Positioning method and device, electronic equipment and computer program product

Abstract

The invention relates to the technical field of wireless and terminals, and particularly provides a positioning method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a first ranging signal of a first positioning source and a second ranging signal of a second positioning source corresponding to a to-be-positioned target; the observation noise is determined based on the first ranging signal, the second ranging signal and a preset random model, and the preset random model is determined at least based on a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise; determining a positioning combination result based on speed constraint and observation noise; and performing nonlinear processing on the positioning combination result, and determining a final positioning result of the to-be-positioned target. According to the invention, the random model is refined according to the characteristics of different positioning sources, the continuous smooth positioning of the transition scene is achieved, and the linearization error is weakened, so that the availability, smoothness and accuracy of the positioning result are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless and terminal technologies, and in particular to a positioning method, device, electronic device, and computer program product. Background Art

[0002] The Beidou satellite navigation system is suitable for use in open scenes, but not in densely built areas, tree-lined avenues and other obstructed urban canyon areas. 5G positioning is suitable for use in indoor obstructed scenes. The integration of the two can complement each other and is suitable for use in the transition from open scenes to fully obstructed scenes, which can improve the availability, smoothness and continuity of positioning navigation.

[0003] However, in the existing Beidou + 5G tight combination positioning technology solutions, the arrival time is generally used to obtain the Beidou and 5G signal measurement distances respectively, the distance observation equation is linearized, and the least squares, extended Kalman filter (EKF) and other methods are used for fusion positioning. There will be problems such as poor availability and smoothness of positioning results, and inaccurate positioning results. Summary of the invention

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides a positioning method, device, electronic device and computer program product.

[0005] According to one aspect of the present disclosure, a positioning method is provided, the method comprising: acquiring a first ranging signal of a first positioning source and a second ranging signal of a second positioning source corresponding to a target to be positioned; determining observation noise based on the first ranging signal, the second ranging signal and a preset random model, wherein the preset random model is determined based on at least a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise; determining a positioning combination result based on a velocity constraint and the observation noise; and performing nonlinear processing on the positioning combination result to determine a final positioning result of the target to be positioned.

[0006] In addition, according to one aspect of the present disclosure, a positioning method, wherein a preset random model is determined based on at least a first signal characteristic of a first observation noise and a second signal characteristic of a second observation noise, includes: determining a first signal characteristic and a second signal characteristic based on the first observation noise and the second observation noise; determining a first preset random result and a second preset random result based on the first signal characteristic and the second signal characteristic.

[0007] In addition, according to a positioning method according to one aspect of the present disclosure, determining a positioning combination result based on speed constraints and observation noise includes: determining a speed constraint based on multiple speed measurement models; determining a dynamic result in the positioning combination result based on the speed constraint; and determining an observation result in the positioning combination result based on observation noise.

[0008] In addition, according to a positioning method of one aspect of the present disclosure, nonlinear processing is performed on the positioning combination result to determine the final positioning result of the target to be positioned, which includes: obtaining the initial positioning result of the target to be positioned; determining an updated positioning result based on the dynamic result and the initial positioning result; determining the weight of the updated positioning result based on multiple updated positioning results and observation results; adjusting the weight of the updated positioning result to determine the characteristic value of the updated positioning result; and determining the final positioning result of the target to be positioned when the characteristic value is less than a preset threshold.

[0009] According to another aspect of the present disclosure, a positioning device is provided, the device comprising: a signal acquisition module, used to acquire a first ranging signal of a first positioning source and a second ranging signal of a second positioning source corresponding to a target to be positioned; a noise determination module, used to determine an observation noise based on the first ranging signal, the second ranging signal and a preset random model, wherein the preset random model is determined based on at least a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise; a positioning result module, used to determine a positioning combination result based on a velocity constraint and the observation noise; and a filtering processing module, used to perform nonlinear processing on the positioning combination result to determine a final positioning result of the target to be positioned.

[0010] In addition, according to a positioning device according to one aspect of the present disclosure, the noise determination module includes: an analysis unit for determining a first signal characteristic and a second signal characteristic based on a first observed noise and a historical second observed noise; and a determination unit for determining a first preset random result and a second preset random result based on the first signal characteristic and the second signal characteristic.

[0011] In addition, according to a positioning device according to one aspect of the present disclosure, the positioning result module includes: a speed constraint unit, which is used to determine the speed constraint based on multiple speed measurement models; a dynamic result unit, which is used to determine the dynamic result in the positioning combination result based on the speed constraint; and an observation result unit, which is used to determine the observation result in the positioning combination result based on the observation noise.

[0012] In addition, according to a positioning device according to one aspect of the present disclosure, the filtering processing module includes: an initial unit, used to obtain an initial positioning result of the target to be positioned; an updating unit, used to determine an updated positioning result based on the dynamic result and the initial positioning result; a weight unit, used to determine the weight of the updated positioning result based on the updated positioning result and the observation result; a calculation unit, used to adjust the weight of the updated positioning result and determine the characteristic value of the updated positioning result; and a positioning unit, used to determine the final positioning result of the target to be positioned when the characteristic value is less than a preset threshold.

[0013] According to yet another aspect of the present disclosure, an electronic device is provided, including: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions so that the electronic device executes the positioning method as described above.

[0014] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the positioning method as described above is implemented.

[0015] As will be described in detail below, according to the positioning method of the embodiment of the present disclosure, the random model is refined according to the characteristics of different positioning sources, continuous and smooth positioning of transition scenes is achieved, and linearization errors are weakened, thereby improving the availability, smoothness and accuracy of the positioning results.

[0016] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 It is a scenario diagram illustrating an application scenario of the positioning method according to an embodiment of the present disclosure.

[0019] Figure 2 is a method flow chart illustrating a positioning method according to an embodiment of the present disclosure.

[0020] Figure 3 It is a flowchart illustrating the overall process of the positioning method according to an embodiment of the present disclosure.

[0021] Figure 4 is a flowchart illustrating a method for nonlinear processing in a positioning method according to an embodiment of the present disclosure.

[0022] Figure 5 is a schematic diagram of a positioning device according to an embodiment of the present disclosure.

[0023] Figure 6 is a hardware block diagram illustrating an electronic device according to an embodiment of the present disclosure; and

[0024] Figure 7 is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present disclosure more obvious, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0026] First, refer to Figure 1 The application scenarios according to the embodiments of the present disclosure are summarized.

[0027] Figure 1 is a schematic diagram illustrating an application scenario of the positioning method according to an embodiment of the present disclosure. Figure 1 As shown, the application scenario includes at least: positioning satellite, base station, user terminal and server (wherein the server Figure 1 not shown).

[0028] Among them, positioning satellites are satellites that can provide positioning and navigation services on a global scale, including but not limited to: satellites of positioning satellite systems such as the Global Positioning System GPS, the Global Navigation Satellite System GLONASS, Galileo, and the Beidou Satellite Navigation System. For the sake of convenience in description, this disclosure takes Beidou satellites as an example. It should be noted that this is only an exemplary description and does not constitute a limitation. Beidou satellites can achieve static positioning accuracy of decimeters or even centimeters and dynamic positioning accuracy of meters to sub-meters in open scenes for civilian consumer-grade terminals. However, the signal strength of the Beidou satellite reaching the ground is very weak. In urban canyon areas such as densely built areas and boulevards, the Beidou navigation signal will be blocked or interfered with, resulting in positioning failure.

[0029] The base station is an important component of the wireless communication system, which can be a base station (base transceiver station, BTS) in the global mobile communication system GSM or code division multiple access CDMA communication system, a base station (nodeB, NB) in the wideband code division multiple access WCDMA communication system, an evolutionary base station (eNB or eNodeB) in the long-term evolution LTE communication system, a next generation base station (next generation evolved node B, ng-eNB) in the new air interface NR communication system, a next generation base station (next generation node B, gNB) in the NR communication system, or a base station in a future communication system or other equipment in the core network (CN), and the present disclosure does not make specific limitations. Similarly, for the convenience of description, the present disclosure takes a 5G base station as an example, which is only an exemplary description and does not constitute a limitation.

[0030] The emergence of the fifth generation of mobile communication technology (5G) provides a more accurate technical means for wireless ranging. 5G uses large-scale array antenna technology with higher degrees of freedom, which can further improve the accuracy of ranging and angle measurement. 5G can make full use of the positioning advantages of millimeter waves, dense deployment of small base stations and beamforming to achieve more accurate indoor positioning. However, due to the propagation characteristics of millimeter waves with large path loss, 5G signals attenuate severely with distance, and effective positioning cannot be performed when the measuring station is far away from the base station.

[0031] Therefore, in the transition from open areas to completely blocked scenes, it is necessary to coordinate and integrate the two positioning methods of 5G and Beidou to form complementarity and further provide user terminals with better positioning services in complex environments.

[0032] Among them, in Beidou + 5G combined positioning, based on the Beidou satellite navigation system and 5G communication technology, the signal propagation distance is calculated by measuring the arrival time of the signal from the signal source (such as the above-mentioned Beidou satellite or 5G base station) to the receiving device (such as the user terminal).

[0033] Specifically, when a mobile device receives a signal from a Beidou satellite or a 5G base station, it records the timestamp of the signal's arrival. By comparing the time the signal was sent and the time it arrived, the time it takes for the signal to propagate in space can be calculated, and the signal's propagation distance can be calculated by multiplying speed by time.

[0034] The user terminal may be an IoT device with wireless transceiver functions, and may be a handheld terminal device, a vehicle, a vehicle-mounted terminal, a smart wearable device, or other computing device. For example, the positioning terminal may be a portable terminal device such as a mobile phone or a tablet computer; it may also be an augmented reality (AR) device, a virtual reality (VR) device, or an ultra-mobile personal computer (UMPC). It is easy to understand that the embodiments of the present disclosure do not limit the specific type and number of user terminals.

[0035] The server may be a server for supporting navigation and positioning technology, and may communicate with positioning satellites, base stations, and user terminals.

[0036] Figure 2 FIG. 1 is a flowchart illustrating a positioning method according to an embodiment of the present disclosure. Figure 2 As shown, the positioning method may include at least the following steps.

[0037] In step S201, obtain the first ranging signal of the first positioning source and the second ranging signal of the second positioning source corresponding to the target to be positioned. As mentioned above, for the convenience of description, the first positioning source can be exemplarily understood as Beidou navigation, so the first ranging signal can be exemplarily understood as Beidou ranging signal, and the second positioning source can be exemplarily understood as 5G, so the second ranging signal can be exemplarily understood as 5G ranging signal, which does not constitute a limitation. The present disclosure aims to improve the accuracy, smoothness, etc. of Beidou + 5G joint positioning and navigation in the transition from open scenes to completely blocked scenes. This step obtains Beidou ranging signals and 5G ranging signals respectively.

[0038] In step S202, based on the first ranging signal, the second ranging signal and the preset random model, the observation noise is determined, wherein the preset random model is determined based on at least the first signal characteristic of the first observation noise and the second signal characteristic of the second observation noise. As described above, in the prior art, when building the random model, the signal characteristics of Beidou and 5G signals are not considered, resulting in large errors. When building the preset random model, the present disclosure obtains a more accurate random model based on the respective characteristics of the Beidou signal and the 5G signal, specifically in Figure 3 The step S1 is described in detail. Then, a more accurate observation noise can be obtained based on the random model to optimize the observation result.

[0039] In step S203, the positioning combination result is determined based on the speed constraint and the observation noise. The positioning combination result may include the dynamic result and the above observation result. As mentioned above, there is a problem of uneven trajectory in the prior art. In order to solve this problem and improve the accuracy of positioning, the present disclosure introduces a speed constraint into the dynamic equation of the positioning combination result (i.e., the dynamic result), and combines the observation noise calculated based on the preset random model in step S202 to generate a more accurate positioning combination result. Specifically, Figure 3 The step S3 is described in detail.

[0040] In step S204, nonlinear processing is performed on the positioning combination result to obtain the final positioning result of the target to be positioned. This step is intended to use the nonlinear processing as a nonlinear optimal estimation method to obtain a smoother and more accurate positioning result. Figure 3 The step S4 is described in detail.

[0041] Figure 3 FIG. 2 is a flow chart illustrating the overall flow of the positioning method according to an embodiment of the present disclosure. Figure 3 As shown, the overall process of the positioning method in the embodiment of the present disclosure may include the following steps (still taking Beidou + 5G as an example for exemplary description):

[0042] S1. Determine the preset random model

[0043] This step can be understood as a pre-step. When positioning based on Beidou and 5G signals, the positioning results are often affected by a certain degree of randomness due to various environmental factors, signal propagation characteristics, hardware errors and other factors. This step determines the first signal characteristics and the second signal characteristics based on the first observation noise and the second observation noise; based on the first signal characteristics and the second signal characteristics, determines the first preset random model random results and the second preset random model results to improve positioning accuracy and reliability.

[0044] Specifically, by analyzing and processing a large amount of historical data (for example, Beidou navigation module observation noise and 5G signal observation noise, i.e., the first observation noise and the second observation noise), the random characteristics and error distribution laws in the system can be extracted, and then a preset random model (for example, a preset Beidou random model and a preset 5G random model, i.e., the first preset random result and the second preset random result) can be determined to describe the characteristics of these random errors so as to correct them in subsequent positioning.

[0045] Random models are also used in the prior art, but they do not take signal characteristics into account in the process of establishing random models, so the effect is often not ideal. The present disclosure proposes to refine the preset Beidou random model and the preset 5G random model based on the characteristics of the stronger correlation between the observation noise and the signal-to-noise ratio of the consumer-grade Beidou navigation module (i.e., the first signal characteristic mentioned above) and the serious attenuation of 5G signals with distance (i.e., the second signal characteristic mentioned above).

[0046] In one embodiment of the present disclosure, for a consumer-grade Beidou navigation module, since its observation noise is more strongly correlated with the signal-to-noise ratio, a signal-to-noise ratio weighting model may be used:

[0047]

[0048] Among them, σ j represents the observation noise of the jth satellite, CNR j represents the carrier-to-noise ratio of the jth satellite, and a, b and c are empirical parameters.

[0049] Furthermore, due to the differences in signal quality and characteristics of each satellite system and each frequency band, in order to obtain a more accurate random model, it is necessary to independently calculate the different frequency points of the Beidou satellite system, and obtain the empirical parameters a, b and c corresponding to the different frequency points, respectively. In combination with the actual environmental conditions, a signal-to-noise ratio weighted model suitable for the current environment is obtained, which is used for weighted processing of signals at different frequencies, thereby improving the accuracy and robustness of the positioning system in different frequency bands, which serves as the refined preset Beidou random model.

[0050] In one embodiment of the present disclosure, for 5G signals, the attenuation is serious with the distance, so the preset 5G random model can be refined according to this characteristic. Specifically:

[0051] Received Signal Power (RSP) can be used as an important indicator to measure the strength of 5G signals. The formula for the received power of a 5G signal receiving terminal (such as UE) is as follows:

[0052]

[0053] Among them, P t , P r Respectively represent the transmit power and receive power of 5G signals; G t is the transmitting antenna gain; G r is the receiving antenna gain; d is the distance between the signal transmitting and receiving terminal antennas; λ is the 5G signal wavelength.

[0054] Then, the reference distance d0 is introduced. The reference distance can be used as a basis for calculating the path loss. Substituting the reference distance d0 into the above received power formula, we have:

[0055]

[0056] In order to more intuitively show the trend of signal strength changing with distance, the above formula is converted to decibel milliwatt (dBm) to obtain the logarithmic propagation attenuation model of 5G signals in free space:

[0057]

[0058] Where P(d) is the signal receiving strength when the distance between the signal receiving end and the transmitting end is d; P(d0) is the signal receiving strength when the distance between the signal receiving end and the transmitting end is d0. Free space refers to an open area without any obstacles or interferences, in which the signal can propagate freely without being hindered or attenuated. In the field of wireless communications, free space can be understood as an ideal propagation environment, in which signal propagation is not interfered by objects such as buildings, trees, and terrain, nor is it affected by multipath propagation, reflection, diffraction, etc.

[0059] However, in actual environments, especially indoor environments, the free space propagation model is no longer applicable due to the obstruction of obstacles, the movement of people, and the reflection, diffraction, and scattering of 5G signals. The attenuation model under the free space mentioned above needs to be further processed:

[0060]

[0061] Where d0 is the known distance between the mobile station and the base station; n is the path loss index, which is related to the indoor environment and building materials; F is the attenuation factor, and are the signal receiving power and propagation distance received by the UE from the i-th 5G base station respectively.

[0062] In order to establish the relationship between signal strength and measurement noise, it is necessary to set up a 5G mobile station at a known point indoors to receive the 5G signals broadcast by each base station. The path loss index n and the attenuation factor F are fitted using the known mobile station and base station coordinates, and the signal propagation distance of the i-th 5G base station is calculated based on the attenuation factor model. and distance observations By doing subtraction, we can get the observation noise of the 5G ranging signal, which is used as the refined preset 5G random model:

[0063]

[0064] Among them, σ i represents the observation noise of the i-th 5G base station, d i The signal propagation distance of the i-th 5G base station is calculated based on the attenuation factor model, which is related to the path loss index n and the attenuation factor F.

[0065] At this point, step S1 is introduced, and a preset Beidou random model and a preset 5G random model (collectively referred to as preset random models) determined based on the Beidou and 5G signal characteristics are obtained. The above-mentioned preset random models are based on the signal characteristics of different signals to more accurately model their random errors, and the observation noise obtained by the above-mentioned preset random models is also more accurate.

[0066] S2. Determine the observation noise

[0067] As described above, in step S1, a more accurate preset Beidou random model and a preset 5G random model have been obtained based on historical data. In this step, the acquired real-time Beidou ranging signal and 5G ranging signal are respectively brought into the above-mentioned preset random model to obtain real-time observation noise (specifically described in detail in conjunction with step S3), which is used for the observation results in the subsequent positioning combination results, so that the observation results are more accurate.

[0068] S3. Determine the positioning combination result

[0069] As described above, the positioning combination result may include dynamic results and observation results. The observation results in the positioning combination result are determined based on the observation noise; the dynamic results in the positioning combination result are determined based on the speed constraint, wherein the speed constraint is determined based on multiple speed measurement models, which will be introduced one by one below:

[0070] First, determine the observation results (also called observation equations):

[0071] The core idea of ​​Beidou and 5G combined positioning is to use each 5G base station as a pseudo-satellite for ranging positioning. In one embodiment of the present disclosure, assume that the coordinates of the UE are marked as (x0, y0, z0), and the coordinates of the i-th 5G base station are marked as (x i ,y i ,z i ), where 1≤i≤n, n is the total number of 5G base stations, and the observed distance from the UE to the i-th 5G base station is denoted as d i , then

[0072]

[0073] Where c represents the speed of light, which is 299,792,458 m / s, and δt 5G represents the timing error, ε 5G represents the 5G signal noise term. It should be noted that ε 5G That is, the 5G real-time observation noise calculated based on the preset 5G random model obtained in step S1.

[0074] Similarly, in one embodiment of the present disclosure, when the coordinates of the UE are still (x0, y0, z0), it is assumed that the coordinates of the jth satellite are m is the total number of satellites, and the observed distance from the UE to the jth satellite is recorded as P j , then

[0075]

[0076] in, They represent the satellite clock error of satellite j, which can be corrected by the satellite clock error parameters provided by the broadcast ephemeris; δt r Represents the receiver clock error of the receiver; I j ,T j represents the ionospheric delay error and the tropospheric delay error, ε j represents the pseudorange noise term. It should be noted that ε j That is, the Beidou real-time observation noise calculated based on the preset Beidou random model obtained in step S1.

[0077] Combining the above two formulas, the Beidou + 5G combined positioning observation equation is:

[0078]

[0079] For the convenience of expression, the above formula can be simplified as

[0080] L k =h(X k)+ε k

[0081] Among them, X k is the parameter to be estimated, L k is the observed distance between Beidou and 5G signals, h(X k ) reflects L k and the parameter to be estimated X k The relationship between.

[0082] Next, determine the dynamic results (also called dynamic equations):

[0083] Since UE can be in constant motion, the estimated parameter X k It is necessary to describe how the UE changes over time, which can be understood as a dynamic equation. The parameters to be estimated mainly include coordinate parameters and clock error parameters. In the prior art, the dynamic equation generally uses a constant velocity model or a constant acceleration model to simulate the movement of the UE. However, for more complex movements, these models are difficult to fit accurately, and the robustness of subsequent filtering processing cannot be guaranteed. In severe cases, it may even cause filtering divergence. Therefore, the present disclosure proposes to use Doppler velocity measurement or carrier phase epoch difference (Time-differenced Carrier Phase, TDCP) velocity measurement to constrain the position change of the carrier in the previous and next epochs.

[0084] Among them, TDCP speed measurement has higher accuracy, but the speed measurement result will jump when the phase is small; Doppler speed measurement has lower accuracy than TDCP speed measurement, but its result is more stable in complex scenarios. Therefore, the present disclosure combines Doppler speed measurement and TDCP speed measurement to select a better speed, thereby ensuring the high accuracy and reliability of the result. The details are as follows:

[0085] In one embodiment of the present disclosure, the Doppler velocity measurement observation equation is as follows:

[0086]

[0087] Among them, λ f is the wavelength corresponding to the frequency f, Satellite S i The Doppler observation value corresponding to the frequency point f, is the geometric distance from the satellite antenna phase center to the receiver receiving center, r R and are the receiver position and velocity vectors, respectively, and the receiver velocity vector contains the velocity to be determined at the current epoch k and Satellite S i The position and velocity vectors of , c is the speed of light, is the receiver clock rate, is the satellite clock speed, and are the delay change rates of the troposphere and ionosphere, respectively. is the Doppler observation noise.

[0088] The observation equation is linearized and solved to obtain the instantaneous velocity at the current epoch k. To obtain the position change of the carrier from the kth to the k+1th epoch The calculation used is as follows:

[0089]

[0090] In one embodiment of the present disclosure, the TDCP speed measurement observation equation is as follows:

[0091]

[0092] Where Δ is the epoch difference operator, Satellite S i The carrier phase observation value corresponding to the frequency point f, is the displacement change between the previous and next epochs, including the displacement to be calculated [dx dy dz] T , and dt r Satellite S i and the receiver clock difference, tropospheric error and ionospheric errors It changes slowly over time, and after taking the difference between epochs, its residual error can be ignored. is the carrier phase epoch difference observation equation noise.

[0093] By linearizing and solving the observation equation, we can obtain the position change between the previous and next epochs [dx dy dz] T .

[0094] Combining the above Doppler velocity observation equation and TDCP velocity observation equation, the dynamic equation of velocity constraint is:

[0095] X k =Φ k,k-1 ·X k-1 +B k-1 ·u k-1 +v k-1

[0096] The parameters to be estimated are

[0097]

[0098] In the dynamic equation

[0099] Φk,k-1 =I

[0100] B k-1 =I·Δt

[0101] u k-1 =[dx dy dz] T

[0102] Among them, u k-1 is the control input, which is the speed selection result of Doppler speed measurement or TDCP speed measurement. It should be noted that if the TDCP speed measurement is successful, the TDCP speed measurement result is used; if the TDCP speed measurement fails but the Doppler speed measurement is successful, the Doppler speed measurement result is used; if the TDCP speed measurement fails and the Doppler speed measurement fails, the constant speed model is used.

[0103] In summary, the positioning combination model can include dynamic equations and observation equations, which can be expressed as:

[0104]

[0105] Among them, k is the time, X k is the state variable, Φ k,k-1 is the state transfer matrix of the constant velocity model, v k-1 is the state noise, L k is the observed distance between Beidou and 5G signals, h(X k ) reflects the distance observation value L k and the parameter to be estimated X k The relationship between k is the ranging noise of Beidou and 5G signals, as mentioned above, ε k is calculated based on the random model in step S1.

[0106] So far, step S3 has been introduced, and the combined positioning result of Beidou + 5G has been obtained. This result constructs a speed-constrained combined positioning result by combining Doppler speed measurement and TDCP speed measurement, which solves the problems of inaccurate positioning and uneven trajectory in transition scenarios, and realizes continuous and smooth positioning in transition scenarios, meeting the user's seamless and blind-spot-free positioning service needs.

[0107] S4. Optimal Estimation

[0108] In order to further improve the positioning accuracy and stability, it is necessary to perform nonlinear processing on the combined positioning result of Beidou + 5G in step S3.

[0109] In one embodiment of the present disclosure, the nonlinear processing method for weakening the linearization error in the Beidou + 5G combined positioning result may be filtering processing, which may be as follows: Figure 4 shown.

[0110] Figure 4 FIG. 1 is a flowchart illustrating a method for nonlinear processing in a positioning method according to an embodiment of the present disclosure. Figure 4 The nonlinear processing method may include at least the following steps: In step S401, an initial positioning result of a target to be positioned is obtained. Specifically, the target to be positioned is a UE, and an initial value is generated in space according to the UE position in the previous state.

[0111] In step S402, based on the dynamic result and the initial positioning result, an updated positioning result is determined. Specifically, for time k=1, 2, 3, ..., the next moment position of the UE is calculated according to the dynamic equation in the combined positioning result obtained in step S3.

[0112] In step S403, based on the updated positioning result and the observation result, the weight of the updated positioning result is determined. Specifically, the geometric distance between each position and the measured position at time k = 1, 2, 3, ... is calculated. Substitute different distances into the right curve of the normal distribution to update the weight of each position, that is, as the distance increases, the weight decreases monotonically.

[0113]

[0114] Among them, σ 2 for The variance of , normalize the weights of the above formula to get:

[0115]

[0116] In step S404, the weight of the updated positioning result is adjusted to determine the feature value of the updated positioning result. Perform subsampling to generate subsampling update values The weight coefficient for each position is Calculate the filter value based on the sub-sampling position and sample variance (The eigenvalues ​​may include at least ):

[0117]

[0118] In step S405, when the characteristic value is less than the preset threshold, the final positioning result of the target to be positioned is determined. Specifically, if the sample variance If it is less than a certain limit, the filter is considered to have converged and the sample mean Can be output as positioning result.

[0119] So far, step S4 has been introduced. This step solves the problem of linearization error that cannot be ignored in the combined positioning results of Beidou + 5G through nonlinear processing, and improves the availability and accuracy of the positioning results.

[0120] Figure 5 is a schematic diagram of a positioning device according to an embodiment of the present disclosure. Figure 5 The positioning device 500 at least includes the following modules:

[0121] The signal acquisition module 501 is used to acquire a first ranging signal of a first positioning source and a second ranging signal of a second positioning source corresponding to a target to be positioned;

[0122] A noise determination module 502 is configured to determine an observation noise based on the first ranging signal, the second ranging signal and a preset random model, wherein the preset random model is determined based on at least a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise;

[0123] A positioning result module 503, used to determine a positioning combination result based on the velocity constraint and the observation noise;

[0124] The filtering processing module 504 is used to perform nonlinear processing on the positioning combination result to determine the final positioning result of the target to be positioned.

[0125] The noise determination module 502 may further include:

[0126] An analyzing unit 5021 is used to determine a first signal characteristic and a second signal characteristic based on the first observed noise and the historical second observed noise;

[0127] The determination unit 5022 is used to determine a first preset random result and a second preset random result based on the first signal characteristic and the second signal characteristic.

[0128] The positioning result module 503 may further include:

[0129] A speed constraint unit 5031, configured to determine a speed constraint based on a plurality of speed measurement models;

[0130] A dynamic result unit 5032, used to determine a dynamic result in a positioning combination result based on a speed constraint;

[0131] The observation result unit 5033 is used to determine the observation result in the positioning combination result based on the observation noise.

[0132] The filtering processing module 504 may further include:

[0133] The initial unit 5041 is used to obtain the initial positioning result of the target to be positioned;

[0134] An updating unit 5042, configured to determine an updated positioning result based on the dynamic result and the initial positioning result;

[0135] A weight unit 5043, used to determine the weight of the updated positioning result based on the updated positioning result and the observation result;

[0136] The calculation unit 5044 is used to adjust the weight of the updated positioning result and determine the characteristic value of the updated positioning result;

[0137] The positioning unit 5045 is used to determine the final positioning result of the target to be positioned when the characteristic value is less than a preset threshold.

[0138] Figure 6 1 is a hardware block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device according to an embodiment of the present disclosure includes at least a processor; and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor executes the positioning method as described above.

[0139] Figure 6 The electronic device 600 shown specifically includes: a central processing unit (CPU) 601, a graphics processing unit (GPU) 602 and a memory 603. These units are interconnected via a bus 604. The central processing unit (CPU) 601 and / or the graphics processing unit (GPU) 602 can be used as the above-mentioned processor, and the memory 603 can be used as the above-mentioned memory for storing computer-readable instructions. In addition, the electronic device 600 may also include a communication unit 605, a storage unit 606, an output unit 607, an input unit 608 and an external device 609, which are also connected to the bus 604.

[0140] Figure 7 is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. Figure 7 As shown, a computer program product 700 according to an embodiment of the present disclosure has a computer program 701 stored thereon. When the computer program 701 is executed by a processor, the positioning method described with reference to the above figures is executed. The computer program product includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0141] The positioning method, device, electronic device and computer program product according to the embodiments of the present disclosure are described above with reference to the accompanying drawings. According to the positioning method of the embodiments of the present disclosure, the random model is refined according to the characteristics of different positioning sources, continuous and smooth positioning of transition scenes is achieved, and linearization errors are weakened, thereby improving the availability, smoothness and accuracy of the positioning results.

[0142] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0143] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0144] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0145] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0146] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0147] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0148] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0149] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A positioning method, characterized in that: The method comprises: Acquire a first ranging signal from a first positioning source and a second ranging signal from a second positioning source corresponding to the target to be positioned; Determine observation noise based on the first ranging signal, the second ranging signal and a preset random model, wherein the preset random model is determined based on at least a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise; Determine a combined positioning result based on the velocity constraint and the observation noise; The positioning combination result is processed nonlinearly to determine the final positioning result of the target to be positioned.

2. The positioning method according to claim 1, characterized in that: The preset random model is determined based on at least a first signal characteristic of a first observation noise and a second signal characteristic of a second observation noise, and includes: determining the first signal characteristic and the second signal characteristic based on the first observation noise and the second observation noise; Based on the first signal characteristic and the second signal characteristic, a first preset random result and a second preset random result are determined.

3. The positioning method according to claim 1, characterized in that: Determining the combined positioning result based on the speed constraint and the observation noise includes: determining the speed constraint based on a plurality of speed measurement models; Based on the speed constraint, determining a dynamic result in the positioning combination result; Based on the observation noise, an observation result in the positioning combination result is determined.

4. The positioning method according to claim 1 or 3, characterized in that: The performing nonlinear processing on the positioning combination result to determine the final positioning result of the target to be positioned includes: Obtaining an initial positioning result of the target to be positioned; Determine an updated positioning result based on the dynamic result and the initial positioning result; Determining a weight of the updated positioning result based on the updated positioning result and the observation result; Adjusting the weight of the updated positioning result to determine a characteristic value of the updated positioning result; When the characteristic value is less than a preset threshold, a final positioning result of the target to be positioned is determined.

5. A positioning device, characterized in that: The device comprises: A signal acquisition module, used to acquire a first ranging signal of a first positioning source and a second ranging signal of a second positioning source corresponding to a target to be positioned; a noise determination module, configured to determine an observation noise based on the first ranging signal, the second ranging signal and a preset random model, wherein the preset random model is determined based on at least a first signal characteristic of the first observation noise and a second signal characteristic of the second observation noise; A positioning result module, used for determining a positioning combination result based on a velocity constraint and the observation noise; The filtering processing module is used to perform nonlinear processing on the positioning combination result to determine the final positioning result of the target to be positioned.

6. The positioning device according to claim 5, characterized in that: The noise determination module comprises: an analyzing unit, configured to determine the first signal characteristic and the second signal characteristic based on the first observation noise and the second observation noise; A determination unit is used to determine a first preset random result and a second preset random result based on the first signal characteristic and the second signal characteristic.

7. The positioning device according to claim 5, characterized in that: The positioning result module includes: A speed constraint unit, configured to determine the speed constraint based on a plurality of speed measurement models; A dynamic result unit, used for determining a dynamic result in the positioning combination result based on the speed constraint; An observation result unit is used to determine the observation result in the positioning combination result based on the observation noise.

8. The positioning device according to claim 5 or 7, characterized in that: The filtering processing module comprises: An initial unit, used to obtain an initial positioning result of the target to be positioned; An updating unit, configured to determine an updated positioning result based on a dynamic result and the initial positioning result; A weighting unit, configured to determine a weight of the updated positioning result based on the updated positioning result and the observation result; A calculation unit, used to adjust the weight of the updated positioning result to determine the characteristic value of the updated positioning result; The positioning unit is used to determine the final positioning result of the target to be positioned when the characteristic value is less than a preset threshold.

9. An electronic device, characterized in that: include: a memory for storing computer readable instructions; as well as A processor is used to run the computer-readable instructions so that the electronic device performs the positioning method as described in any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the positioning method according to any one of claims 1 to 4 is implemented.

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