there are wild value phenomena in the measurement data of the delay between base station RF receiving channels, and there is a jump phenomenon in the delay state between base station RF receiving channels. If the above two phenomena are not taken into account, the accuracy and stability of the online estimation will be affected. To this end, this application takes the above two phenomena into consideration and innovatively proposes an online estimation method based on an improved adaptive robust Kalman filter, which can accurately identify and process the above two phenomena. By using the calculation results of this application, the TDOA relative ranging value of the positioning UE can be calibrated in real time, thereby significantly improving the accuracy, reliability and availability of the TDOA hyperbolic geometry solution of 5G UL-TDOA indoor positioning.

[0308] This application proposes an online estimation method for the time delay between base station RF receiving channels for 5G UL-TDOA indoor positioning solution. Through an innovative beacon UE complete implementation solution based on the 5G UL-TDOA positioning signaling process, it solves the problem that the existing base station RF receiving channel time delay calculation solution based on reference UE cannot be directly applied to 5G UL-TDOA positioning and the description is incomplete. Specifically, it comprehensively explains (1) beacon UE selection and indoor site selection; (2) beacon UE installation, deployment and maintenance; (3) LMF side obtains input data for base station RF receiving channel time delay estimation (corresponding to the first data and second data mentioned above); (4) online time delay estimation method; (5) base station RF receiving channel time delay estimation transmission, etc.

[0309] This application applies the Kalman filter algorithm model to the state estimation of the time delay between base station RF receiving channels. According to the normal state characteristics of the time delay between base station RF receiving channels, a state equation model based on random walk is constructed (corresponding to the aforementioned second equation model), and according to the relationship between the conventional TDOA relative measurement value and the time delay between base station RF receiving channels, a measurement equation model is constructed (corresponding to the aforementioned first equation model). This solves the problem of constructing the state equation model and measurement equation model for the time delay estimation between base station RF receiving channels based on Kalman filtering.

[0310] Aiming at the time-varying process noise variance of the random walk process of the delay state between the base station RF receiving channels, this application uses the improved Sage-Husa adaptive filtering to estimate the process noise covariance matrix in real time (corresponding to the aforementioned eleventh covariance matrix); for the phenomenon that the delay state between base station RF receiving channels will jump when the cell is reset, restarted or fails, and the phenomenon that there are wild values ​​in the TDOA measurement data, the Kalman filter innovation statistics are designed to judge the delay state jump phenomenon between base station RF receiving channels and the wild value phenomenon of the delay measurement between base station RF receiving channels, and the discriminant equation is constructed by designing two sets of inter-epoch difference statistics to effectively identify single-epoch-single-epoch recovery wild values, but cannot effectively distinguish single-epoch-multi-epoch recovery wild values, multi-epoch-single-epoch recovery wild values, multi-epoch-multi-epoch recovery wild values ​​and state jumps, and the anti-error factor (corresponding to the aforementioned second factor) and the adaptive factor (corresponding to the aforementioned first factor) are designed, and the dual-factor model is used to adjust them respectively. (corresponding to the second covariance matrix mentioned above) and R k (corresponding to the first covariance matrix mentioned above) is (corresponding to the fourth covariance matrix mentioned above) and (corresponding to the aforementioned fifth covariance matrix), realizing sensitive conversion of state jumps and effective suppression of measurement wild values, the complete process can be called a real-time online estimation method for the time delay between base station RF receiving channels based on the improved adaptive robust Kalman filter. For the case where there are multiple beacon UEs in the venue, this application can obtain the time delay estimation between all base station RF receiving channels in the venue through the estimation transmission of adjacent beacon UEs, under the condition that the line-of-sight TRP sets of adjacent beacon UEs intersect.

[0311] The following takes the UE as a beacon UE as an example to explain the technical solution of the present application in detail. The technical solution of the present application includes the following steps:

[0312] Step 1: Requirements for beacon UE

[0313] (1) Beacon UE selection requirements

[0314] Commercial 5G low-power terminals with version R16 or above that support SRS signal transmission.

[0315] (2) Principles for indoor site selection of beacon UEs

[0316] The indoor site selection of beacon UEs for engineering deployment should meet the following principles: 1) establish a line-of-sight relationship with as many TRPs as possible; 2) have a good reception signal-to-noise ratio; 3) be less susceptible to interference caused by changes in the surrounding environment; 4) be easy to install, deploy, and maintain; and 5) not affect indoor business operations and production operations.

[0317] (3) Planning requirements for beacon UEs

[0318] According to the distribution of indoor TRP, in accordance with the above-mentioned beacon UE selection requirements and site selection principles, one or more beacon UEs are planned to cover all TRPs (wherein any TRP forms a line-of-sight relationship with one or more beacon UEs). When planning multiple beacon UEs, it is necessary to ensure that the line-of-sight TRP sets of adjacent beacon UEs have an intersection.

[0319] (4) Installation, deployment, and maintenance requirements for beacon UEs

[0320] For one or more planned beacon UEs, they and supporting facilities are installed on a stable platform, and then the beacon UE is locally deployed to ensure that the beacon UE remains connected to its service cell. At the same time, fault monitoring, power supply and other maintenance operations are continuously performed on the beacon UE online, including: background detection of whether the beacon UE has normally accessed 5G, detection of whether the beacon UE is residing in the designated cell, detection of whether the beacon UE's 5G uplink positioning process is normal, detection of whether there is any power supply abnormality for the beacon UE, etc.

[0321] Step 2: Base station RF receiving channel delay estimation process for 5G UL-TDOA indoor positioning

[0322] The following takes the UE as a beacon UE and the base station as a serving gNB as an example to illustrate the delay determination method of the embodiment of the present application. The number of beacon UEs can be one or more, for example, s Specifically, it may include beacons UE1, UE2, ..., UE n , n is a positive integer greater than or equal to 1; the number of TRPs can be multiple, that is, TRPs.

[0323] Figure 4 This is a flowchart of the delay determination method of the embodiment of the present application. Figure 4 ,like Figure 4 As shown, the method includes the following steps:

[0324] Step 0: LMF stores TRPs and beacon UEs location data (corresponding to the aforementioned second data);

[0325] Here, the TRP and beacon UE position data are the input data of LMF for estimating the delay between base station RF receiving channels. The data acquisition process includes two parts: coordinate system definition and coordinate measurement.

[0326] (1) Coordinate system definition

[0327] First define the coordinate system indoors, select the coordinate system origin O, two mutually perpendicular horizontal directions x and y, and the vertical direction h, and customize the local space rectangular coordinate system O-xyh.

[0328] (2) Coordinate measurement

[0329] High-precision coordinate measurement methods, such as total station traverse measurement, are used indoors to calibrate the positions of all TRPs and installed beacon UEs, and the position information is stored on the LMF side.

[0330] Step 1: The LMF can use the LPP capability transfer procedure to request the positioning capabilities of the beaconing UEs;

[0331] Step 2: The LMF sends an NRPPa POSITIONING INFORMATION REQUEST message (corresponding to the fifth message mentioned above) to the serving gNB to request the UL-SRS configuration information of the beaconing UEs (corresponding to the configuration information of the reference signal used for positioning of the UE mentioned above);

[0332] Step 3: The serving gNB determines the UL-SRS resources available for positioning and configures the beaconing UEs with the UL-SRS resource set in step 3a.

[0333] Step 4: The serving gNB provides uplink information to the LMF in the NRPPa POSITIONING INFORMATION RESPONSE message (corresponding to the third message mentioned above);

[0334] Step 5: For semi-persistent or aperiodic UL-SRS, the LMF can request activation of the UL-SRS transmission of the beacon UEs by sending an NRPPa POSITIONING ACTIVATION REQUEST message (corresponding to the sixth message mentioned above) to the serving gNB of the beacon UEs. The serving gNB then activates the UL-SRS transmission and sends an NRPPa POSITIONING ACTIVATION RESPONSE message (corresponding to the seventh message mentioned above). The beacon UEs start UL-SRS transmission according to the time domain behavior of the UL-SRS resource configuration.

[0335] Step 6: The LMF provides the UL-SRS configuration (corresponding to the configuration information of the reference signal used for positioning) to the serving gNB in ​​the NRPPa MEASUREMENT REQUEST message (corresponding to the first message mentioned above). This message also includes all information required to enable the serving gNB to perform UL-RTOA measurements.

[0336] Step 7: The serving gNB measures the UL-SRS transmission from the beaconing UEs and obtains the UL-RTOA measurement value (corresponding to the first data mentioned above).

[0337] Step 8: The serving gNB reports the UL-RTOA measurement value to the LMF in the NRPPa MEASUREMENT RESPONSE message (corresponding to the second message mentioned above);

[0338] Step 9: The LMF determines the inter-channel delay of the base station RF reception (corresponding to the aforementioned delay) through the TRPs and beacon UEs location data, as well as the UL-RTOA measurement value, and stores it;

[0339] (1) An online estimation method for the time delay between the necessary linearly independent base station radio frequency receiving channels corresponding to each beacon UE, i.e., an online estimation method for the time delay between the base station radio frequency receiving channels based on an improved adaptive robust Kalman filter.

[0340] 1) Construction of the Kalman filter state equation model (corresponding to the aforementioned second equation model) and the measurement equation model (corresponding to the aforementioned first equation model) for estimating the delay between base station RF receiving channels

[0341] A. Equation of State

[0342] Single beacon UE u For example, let UE u With TRP1, TRP2, ..., TRP nThe line-of-sight relationship is formed, where n is the number of line-of-sight TRPs. The state vector X is composed of the necessary linearly independent base station RF receiving channel delay parameters to be estimated, which is an n-1 dimensional vector. The state vector X can be constructed in many ways and can be set as X = (s 2,1 s 3,1 …s n,1 ) T .

[0343] The state equation is modeled as a random walk process, as shown in the following formula (1):

[0344]

[0345] in, and They correspond to the estimated values ​​of the state vector at time k and time k-1, Φ k,k-1 is the state transition matrix from time k-1 to time k, Φ k,k-1 =I n-1 ;w k It represents the process noise vector from time k-1 to time k, and its covariance matrix is ​​Q k , Q k =(q△t)·I n-1 , is a non-negative definite matrix, where q is the process noise power spectral density (its unit is ns / Hz), and △t is the sampling time interval.

[0346] B. Measurement equation

[0347] Assuming a single beacon UE u The coordinates of can be b=(x u ,y u ,h u ) T , TRP i The coordinates can be α i =(x i ,y i ,h i ) T ,i=1,2,…,n, the beacon UE and TRP location coordinate data are stored on the LMF side in step 1.

[0348] Taking TRP1 as the reference TRP, the TDOA relative ranging value is:

[0349]

[0350] in, is the UL-RTOA measurement value of TRP1 at time k (seconds), T i k TRP i(i=2,3,…,n) UL-RTOA measurement value at time k (seconds). The UL-RTOA measurement data is returned to the LMF in step 8. c=299792458 m / s is the speed of light in vacuum.

[0351] Construct the TDOA relative ranging measurement equation:

[0352]

[0353] Where a = c * 10 -9 is the speed of light in vacuum in m / ns, is the delay between base station RF receiving channels at time k, is the measurement noise of the TDOA relative ranging value (in meters), which is modeled as zero-mean white noise, and its variance-covariance matrix is

[0354] in, TRP i The UL-RTOA measurement variance converted to meters for (i=1,2,…,n) can be determined based on the nominal accuracy.

[0355] Next, we can construct the delay between base station RF receiving channels The unit is ns, as shown in the following formula (5):

[0356]

[0357] Then the delay measurement equation between base station RF receiving channels (corresponding to the first equation model mentioned above) is:

[0358]

[0359] in, is the delay measurement noise between base station RF receiving channels (ns). Since a is less than 1, The relative error Zoomed in Times, as follows:

[0360] The prior variance-covariance matrix of the delay measurement value between the base station RF receiving channels is:

[0361] Convert the delay measurement equation between base station RF receiving channels into matrix form: Z k =H k X k +e k .

[0362] in, is the measurement vector; H kis the design matrix, where H k =I n-1 , I n-1 is the identity matrix; is the measurement noise, and the corresponding covariance matrix is ​​R k , R k =D S , is a positive definite matrix.

[0363] For w k and e k , assuming the following conditions are met:

[0364]

[0365] Among them, δ kj is the Kronecker function.

[0366] The state equation model and measurement equation model constructed in this application can be used as input for the base station radio frequency receiving channel delay estimation algorithm based on the improved adaptive robust Kalman filter.

[0367] Since the solutions of the related art do not use the state estimation method to calculate the delay between base station RF receiving channels, there are problems with insufficient reliability and timeliness. Therefore, this application applies the means of Kalman filter state estimation to the online real-time calculation of the delay between base station RF receiving channels, solving the problem of constructing the state equation model and measurement equation model for the delay estimation between base station RF receiving channels based on Kalman filtering.

[0368] 2) Base station RF receiving channel delay estimation algorithm based on improved adaptive robust Kalman filter

[0369] A. Initial filter value and determination of P0

[0370] The determination of the initial value of the filter is the premise of the operation of the Kalman filter, when it meets the following conditions:

[0371]

[0372] During the filtering process and P k will be unbiased, for the case where the epoch before the current epoch is the first epoch, The initial values ​​of P0 can be:

[0373]

[0374] B. Time prediction

[0375] Through the state equation, the state vector at time k-1 is and its covariance matrix (Covariance matrix estimate corresponding to the fourth vector, i.e., the third covariance matrix) Predict the state vector at time k and its covariance matrix (corresponding to the second covariance matrix), i.e.:

[0376]

[0377] C. Calculate the innovation vector r k , the cross-covariance matrix P between the predicted state and the predicted measurement xz,k,k-1 , the covariance matrix of the innovation vector Then:

[0378]

[0379] D. Detection and processing of state jumps and measurement outliers

[0380] Use the innovation vector r k and its covariance matrix to construct a test statistic (corresponding to the first test statistic mentioned above), which is used for the detection of state jumps and measurement outliers.

[0381] Assume that the vector elements of r k are r k,i , i = 1, 2,..., n - 1, and the diagonal elements of Construct the test statistic When |v k,i | < C1, it is considered that there is no state jump or measurement outlier in the i-th element at time k, otherwise it is considered that there is a state jump or measurement outlier, where C1 is a priori threshold and can be determined according to experience.

[0382] When a state jump or measurement outlier is detected, use the inter-epoch difference measurement value to construct a test statistic to further distinguish between state jumps and measurement outliers. Since measurement outliers can be divided into single-epoch outliers and multi-epoch outliers according to the number of epochs affected, and can be divided into single-epoch recovery outliers and multi-epoch recovery outliers according to the number of epochs for the measurement to return to normal. Using and by selecting an appropriate test threshold, single-epoch - single-epoch recovery outliers can be effectively identified, while single-epoch - multi-epoch recovery outliers, multi-epoch - single-epoch recovery outliers, multi-epoch - multi-epoch recovery outliers and state jumps cannot be effectively distinguished. Therefore, design to use two groups of inter-epoch difference measurement values and (m > 1, typical value is 3). When (corresponding to the second test statistic satisfies the second condition) and (corresponding to the third test statistic meeting the second condition), it is considered that the i-th element at time k has a state jump, and the amplification Otherwise, it is considered that the i-th element at time k has an outlier value, and R is amplified. k The corresponding elements in .

[0383] Based on the above process, the adaptive factor (corresponding to the first factor mentioned above) and the robustness factor (corresponding to the second factor mentioned above) can be set as:

[0384]

[0385] Use two-factor model to adjust and R k for and in,

[0386]

[0387] in, and They are and The (i-1,j-1) element of and R k and The (i-1,j-1) element of .

[0388] In this way, sensitive conversion of delay state transitions between base station RF receiving channels and effective suppression of wild values ​​in delay measurement between different types of base station RF receiving channels are achieved. This solves the problem of insufficient accuracy and reliability of delay estimation between base station RF receiving channels when the delay state transitions when a cell undergoes adjustments such as reset or restart or a failure occurs, as well as when wild values ​​are present in TDOA measurement data. This application solution can effectively identify and process state transitions and measurement wild values, ensuring that the estimation process is not affected by state transitions and measurement wild values, thereby improving the accuracy and reliability of the estimation.

[0389] It can be seen that the various implementation forms of the traditional adaptive robust Kalman filter algorithm are essentially different from the improved adaptive robust Kalman filter algorithm in this application:

[0390] a. This application uses the innovation statistic and the delay measurement value between the base station radio frequency receiving channels of the two sets of epoch differences to construct the test statistic, which is different from the traditional adaptive robust Kalman filter algorithm;

[0391] b. Since the single-epoch-single-epoch recovery wild values, single-epoch-multi-epoch recovery wild values, multi-epoch-single-epoch recovery wild values, multi-epoch-multi-epoch recovery wild values, and state jump problems are used to distinguish and process the time delay estimation between base station RF receiving channels, the implementation form of the adaptive factor and the robustness factor in this application is completely different from the implementation form of the traditional adaptive robust Kalman filter.

[0392] E. Update the cross-covariance matrix between the predicted state and the predicted measurement Covariance matrix of the innovation vector

[0393]

[0394] F. Calculate the Kalman gain matrix K k :

[0395]

[0396] G. Measurement Update

[0397] Using measurement information and Make corrections to get the posterior estimate and P k :

[0398]

[0399] To ensure P k The symmetry of P k For further processing:

[0400]

[0401] H. Real-time estimation process noise covariance matrix

[0402] Real-time estimation of process noise covariance matrix using improved Sage-Husa adaptive filtering

[0403]

[0404] Among them, α k =(1-b) / (1-b k+1 ), b is the forgetting factor, and its value range is 0.95~0.99. Non-negative definite. Take the absolute value of the diagonal elements of the second matrix on the right side of the second equation above and take the off-diagonal elements to be zero.

[0405] When the standard Kalman filter is applied to the estimation of the time delay between base station RF receiving channels, a fixed value is used for the process noise variance of the random walk process of the time delay state between base station RF receiving channels. The time-varying nature of the process noise variance is not considered, resulting in limited estimation accuracy. The innovation of this application is to apply the improved Sage-Husa adaptive filter to the time-varying estimation of the process noise variance of the random walk process of the time delay state between base station RF receiving channels, thereby improving the estimation accuracy of the time delay between base station RF receiving channels. Compared with the standard Sage-Husa adaptive filter, the improved Sage-Husa adaptive filter has higher accuracy for time-varying estimation.

[0406] (2) Delay estimation and transmission between base station RF receiving channels

[0407] 1) Single beacon UEu

[0408] When the delay between base station RF receiving channels of beacon UEu is estimated by step 9 (1) Then, through the vector Subtracting any two elements in the , we can get the estimated delay between the base station RF receiving channels of all line-of-sight TRPs corresponding to the beacon UEu

[0409]

[0410] 2) Neighboring beacon UEu and beacon UEv

[0411] When the inter-base station RF receiving channel delay estimates of all line-of-sight TRPs corresponding to beacon UEu and beacon UE v are calculated respectively through step 9 (1) and step 9 (2) 1), it can be seen from step 1 that the corresponding line-of-sight TRP sets must be satisfied to have an intersection. Assume that TRP1 is the common line-of-sight TRP of beacon UEu and beacon UE v, and the TRP of beacon UEu is m and TRP of beacon UE v n For non-shared line-of-sight TRP, It can be obtained by the following formula:

[0412]

[0413] This application proposes a method for transmitting the delay estimation between base station radio frequency receiving channels, which can calculate the delay estimation between base station radio frequency receiving channels of all TRPs in indoor venues, solves the problem of incomplete delay estimation between base station radio frequency receiving channels estimated based on multiple beacon UEs, and improves the availability of delay estimation between base station radio frequency receiving channels.

[0414] (3) Data storage of delay estimation between base station RF receiving channels

[0415] According to the calculation results of step 9 (2), LMF stores the estimated delays between all base station RF receiving channels and the corresponding relative TRP IDs calculated at time k, and the data stored at time k+1 overwrites the data stored at time k.

[0416] Step 10: The LMF sends an NRPPa POSITIONING DEACTIVATION message to the serving gNB (corresponding to the eighth message mentioned above).

[0417] The actual scene effect diagram of this application scheme is as follows Figure 5 、 Figure 6 As shown, Figure 5 This is a timing diagram of estimating the relative radio frequency channel delay 91-92 of the beacon UE1 in an embodiment of the present application. Figure 6 This is a timing diagram of the relative RF channel delay 91-92 estimation of the beacon UE2 in an embodiment of the present application. The dotted line in the above figure is the measured value of the base station RF receiving channel delay relative to TRP ID 91-92, and the solid line is the estimated value of the base station RF receiving channel delay relative to TRP ID 91-92. It can be seen from the figure that the technical solution of the present application can achieve rapid convergence of estimation, sensitive conversion for state jumps, and effective suppression of measurement wild values. In addition, the estimation results of beacon UE1 and beacon UE2 are very close, which verifies that the base station RF receiving channel delay is almost unrelated to the beacon UE position.

[0418] Table 1: Positioning accuracy of UE1 before and after compensation for inter-base station RF receiving channel delay

[0419]

[0420] Table 2: Positioning accuracy of UE2 before and after compensation for inter-base station RF receiving channel delay

[0421]

[0422] Tables 1 and 2 compare the positioning accuracy before and after compensating the TDOA relative ranging values ​​of UE1 and UE2 using the calculation results of this application. It can be seen that the positioning accuracy of UE1 and UE2 after compensation is improved by an average of nearly 80%, which is a significant improvement.

[0423] This application addresses the phenomenon that the delay state between base station RF receiving channels may jump when the cell undergoes reset, restart or other adjustments or a failure occurs, as well as the phenomenon that wild values ​​exist in TDOA measurement data. By designing Kalman filter new information statistics, the delay state jump phenomenon between base station RF receiving channels and the wild value phenomenon of the delay measurement between base station RF receiving channels are judged. By designing two sets of inter-epoch difference statistics to construct a discriminant equation, the single-epoch-single-epoch recovery wild values ​​can be effectively identified, but single-epoch-multi-epoch recovery wild values, multi-epoch-single-epoch recovery wild values, multi-epoch-multi-epoch recovery wild values ​​and state jumps cannot be effectively distinguished. The anti-error factor and adaptive factor are designed to use a dual-factor model to adjust the predicted state covariance matrix and the measurement noise covariance matrix respectively, to achieve sensitive conversion of state jumps and effective suppression of measurement wild values. The complete process can be called an online real-time estimation method for the delay between base station RF receiving channels based on an improved adaptive anti-error Kalman filter.

[0424] For the case of multiple beacon UEs in a venue, this application can obtain the estimated time delays between all base station RF receiving channels in the venue through the estimated transmission of adjacent beacon UEs, provided that the line-of-sight TRP sets of adjacent beacon UEs intersect. This solves the problem of insufficient accuracy and reliability of the estimation of the time delay between base station RF receiving channels when the delay state between base station RF receiving channels jumps when the cell is reset, restarted, or fails, and when there are wild values ​​in the TDOA measurement data. It also solves the problem of calculating the delay between base station RF receiving channels estimated by a single beacon UE to the delay between the RF receiving channels of the full combination of base stations in the venue. It can also effectively identify and process state jumps and measurement wild values. The estimation process is not affected by state jumps and measurement wild values, thereby improving the accuracy and reliability of the estimation, and obtaining the delay between the RF receiving channels of the full combination of base stations in the indoor venue through transmission conversion.

[0425] This application selects beacon UEs and indoor sites, installs, deploys and maintains beacon UEs, and applies the Kalman filter algorithm model to the state estimation of the base station RF receiving channel delay based on the 5G UL-TDOA positioning signaling process and in combination with the TRP position data, beacon UE position data and 5G UL-TDOA measurement data obtained on the LMF side. A state equation model based on random walk is constructed according to the normal state characteristics of the base station RF receiving channel delay, and a measurement equation model is constructed according to the relationship between the conventional TDOA relative measurement value and the base station RF receiving channel delay. The improved Sage-Husa adaptive filter is used to estimate the process noise covariance matrix in real time, and the improved adaptive robust Kalman filter algorithm is used to estimate the line-of-sight base station RF receiving channel delay of a single beacon UE, and the line-of-sight base station RF receiving channel delay estimation of multiple beacon UEs is transferred to obtain the base station RF receiving channel delay of all indoor venues, and finally the results are stored in the database. It solves the technical problems of significant deviation in 5G uplink TDOA relative ranging caused by the time delay between base station RF receiving channels, which results in limited positioning accuracy, poor positioning reliability and insufficient positioning availability of 5G UL-TDOA indoor hyperbolic positioning solution. It has better real-time performance and high estimation accuracy. The estimated residual error can be less than 1ns, and the 5G UL-TDOA indoor hyperbolic positioning accuracy is close to 80%.

[0426] Compared with the solutions of the related art, the solution of this application has the following beneficial effects:

[0427] 1. This application is based on the UL-TDOA process of the 3GPP R16 standard and can be directly applied to 5G UL-TDOA positioning, significantly improving the UL-TDOA positioning accuracy.

[0428] 2. The technical solution process of this application is more complete, and describes the preliminary processes of estimating the delay between base station RF receiving channels, including beacon UE selection, beacon UE indoor site selection, beacon UE installation, deployment and maintenance, indoor local coordinate system establishment and position calibration.

[0429] 3. This application applies Kalman filtering theory to the time delay estimation between base station RF receiving channels, and solves the problem of constructing the state equation model and measurement equation model based on Kalman filtering for the time delay estimation between base station RF receiving channels.

[0430] 4. This application takes into account the state process properties, state jumps and measurement wild value phenomena of the base station RF receiving channel delay, and proposes a real-time estimation algorithm for the base station RF receiving channel delay based on an improved adaptive robust Kalman filter, which is more practical.

[0431] 5. This application applies the improved Sage-Husa adaptive filtering to the time-varying estimation of the process noise variance of the random walk process of the delay state between the base station RF receiving channels, which solves the problem of time-varying estimation of the process noise variance of the random walk process of the delay state between the base station RF receiving channels and is more practical.

[0432] In order to implement the delay determination method of the embodiment of the present application, the embodiment of the present application also provides a delay determination device, which is applied to LMF. Figure 7 FIG. 1 is a schematic diagram of the structure of the delay determination device according to an embodiment of the present application. Figure 7 As shown, the device includes:

[0433] The first receiving unit 71 is configured to receive first data, wherein the first data includes measurement data related to UL-RTOA;

[0434] The first determination unit 72 is used to determine the delay using the first data and the second data; the second data includes the location data of the TRP and the location data of the UE.

[0435] In one embodiment, the first receiving unit 71 is specifically configured to:

[0436] A first message is sent to a base station; the first message includes configuration information of a reference signal for positioning and information required to perform UL-RTOA measurement; based on the first message, a second message sent by the base station is received; the second message includes the first data.

[0437] In one embodiment, the first data is sent by a base station.

[0438] In one embodiment, the device further includes: an acquisition unit and a positioning unit; wherein,

[0439] The acquiring unit is configured to acquire the positioning capability of the UE and configuration information of a reference signal used for positioning of the UE;

[0440] The positioning unit is configured to position the UE based on the positioning capability of the UE and configuration information of a reference signal used for positioning of the UE.

[0441] In one embodiment, the device further includes: a second receiving unit; wherein,

[0442] The second receiving unit is configured to receive a third message sent by a base station; the third message includes uplink information, and the uplink information carries configuration information of a reference signal used for positioning of the UE.

[0443] In one embodiment, the UE is one or more, and the one or more UEs meet the target selection requirements and target site selection principles; wherein any TRP forms a line-of-sight relationship with the one or more UEs; when there are multiple UEs, the line-of-sight TRP sets of adjacent UEs have an intersection.

[0444] In one embodiment, the first determining unit 72 includes: a first constructing unit and a second determining unit; wherein,

[0445] The first construction unit is configured to construct a first equation model based on the first data and the second data; the first equation model represents a measurement equation model of a Kalman filter;

[0446] The second determining unit is configured to determine the time delay based on the first equation model and the second equation model; the second equation model represents a state equation model of the Kalman filter;

[0447] In which, the delay determination is related to the adjustment of the first covariance matrix and the second covariance matrix, the first covariance matrix represents the measurement noise covariance matrix, and the measurement noise is the noise of the constructed base station RF receiving channel delay measurement value; the second covariance matrix represents the covariance matrix estimate of the first vector, the first vector represents the time prediction value of the state vector of the current epoch, and the state vector is composed of the necessary linearly independent delay parameters to be estimated corresponding to each UE.

[0448] In one embodiment, the device further comprises: a second building unit; wherein,

[0449] The second construction unit is used to construct the second equation model;

[0450] Wherein, the second building block is specifically used for:

[0451] Determine a first matrix, a second vector, and a third vector; the first matrix represents a state transfer matrix from the previous epoch of the current epoch to the current epoch, the second vector represents a process noise vector from the previous epoch of the current epoch to the current epoch, and the third vector represents an estimated value of the state vector of the previous epoch of the current epoch; construct the second equation model based on the first matrix, the second vector, and the third vector.

[0452] In one embodiment, the first building unit is specifically used to:

[0453] Based on the first data and the second data, determine the delay measurement value between the base station radio frequency receiving channels of the current epoch; based on the delay measurement value between the base station radio frequency receiving channels of the current epoch and the delay measurement noise, construct the first equation model.

[0454] In one embodiment, the time delay is a time delay related to TDOA; the second determining unit includes: a third determining unit, a fourth determining unit and a fifth determining unit; wherein,

[0455] The third determining unit is configured to determine the first vector based on a fourth vector using the second equation model; the fourth vector includes an initial value of the state vector or an estimated value of the state vector of the previous epoch before the current epoch;

[0456] The fourth determining unit is configured to determine the first covariance matrix through the first equation model;

[0457] The fifth determining unit is configured to determine the time delay based on the first vector and the first covariance matrix.

[0458] In one embodiment, the fifth determining unit includes: a sixth determining unit and a seventh determining unit; wherein,

[0459] The sixth determining unit is configured to determine the second covariance matrix;

[0460] The seventh determining unit is configured to determine the time delay based on the first vector, the first covariance matrix, and the second covariance matrix.

[0461] In one embodiment, the sixth determining unit is specifically configured to:

[0462] Determine a third covariance matrix; the third covariance matrix represents a covariance matrix estimate of the fourth vector; and determine the second covariance matrix based on the third covariance matrix.

[0463] In one embodiment, the seventh determining unit includes: a first measurement updating unit and an eighth determining unit; wherein,

[0464] The first measurement update unit is configured to perform measurement update on the first vector based on the first covariance matrix and the second covariance matrix to obtain a fifth vector; the fifth vector represents an estimated value of the state vector of the current epoch;

[0465] The eighth determining unit is configured to determine the time delay based on the fifth vector.

[0466] In one embodiment, the first measurement updating unit includes: a ninth determining unit and a second measurement updating unit; wherein,

[0467] The ninth determining unit is configured to determine a fourth covariance matrix based on the second covariance matrix, and to determine a fifth covariance matrix based on the first covariance matrix;

[0468] The second measurement update unit is configured to perform measurement update on the first vector based on the fourth covariance matrix and the fifth covariance matrix to obtain the fifth vector.

[0469] In one embodiment, the ninth determining unit includes: a tenth determining unit and an adjusting unit; wherein,

[0470] The tenth determining unit is configured to determine a first factor and a second factor, wherein the first factor represents an adaptive factor, and the second factor represents a robustness factor;

[0471] The adjustment unit is configured to adjust the second covariance matrix based on the first factor to obtain the fourth covariance matrix, and to adjust the first covariance matrix based on the second factor to obtain the fifth covariance matrix.

[0472] In one embodiment, the tenth determining unit is specifically configured to:

[0473] When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the second condition, the first factor is determined to be the first value; the first test statistic represents the normalized innovation vector, the second test statistic represents the statistics composed of the first group of inter-epoch differential measurement values, and the third test statistic represents the statistics composed of the second group of inter-epoch differential measurement values; except when the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the second condition, the first factor is determined to be the second value.

[0474] In one embodiment, the tenth determining unit is specifically configured to:

[0475] When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the third condition, the second factor is determined to be the first value; the first test statistic represents the normalized innovation vector, the second test statistic represents the statistics composed of the first group of inter-epoch differential measurement values, and the third test statistic represents the statistics composed of the second group of inter-epoch differential measurement values; in addition to the first test statistic satisfying the first condition and the second test statistic and the third test statistic satisfying the third condition, the second factor is determined to be the second value.

[0476] In one embodiment, the apparatus further includes: an eleventh determining unit; wherein,

[0477] the eleventh determining unit, configured to determine whether the first test statistic satisfies a first condition;

[0478] The eleventh determining unit is specifically configured to:

[0479] When it is determined that there is a jump in the state vector or there is a measurement outlier in the delay measurement data, it is determined that the first test statistic meets the first condition; when it is determined that there is no jump in the state vector and there is no measurement outlier in the delay measurement data, it is determined that the first test statistic does not meet the first condition.

[0480] In one embodiment, the eleventh determining unit is specifically configured to:

[0481] Determine a first test statistic; the first test statistic represents a normalized innovation vector; based on the first test statistic, determine whether the state vector has a jump or the delay measurement data has a measurement outlier.

[0482] In one embodiment, the second measurement updating unit includes a twelfth determining unit and a third measurement updating unit; wherein,

[0483] The twelfth determining unit is configured to determine a second matrix based on the fourth covariance matrix and the fifth covariance matrix, wherein the second matrix represents an updated Kalman gain matrix;

[0484] The third measurement update unit is configured to perform measurement update on the first vector based on the second matrix to obtain the fifth vector.

[0485] In one embodiment, the twelfth determining unit is specifically configured to:

[0486] Based on the fourth covariance matrix, a sixth covariance matrix is ​​determined; the sixth covariance matrix represents the mutual covariance matrix of the updated predicted state and the predicted measurement; based on the sixth covariance matrix and the fifth covariance matrix, a seventh covariance matrix is ​​determined; the seventh covariance matrix represents the covariance matrix of the updated new information vector; based on the sixth covariance matrix and the seventh covariance matrix, the second matrix is ​​determined.

[0487] In one embodiment, the third measurement updating unit is specifically configured to:

[0488] Based on the product of the second matrix and the sixth vector, a seventh vector is determined; the sixth vector represents the innovation vector; based on the difference between the first vector and the seventh vector, the fifth vector is determined.

[0489] In one embodiment, when there is a single UE, the eighth determining unit is specifically configured to:

[0490] Based on the difference between any two vector elements in the fifth vector, the negative value of the vector element and the vector element itself, determine the delay estimation of the single UE; determine the delay estimation of the single UE as the delay; the delay includes the delay estimation between the base station RF receiving channels of all line-of-sight TRPs corresponding to the single UE.

[0491] In one embodiment, when there are multiple UEs, the eighth determining unit includes a thirteenth determining unit and a fourteenth determining unit; wherein,

[0492] The thirteenth determining unit is configured to determine a first delay based on the fifth vector, where the first delay represents a delay estimate of each UE;

[0493] The fourteenth determining unit is configured to determine the delay based on the first delay.

[0494] In one embodiment, the thirteenth determining unit is specifically configured to:

[0495] The first delay is determined based on the difference between any two vector elements in the fifth vector, the negative value of the vector element and the vector element itself; wherein the first delay includes the estimated delay between the base station RF receiving channels of all line-of-sight TRPs corresponding to each UE.

[0496] In one embodiment, the fourteenth determining unit is specifically configured to:

[0497] Based on the first delay, a second delay is determined; the second delay represents a delay estimate of a non-shared line-of-sight TRP of an adjacent UE; and the delay is determined based on the first delay and the second delay.

[0498] In actual application, the first receiving unit 71 can be implemented by a communication interface in the time delay determination device; and the first determining unit 72 can be implemented by a processor in the time delay determination device.

[0499] It should be noted that the delay determination device provided in the above embodiment is illustrated by the division of the aforementioned program modules. In actual applications, the aforementioned processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the aforementioned processing. Furthermore, the delay determination device provided in the above embodiment and the delay determination method embodiment are based on the same concept. The specific implementation process is detailed in the delay determination method embodiment and will not be repeated here.

[0500] Based on the hardware implementation of the above program module, and in order to implement the delay determination method of the embodiment of the present application, the embodiment of the present application also provides a delay determination device, which can be LMF, Figure 8This is a schematic diagram of the hardware structure of the delay determination device according to an embodiment of the present application. Figure 8 As shown, the time delay determination device 80 includes:

[0501] Communication interface 81, capable of exchanging information with other devices (such as base stations);

[0502] The processor 82 is connected to the communication interface 81 to implement information interaction with other devices and is used to execute the delay determination method provided above when running a computer program, and the computer program is stored in the memory 83.

[0503] It should be noted that the specific processing process of the communication interface 81 and the processor 82 can be understood with reference to the above-mentioned delay determination method.

[0504] Of course, in actual application, the various components in the delay determination device 80 are coupled together via the bus system 84. It is understood that the bus system 84 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 84 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 8 Various buses are labeled as bus system 84 .

[0505] The memory 83 in the embodiment of the present application is used to store various types of data to support the operation of the time delay determination device 80. Examples of such data include: any computer program used to operate on the time delay determination device 80.

[0506] The delay determination method disclosed in the above-mentioned embodiments of the present application can be applied to the processor 82 or implemented by the processor 82. The processor 82 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the delay determination method can be completed by hardware integrated logic circuits or software instructions in the processor 82. The processor 82 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 82 can implement or execute the various delay determination methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the delay determination method disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the memory 83. The processor 82 reads the information in the memory 83 and, in conjunction with its hardware, completes the steps of the delay determination method.

[0507] In an exemplary embodiment, the delay determination device 80 can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned delay determination method.

[0508] It can be understood that the memory 83 of the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 83 described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memory.

[0509] In order to implement the delay determination method of the embodiment of the present application, the embodiment of the present application also provides a delay determination system. Figure 9 FIG. 1 is a schematic diagram of the structure of the delay determination system according to an embodiment of the present application. Figure 9 As shown, the system includes: a base station 91 and a LMF 92; wherein,

[0510] The base station 91 is configured to send first data to the LMF 92; the first data includes measurement data related to UL-RTOA;

[0511] The LMF92 is used to receive first data; determine the delay using the first data and the second data; the second data includes the location data of the TRP and the location data of the UE.

[0512] It should be noted that the specific processing process for base station 91 and LMF 92 to complete the delay determination has been described in detail above and will not be repeated here.

[0513] In an exemplary embodiment, the present application further provides a storage medium, namely, a computer storage medium, specifically, a computer-readable storage medium, including, for example, a memory 83 storing a computer program. The computer program can be executed by a processor 82 in a delay determination device 80 to complete the steps of the delay determination method described in the aforementioned embodiment of the present application. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface mount storage, optical disk, or CD-ROM.

[0514] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which can be executed by the processor 82 in the delay determination device 80 to complete the steps of the delay determination method described in the above embodiment of the present application.

[0515] It should be noted that: "first", "second", "third", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0516] In addition, the technical solutions described in the embodiments of the present application can be arbitrarily combined without conflict.

[0517] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for determining a time delay, characterized in that: The method comprises: The location management function LMF receives first data; the first data comprises measurement data related to an uplink relative time of arrival UL-RTOA; The delay is determined using the first data and the second data; the second data includes the location data of the sending and receiving point TRP and the location data of the user equipment UE.

2. The method according to claim 1, characterized in that The receiving of the first data includes: Sending a first message to a base station; the first message includes configuration information of a reference signal for positioning and information required to perform UL-RTOA measurement; Based on the first message, a second message sent by the base station is received; the second message includes the first data.

3. The method according to claim 1, characterized in that The first data is sent by a base station.

4. The method according to claim 1, wherein The method further comprises: Acquire the positioning capability of the UE and configuration information of a reference signal used for positioning of the UE; The UE is positioned based on the positioning capability of the UE and configuration information of a reference signal of the UE used for positioning.

5. The method according to claim 1, wherein The method further comprises: Receive a third message sent by the base station; the third message includes uplink information, and the uplink information carries configuration information of a reference signal used for positioning of the UE.

6. The method according to claim 1, characterized in that The UE is one or more, and the one or more UEs meet the target selection requirements and target site selection principles; Among them, any TRP forms a line-of-sight relationship with the one or more UEs; when there are multiple UEs, the line-of-sight TRP sets of adjacent UEs have an intersection.

7. The method according to claim 1, characterized in that The determining the time delay by using the first data and the second data includes: Based on the first data and the second data, a first equation model is constructed; the first equation model represents a measurement equation model of a Kalman filter; Determining a time delay based on the first equation model and the second equation model; the second equation model represents a state equation model of a Kalman filter; In which, the delay determination is related to the adjustment of the first covariance matrix and the second covariance matrix, the first covariance matrix represents the measurement noise covariance matrix, and the measurement noise is the noise of the constructed base station RF receiving channel delay measurement value; the second covariance matrix represents the covariance matrix estimate of the first vector, the first vector represents the time prediction value of the state vector of the current epoch, and the state vector is composed of the necessary linearly independent delay parameters to be estimated corresponding to each UE.

8. The method according to claim 7, characterized in that The method further includes: constructing the second equation model; Wherein, constructing the second equation model includes: Determine a first matrix, a second vector, and a third vector; the first matrix represents a state transfer matrix from the previous epoch of the current epoch to the current epoch, the second vector represents a process noise vector from the previous epoch of the current epoch to the current epoch, and the third vector represents an estimated value of the state vector of the previous epoch of the current epoch; The second equation model is constructed based on the first matrix, the second vector and the third vector.

9. The method according to claim 7, characterized in that The constructing a first equation model based on the first data and the second data includes: Determine a base station radio frequency receive channel delay measurement value for a current epoch based on the first data and the second data; The first equation model is constructed based on the base station radio frequency receiving channel delay measurement value and delay measurement noise of the current epoch.

10. The method according to claim 7, characterized in that The time delay is a time delay related to the time difference of arrival (TDOA); and determining the time delay based on the first equation model and the second equation model includes: Determining the first vector based on a fourth vector using the second equation model; the fourth vector comprising an initial value of the state vector or an estimated value of the state vector of the previous epoch before the current epoch; Determining the first covariance matrix through the first equation model; The time delay is determined based on the first vector and the first covariance matrix.

11. The method according to claim 10, characterized in that The determining the time delay based on the first vector and the first covariance matrix includes: determining the second covariance matrix; The time delay is determined based on the first vector, the first covariance matrix, and the second covariance matrix.

12. The method according to claim 11, characterized in that The determining of the second covariance matrix includes: Determining a third covariance matrix; the third covariance matrix represents an estimated value of the covariance matrix of the fourth vector; Based on the third covariance matrix, the second covariance matrix is ​​determined.

13. The method according to claim 11, characterized in that The determining the time delay based on the first vector, the first covariance matrix, and the second covariance matrix includes: Based on the first covariance matrix and the second covariance matrix, the first vector is measured and updated to obtain a fifth vector; the fifth vector represents an estimated value of the state vector of the current epoch; Based on the fifth vector, the time delay is determined.

14. The method according to claim 13, characterized in that The step of measuring and updating the first vector based on the first covariance matrix and the second covariance matrix to obtain a fifth vector includes: determining a fourth covariance matrix based on the second covariance matrix, and determining a fifth covariance matrix based on the first covariance matrix; Based on the fourth covariance matrix and the fifth covariance matrix, the first vector is measured and updated to obtain the fifth vector.

15. The method according to claim 14, characterized in that The determining of a fourth covariance matrix based on the second covariance matrix, and determining of a fifth covariance matrix based on the first covariance matrix, comprises: Determine a first factor and a second factor; the first factor represents an adaptive factor, and the second factor represents a robustness factor; The second covariance matrix is ​​adjusted based on the first factor to obtain the fourth covariance matrix, and the first covariance matrix is ​​adjusted based on the second factor to obtain the fifth covariance matrix.

16. The method according to claim 15, characterized in that The determining of the first factor comprises: When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the second condition, determining that the first factor is a first value; the first test statistic represents a normalized innovation vector, the second test statistic represents a statistic consisting of the first set of inter-epoch difference measurement values, and the third test statistic represents a statistic consisting of the second set of inter-epoch difference measurement values; When the first test statistic satisfies a first condition and the second test statistic and the third test statistic satisfy a second condition, the first factor is determined to be a second value.

17. The method according to claim 15, characterized in that Determining the second factor includes: When the first test statistic satisfies the first condition and the second test statistic and the third test statistic satisfy the third condition, determining the second factor to be the first value; the first test statistic represents the normalized innovation vector, the second test statistic represents the statistic composed of the first set of inter-epoch difference measurement values, and the third test statistic represents the statistic composed of the second set of inter-epoch difference measurement values; When the first test statistic satisfies a first condition and the second test statistic and the third test statistic satisfy a third condition, the second factor is determined to be a second value.

18. The method according to claim 16 or 17, characterized in that The method further includes: determining whether the first test statistic satisfies a first condition; Determining whether the first test statistic satisfies a first condition includes: When it is determined that the state vector has a jump or the delay measurement data has a measurement outlier, determining that the first test statistic satisfies a first condition; When it is determined that the state vector does not have a jump and the delay measurement data does not have a measurement outlier, it is determined that the first test statistic does not meet the first condition.

19. The method according to claim 18, characterized in that The determining that the state vector has a jump or the delay measurement data has a measurement wild value includes: Determining a first test statistic; wherein the first test statistic represents a normalized innovation vector; Based on the first test statistic, it is determined that there is a jump in the state vector or there is a measurement outlier in the delay measurement data.

20. The method according to claim 14, wherein The step of measuring and updating the first vector based on the fourth covariance matrix and the fifth covariance matrix to obtain the fifth vector includes: Determining a second matrix based on the fourth covariance matrix and the fifth covariance matrix; wherein the second matrix represents an updated Kalman gain matrix; Based on the second matrix, the first vector is measured and updated to obtain the fifth vector.

21. The method according to claim 20, characterized in that The determining of the second matrix based on the fourth covariance matrix and the fifth covariance matrix includes: Determining a sixth covariance matrix based on the fourth covariance matrix; the sixth covariance matrix represents a cross-covariance matrix of the updated predicted state and the predicted measurement; Determine a seventh covariance matrix based on the sixth covariance matrix and the fifth covariance matrix; the seventh covariance matrix represents the covariance matrix of the updated innovation vector; The second matrix is ​​determined based on the sixth covariance matrix and the seventh covariance matrix.

22. The method according to claim 20, characterized in that The step of measuring and updating the first vector based on the second matrix to obtain the fifth vector includes: Determine a seventh vector based on the product of the second matrix and a sixth vector; the sixth vector represents an innovation vector; The fifth vector is determined based on a difference between the first vector and the seventh vector.

23. The method according to claim 14, wherein The method further comprises: Based on the fourth covariance matrix and the fifth covariance matrix, measuring and updating the fourth covariance matrix to obtain a ninth covariance matrix; Based on the ninth covariance matrix, the tenth covariance matrix is ​​time-varyingly estimated to obtain an eleventh covariance matrix; the tenth covariance matrix represents the covariance matrix of the second vector, the second vector represents the process noise vector from the previous epoch of the current epoch to the current epoch, and the eleventh covariance matrix represents the estimated value of the process noise covariance matrix.

24. The method according to claim 13, wherein When there is a single UE, the determining the delay based on the fifth vector includes: Determine a single UE delay estimate based on a difference between any two vector elements in the fifth vector, a negative value of a vector element, and the vector element itself; The delay estimation of the single UE is determined as the delay; the delay includes the delay estimation between the base station radio frequency receiving channels of all line-of-sight TRPs corresponding to the single UE.

25. The method according to claim 13, wherein When there are multiple UEs, the determining the delay based on the fifth vector includes: Determining a first delay based on the fifth vector, wherein the first delay represents a delay estimate of each UE; The time delay is determined based on the first time delay.

26. The method according to claim 25, characterized in that The determining a first delay based on the fifth vector includes: determining the first time delay based on a difference between any two vector elements in the fifth vector, a negative value of a vector element, and the vector element itself; Among them, the first delay includes the base station RF receiving channel delay estimation of all line-of-sight TRPs corresponding to each UE.

27. The method according to claim 25, characterized in that The determining the delay based on the first delay includes: Determining a second delay based on the first delay; wherein the second delay represents a delay estimate of a non-shared line-of-sight TRP of an adjacent UE; The delay is determined based on the first delay and the second delay.

28. A delay determination device, characterized in that: The device comprises: A first receiving unit is configured to receive first data, wherein the first data includes measurement data related to an uplink relative time of arrival UL-RTOA; The first determination unit is used to determine the delay using the first data and the second data; the second data includes the location data of the sending and receiving point TRP and the location data of the user equipment UE.

29. A time delay determination device, characterized in that: include: a processor and a memory for storing a computer program capable of running on said processor; Wherein, when the processor is used to run the computer program, it executes the steps of the method according to any one of claims 1 to 27.

30. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 27 are implemented.

31. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 27.

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