Target object position information determination method and apparatus, network device, and storage medium

Abstract

Embodiments of the present application provide a target position information determination method and device, network equipment and a storage medium. The method comprises: performing frequency domain point division processing on a received orthogonal frequency division multiplexing echo signal reflected by a target at a first time to obtain an equivalent channel response matrix; resampling a time axis of the equivalent channel response matrix to obtain a first matrix; performing time reversal transformation on a first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix; determining a target matrix according to the first time signal sequence and the second sequence; the target matrix is used to represent a distance between the base station and the target; and determining position information of the target at the first time and a motion state parameter by using a multiple signal classification algorithm according to the target matrix. The present scheme can efficiently and accurately detect a high-speed moving target without high power consumption and high cost.

Description

Technical Field

[0001] This application relates to the field of cross-integration technology of mobile communication and radar detection, specifically to a method, apparatus, network device and storage medium for determining the location information of a target. Background Technology

[0002] In existing technologies, the detection of high-speed moving targets either involves introducing a generalized Radon Transform for acceleration grid search, which heavily relies on multi-dimensional parameter traversal, resulting in excessive computational and storage overhead; or it involves modifying the underlying communication waveform (e.g., introducing Orthogonal Time Frequency Space (OTFS) modulation) to adapt to high-speed scenarios, but this compromises the backward compatibility of existing Orthogonal Frequency Division Multiplexing (OFDM) networks and makes large-scale deployment in low-cost communication base stations (such as 5G / 6G digital baseband) difficult. Therefore, existing solutions have high power consumption and cost requirements, making low-cost resource reuse and pipelined deployment at the underlying level difficult, thus hindering efficient and accurate detection of high-speed moving targets. Summary of the Invention

[0003] At least one embodiment of this application provides a method, apparatus, network device, and storage medium for determining the location information of a target object, which solves the problem in the prior art that the detection of high-speed moving targets has high requirements for power consumption and cost, and it is difficult to carry out low-cost resource reuse and pipeline deployment at the underlying level, thus making it impossible to efficiently and accurately detect high-speed moving targets.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows:

[0005] In a first aspect, embodiments of this application provide a method for determining the location information of a target object, applied to a base station, including:

[0006] The orthogonal frequency division multiplexing echo signal reflected from the target object at the first moment is subjected to frequency domain point division processing to obtain an equivalent channel response matrix; the equivalent channel response matrix is ​​a two-dimensional matrix with respect to round-trip time and initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexing signal between the base station and the target object, and the initial distance is the distance between the target object and the base station determined according to the round-trip time;

[0007] The time axis of the equivalent channel response matrix is ​​resampled to obtain the first matrix;

[0008] A time-reversal transformation is performed on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix.

[0009] A target matrix is ​​determined based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object.

[0010] The target object's position information and motion parameters at the first time are determined using a multi-signal classification algorithm based on the target matrix.

[0011] Optionally, before performing frequency domain point division processing on the received orthogonal frequency division multiplexed echo signal reflected from the target object, the method further includes:

[0012] Orthogonal frequency division multiplexing signals are sent to the target object using a separate uniform planar array;

[0013] The received orthogonal frequency division multiplexed echo signal reflected from the target object is subjected to frequency domain point division processing to obtain the equivalent channel response matrix, including:

[0014] The frequency domain point division process is performed on the orthogonal frequency division multiplexing echo signal based on the complex modulation data of the orthogonal frequency division multiplexing signal to obtain the waveform characteristics of the orthogonal frequency division multiplexing echo signal;

[0015] Based on the waveform characteristics of the orthogonal frequency division multiplexing echo signal, the equivalent channel response matrix is ​​constructed by inverse fast Fourier transform.

[0016] Optionally, the time axis of the equivalent channel response matrix is ​​resampled to obtain a first matrix, including:

[0017] The resampling operator is coupled and mapped with the round-trip time to obtain the first sampling frequency;

[0018] The time axis of the equivalent channel response matrix is ​​resampled according to the first sampling frequency to obtain the first matrix.

[0019] Optionally, the location information includes the azimuth angle and elevation angle of the target object, as well as the distance between the target object and the base station;

[0020] The motion state parameters include the velocity and acceleration of the target object.

[0021] Optionally, the method further includes:

[0022] Obtain location information at multiple time points predicted by at least one of the base stations to obtain location information data;

[0023] Based on the location information data, the motion trend of the target object is predicted through a state-space model to obtain the trajectory prediction result of the target object; the state-space model includes a model part for predicting the motion state of the target object and a model part for calculating the observed values ​​of the motion state of the target object.

[0024] Optionally, before predicting the motion trend of the target object using a state-space model based on the location information data, the method further includes:

[0025] The accuracy of the location information at each time point in the location information data is determined based on the covariance matrix; the covariance matrix is ​​determined based on the covariance matrix of the predicted value of the motion state and the covariance matrix of the observed value.

[0026] Remove the target location information corresponding to the target time point from the location information data; the target location information is the location information whose accuracy is less than the judgment threshold.

[0027] Secondly, embodiments of this application provide a target location information determination device, applied to a base station, comprising:

[0028] The processing module is used to perform frequency domain point division processing on the orthogonal frequency division multiplexing echo signal reflected from the target object at the first time to obtain an equivalent channel response matrix; the equivalent channel response matrix is ​​a two-dimensional matrix with respect to round-trip time and initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexing signal between the base station and the target object, and the initial distance is the distance between the target object and the base station determined according to the round-trip time;

[0029] A resampling module is used to resample the time axis of the equivalent channel response matrix to obtain a first matrix;

[0030] The inversion module is used to perform a time-reversal transformation on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix;

[0031] A first determining module is configured to determine a target matrix based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object;

[0032] The second determining module is used to determine the position information and motion state parameters of the target object at the first time using a multi-signal classification algorithm based on the target matrix.

[0033] Thirdly, embodiments of this application provide a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the target location information determination method as described above.

[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the target location information determination method as described above.

[0035] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the target location information determination method as described above.

[0036] Compared with the prior art, the target location information determination method, apparatus, network device and storage medium provided in this application can strip random communication modulation symbols and accurately extract the equivalent channel phase history containing subcarrier frequency offset by performing frequency domain point division processing on the orthogonal frequency division multiplexing echo signal; by performing resampling and time reversal transformation cascade compensation on the time axis of the equivalent channel response matrix, it can eliminate the high signal-to-noise ratio of time-frequency coupling, provide a stable signal subspace for multiple signal classification algorithms, and finally achieve high-speed target energy physical-level focusing and high-precision dimensionality reduction parameter estimation without complex high-dimensional parameter search. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a schematic diagram illustrating an application scenario according to an embodiment of this application;

[0039] Figure 2 This is a flowchart illustrating the method for determining the location information of a target object according to an embodiment of this application.

[0040] Figure 3 This is a schematic diagram of the integrated communication and sensing system according to an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the structure of a multi-base station joint sensing system according to an embodiment of this application;

[0042] Figure 5 This is a schematic diagram illustrating the relationship between distance and time without processing, as described in an embodiment of this application.

[0043] Figure 6 This is a schematic diagram illustrating the relationship between distance and speed (time) after KT conversion in an embodiment of this application.

[0044] Figure 7 This is a schematic diagram illustrating the relationship between distance and speed (time) after cascade compensation using the KT-MTRT in an embodiment of this application.

[0045] Figure 8 This is a schematic diagram of the target location information determination device according to an embodiment of this application;

[0046] Figure 9 This is a schematic diagram of the structure of a network device according to an embodiment of this application. Detailed Implementation

[0047] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, "A or B" covers three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0048] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc.; an indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0049] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used in the systems and radio technologies mentioned above, as well as in other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th Generation (6G) communication systems.

[0050] Figure 1This diagram illustrates a block diagram of a wireless communication system applicable to embodiments of this application. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 can be a mobile phone, tablet computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR), virtual reality (VR) device, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipboard equipment, pedestrian user equipment (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. It should be noted that the specific type of terminal 11 is not limited in this application embodiment. Network device 12 may include access network devices or core network devices, wherein access network devices may also be referred to as Radio Access Network (RAN) devices, radio access network functions, or radio access network units. Access network devices may include base stations, Wireless Local Area Network (WLAN) access points (APs), or Wireless Fidelity (WiFi) nodes, etc.In this context, a base station may be referred to as a Node B (NB), an Evolved Node B (eNB), a Next Generation Node B (gNB), a New Radio Node B (NR Node B), an Access Point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a Radio Base Station, a Radio Transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a Home Node B (HNB), a Home Evolved Node B, a Transmission Reception Point (TRP), or any other suitable term in the relevant field, as long as the same technical effect is achieved. The base station is not limited to any specific technical terminology. It should be noted that in this application embodiment, only a base station in an NR system is used as an example for introduction, and the specific type of base station is not limited.

[0051] Core network equipment may include, but is not limited to, at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), Binding Support Function (BSF), and Application Function. Function (AF), etc. It should be noted that the embodiments of this application only use the core network equipment in the NR system as an example for introduction, and do not limit the specific type of core network equipment.

[0052] To enable those skilled in the art to better understand the embodiments of this application, the following description is provided first:

[0053] This application addresses the high-speed mobile UAV detection scenario in 6G integrated sensing networks. It reuses existing communication base stations and OFDM systems to solve problems such as energy diffusion, decreased parameter estimation accuracy, and flickering and disconnection during continuous tracking in high-speed target detection under complex low-altitude and low signal-to-noise ratio conditions (e.g., SNR≈-10dB). It proposes a comprehensive high-speed target detection and adaptive tracking method suitable for sensing OFDM signals, namely the target location information determination method of this application.

[0054] High-speed target motion causes cross-range migration (CMD) and Doppler migration / expansion, resulting in the diffusion of target energy in the "range-slow time / Doppler" dimension, which severely degrades the performance of conventional coherent accumulation and direct super-resolution processing, and causes the problem of instantaneous defocusing.

[0055] Existing multi-signal classification (MUSIC) target detection methods for sensory radar do not compensate for time-frequency coupling caused by high-speed motion, resulting in signal subspace rank deficiency, spectral peak broadening, resolution reduction, and increased root mean square error (RMSE) of multidimensional parameter estimation, i.e., the problem of subspace rank deficiency.

[0056] Within the OFDM sensing framework, how can we achieve range travel and Doppler spread correction for high-speed uniformly accelerated radial targets without stripping phase interference from random communication symbols or performing high-dimensional parameter searches, and further obtain high-precision estimated parameters?

[0057] In response to strong ground clutter and target scintillation in complex low-altitude environments, how to overcome the limitations of fixed empirical errors in traditional filtering after obtaining discrete parameters, adaptively identify and softly isolate false intersection points and track deviations, and achieve anti-scintillation continuous tracking, i.e., the problem of track scintillation and false alarm isolation;

[0058] Existing complex high-speed target compensation algorithms (such as generalized Radon transform search) rely on massive storage for full-matrix cross-clock domain caching, which makes them unsuitable for low-cost reuse and large-scale deployment in cost- and power-sensitive 5G / 6G digital baseband processing units, i.e., the hardware cost and power consumption bottleneck.

[0059] To address this, this invention, based on decoupling of the frequency domain points of the inductive OFDM signal, introduces Keystone Transform (KT) and an improved Time Reversal Transform (TRT) to perform algebraic analytical compensation for the motion effects of high-speed targets. Then, the MUSIC super-resolution algorithm is used to achieve precise estimation of multidimensional parameters in the high signal-to-noise ratio stationary subspace of the compensated output. Subsequently, a tracking model based on adaptive decision-making using innovation statistical features outputs a smooth trajectory, and its overall computational architecture is mapped to a low-memory-overhead hardware device based on address reversal read, pipelined multipliers, and Coordinate Rotation Digital Computer (CORDIC) operators.

[0060] As described in the background section, in the prior art, the detection of high-speed moving targets has high requirements for power consumption and cost, and it is difficult to carry out low-cost resource reuse and pipeline deployment at the underlying level. As a result, it is impossible to efficiently and accurately detect high-speed moving targets. In order to solve the above problems, the embodiments of this application provide a method for determining the position information of a target object, which can reduce or avoid the occurrence of the above situations and improve the efficiency and accuracy of target object detection.

[0061] This application provides a method and apparatus for determining the location information of a target object. The method and apparatus are based on the same concept, and since the principles by which they solve the problem are similar, their implementations can be referred to interchangeably; repeated details will not be repeated.

[0062] like Figure 2 As shown in the embodiment of this application, a method for determining the location information of a target object, when applied to a base station, includes the following steps:

[0063] Step 201: Perform frequency domain point division processing on the orthogonal frequency division multiplexing echo signal reflected from the target object at the first time to obtain an equivalent channel response matrix; the equivalent channel response matrix is ​​a two-dimensional matrix with respect to round-trip time and initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexing signal between the base station and the target object, and the initial distance is the distance between the target object and the base station determined based on the round-trip time;

[0064] Step 202: Resample the time axis of the equivalent channel response matrix to obtain the first matrix;

[0065] Step 203: Perform a time-reversal transformation on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix.

[0066] Step 204: Determine a target matrix based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object;

[0067] Step 205: Based on the target matrix, a multi-signal classification algorithm is used to determine the position information and motion state parameters of the target object at the first time.

[0068] The target location information determination method of this application embodiment can strip random communication modulation symbols and accurately extract the equivalent channel phase history containing subcarrier frequency offset by performing frequency domain point division processing on the orthogonal frequency division multiplexing echo signal; by performing resampling and time reversal transformation cascade compensation on the time axis of the equivalent channel response matrix, it can eliminate the high signal-to-noise ratio of time-frequency coupling, provide a stable signal subspace for multiple signal classification algorithms, and finally achieve high-speed target energy physical-level focusing and high-precision dimensionality reduction parameter estimation without complex high-dimensional parameter search.

[0069] The solution in this application is as follows: Figure 3 The integrated sensing and communications (ISAC) sensing system shown, or as... Figure 4 The image shows a downlink sensing integration scenario of a multi-base station joint sensing system.

[0070] Optionally, before performing frequency domain point division processing on the received orthogonal frequency division multiplexed echo signal reflected from the target object, the method further includes:

[0071] Orthogonal frequency division multiplexing signals are sent to the target object using a separate uniform planar array;

[0072] The received orthogonal frequency division multiplexed echo signal reflected from the target object is subjected to frequency domain point division processing to obtain the equivalent channel response matrix, including:

[0073] The frequency domain point division process is performed on the orthogonal frequency division multiplexing echo signal based on the complex modulation data of the orthogonal frequency division multiplexing signal to obtain the waveform characteristics of the orthogonal frequency division multiplexing echo signal;

[0074] Based on the waveform characteristics of the orthogonal frequency division multiplexing echo signal, the equivalent channel response matrix is ​​constructed by inverse fast Fourier transform.

[0075] In this embodiment of the application, the base station adopts a separate transmit / receive uniform planar array to detect mobile drones in three-dimensional space by transmitting OFDM signals;

[0076] Specifically, the OFDM signal is represented in the time domain as:

[0077] ;

[0078] Among them, the transmitted signal Represents the continuous-time OFDM baseband signal emitted by the base station;

[0079] In the formula, and These are the number of symbols and the total number of subcarriers, respectively.

[0080] It is carried in the first The symbol, the first Complex modulation data on each subcarrier;

[0081] The center frequency of the carrier. Subcarrier spacing;

[0082] This is the total duration of the symbol, including the cyclic prefix CP.

[0083] A rectangular window function that defines the duration of the signal;

[0084] In echo processing, This represents the equivalent channel response after decoupling. It is the first The round-trip time corresponding to each symbol is determined by the initial distance of the target. radial velocity and the speed of electromagnetic wave propagation Joint decision, It represents the imaginary unit.

[0085] The echo signal experiences time delay and Doppler shift after being reflected by the UAV. To eliminate random communication data... To mitigate phase interference, the system performs frequency domain division (decoupling) on ​​the received echo and transmitted data to obtain the... The symbol, the first Equivalent channel response of each subcarrier:

[0086] ;

[0087] in, For amplitude attenuation, It is Gaussian white noise; For the first The round-trip time corresponding to each symbol, including the initial distance and radial velocity .

[0088] Specifically, the frequency domain point division processing of the orthogonal frequency division multiplexed echo signal is performed based on the complex modulation data of the orthogonal frequency division multiplexed signal, including:

[0089] The frequency domain point division process is performed by dividing the orthogonal frequency division multiplexing echo signal by the complex modulation data.

[0090] For example, using OFDM complex modulation data known at the transmitter. For the received echo signal Perform frequency domain point division (decoupling):

[0091] .

[0092] Optionally, the equivalent channel response matrix is ​​a two-dimensional matrix of distance and slow time.

[0093] Here, Fast Time is the sampling time t or delay index within a single pulse, corresponding to the distance information (Range).

[0094] The slow time is the time sequence τ or frame index k between adjacent pulses, corresponding to Doppler / velocity information.

[0095] The target location information determination method of this application embodiment can strip away the random communication modulation phase and extract a pure equivalent channel response matrix by performing frequency domain point division processing on the orthogonal frequency division multiplexing echo signal, ensuring that the sampling factor is physically matched with the OFDM subcarrier when the equivalent channel response moment is resampled subsequently.

[0096] Optionally, the time axis of the equivalent channel response matrix is ​​resampled to obtain a first matrix, including:

[0097] The resampling operator is coupled and mapped with the round-trip time to obtain the first sampling frequency;

[0098] The time axis of the equivalent channel response matrix is ​​resampled according to the first sampling frequency to obtain the first matrix.

[0099] In this embodiment of the application, for the distance travel caused by high-speed motion, nonlinear slow-time resampling is introduced in the decoupling process of this application;

[0100] Specifically, the goal is in the The distance and round-trip time for each symbol are:

[0101] ;

[0102] ;

[0103] Specifically, this application utilizes the Keystone transform to achieve resampling of the time axis in slow time;

[0104] Its core lies in the resampling operator and the subcarrier frequency offset unique to OFDM. (Right now Deep binding:

[0105] ;

[0106] In the Keystone transform formula, the resampling operator will operate at the frequency... (in, The offset of the subcarrier relative to the carrier frequency. ) and the original time delay history Perform coupling mapping to obtain a new slow time scale. The sampling interval is dynamically adjusted based on the physical scale differences of each subcarrier, thereby eliminating the phenomenon of echo envelope moving across distance cells.

[0107] like Figure 5 Unprocessed target echoes exhibit severe range migration and defocusing; however, after Keystone transformation, such as Figure 6 As shown, the target energy was successfully aligned in the distance dimension, eliminating tilting and movement, but due to the presence of acceleration, it still appears out of focus in the Doppler dimension.

[0108] The target position information determination method of this application embodiment, after correcting for distance movement, performs cascaded MTRT analytical compensation for the secondary phase distortion caused by acceleration, specifically:

[0109] The first time signal sequence is obtained by sampling the time axis of the first matrix. :

[0110] ;

[0111] In the formula, This represents a discrete slow-time signal sequence (the first time signal sequence) that still contains residual acceleration phase after Keystone transform correction (the resampling).

[0112] For discrete slow-time variables, the processing time corresponds to the OFDM symbol dimension;

[0113] and These represent the Doppler frequency shift caused by the radial motion of the target and the Doppler drift rate (i.e., the second phase coefficient) caused by radial acceleration, respectively.

[0114] The second sequence is obtained by using the inversion transformation through the branches of "slow time series inversion" and "conjugate complex multiplication cancellation":

[0115] ;

[0116] Among them, superscript Indicates conjugate operation;

[0117] Multiplying the first time signal sequence by the second sequence yields the MTRT result (the target matrix):

[0118] .

[0119] The target location information determination method in this application, through cascade compensation of KT-MTRT, eliminates Doppler spread, such as... Figure 7 As shown, energy defocusing caused by acceleration is completely eliminated (comparison). Figure 6 This method perfectly reconstructs the target echo into a sharp three-dimensional impulse spectrum peak. Because this scheme avoids high-dimensional search, its single-frame processing time is significantly lower than traditional search-based algorithms.

[0120] Optionally, the location information includes the azimuth angle and elevation angle of the target object, as well as the distance between the target object and the base station;

[0121] The motion state parameters include the velocity and acceleration of the target object.

[0122] The target location information determination method of this application embodiment obtains a high signal-to-noise ratio stable signal subspace after KT-MTRT cascade compensation. Based on this, the location information and motion state information are extracted by the MUSIC algorithm.

[0123] In this embodiment of the application, the ranging and velocity spectrum function of the MUSIC algorithm is:

[0124] ;

[0125] ;

[0126] in, and The pseudospectral representation of distance and velocity output;

[0127] The dimension (or observation dimension) represents the signal subspace or noise subspace.

[0128] and These are the conjugate transposes of the guiding vectors (guide vectors) for the corresponding search dimensions;

[0129] It is the first of the noise subspaces Each eigenvector represents a spectral function peak, which corresponds to a high-precision estimate of the target parameters.

[0130] Optionally, the method further includes:

[0131] Obtain location information at multiple time points predicted by at least one of the base stations to obtain location information data;

[0132] Based on the location information data, the motion trend of the target object is predicted through a state-space model to obtain the trajectory prediction result of the target object; the state-space model includes a first model part that predicts the motion state of the target object and a second model part that calculates the observed values ​​of the motion state of the target object.

[0133] In this embodiment of the application, the first model part uses a constant velocity (CV) model to describe the target state:

[0134] ;

[0135] The second model part is:

[0136] ;

[0137] in, Representing the The target motion state vector at any given time;

[0138] This is the state transition matrix determined based on the constant velocity model;

[0139] This is process noise;

[0140] The observation vectors extracted from each base station;

[0141] For the observation matrix, The covariance matrix of the observation noise is: .

[0142] For the first Predicting based on the motion state and position information at each moment:

[0143] ;

[0144] In the state prediction formula, Indicates the first The state prior prediction at time step 1 is the optimal estimate based on the previous time step before obtaining the current observation. Combined with the state transition matrix The target position and velocity are pre-deduced, and the prediction error covariance is calculated simultaneously. This predicted value serves as a benchmark for assessing whether subsequent observation data exhibits anomalous jumps.

[0145] Optionally, before predicting the motion trend of the target object using a state-space model based on the location information data, the method further includes:

[0146] The accuracy of the location information at each time point in the location information data is determined based on the covariance matrix; the covariance matrix is ​​determined based on the covariance matrix of the predicted value of the motion state and the covariance matrix of the observed value.

[0147] Remove the target location information corresponding to the target time point from the location information data; the target location information is the location information whose accuracy is less than the judgment threshold.

[0148] In this embodiment of the application, the location information at each time point is determined by the following criteria:

[0149] ;

[0150] in, For the predicted first The deviation of the actual observed values ​​of the location information at each time point;

[0151] No. The covariance matrix of the location information at each time point is composed of the superposition of the system's prediction error covariance. The composition is used to measure the reliability of the current measurement;

[0152] superscript and These represent the transpose and inversion operations of a matrix, respectively.

[0153] This is a pre-defined statistical decision threshold (usually following a chi-square distribution).

[0154] When the calculated value exceeds this threshold, it is determined that the current observation point has experienced a non-physical jump due to multipath interference or RCS flicker. At this point, the system adaptively expands the observation noise covariance matrix. The weight of the measurement is increased to achieve soft isolation of abnormal transition points by increasing the statistical penalty term of the measurement in that frame.

[0155] The target location information determination method of this application embodiment, through precise motion compensation at the front end and adaptive smoothing at the back end, successfully filters out multipath false alarm interference and outputs a smooth and continuous trajectory prediction result.

[0156] The various methods of the embodiments of this application have been described above. Apparatus for implementing the above methods will now be provided.

[0157] like Figure 8 As shown in the illustration, this application also provides a target location information determination device 800, applied to a base station, comprising:

[0158] Processing module 801 is used to perform frequency domain point division processing on the orthogonal frequency division multiplexing echo signal reflected from the target object at the first time to obtain an equivalent channel response matrix; the equivalent channel response matrix is ​​a two-dimensional matrix with respect to round-trip time and initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexing signal between the base station and the target object, and the initial distance is the distance between the target object and the base station determined according to the round-trip time;

[0159] The resampling module 802 is used to resample the time axis of the equivalent channel response matrix to obtain a first matrix.

[0160] The inversion module 803 is used to perform a time inversion transformation on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix;

[0161] The first determining module 804 is used to determine a target matrix based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object;

[0162] The second determining module 805 is used to determine the position information and motion state parameters of the target object at the first time using a multi-signal classification algorithm based on the target matrix.

[0163] The target location information determination device of this application embodiment can strip random communication modulation symbols and accurately extract the equivalent channel phase history containing subcarrier frequency offset by performing frequency domain point division processing on the orthogonal frequency division multiplexing echo signal; by performing resampling and time reversal transformation cascade compensation on the time axis of the equivalent channel response matrix, it can eliminate the high signal-to-noise ratio of time-frequency coupling, provide a stable signal subspace for multiple signal classification algorithms, and finally achieve high-speed target energy physical-level focusing and high-precision dimensionality reduction parameter estimation without complex high-dimensional parameter search.

[0164] Another embodiment of the network device in this application, such as Figure 9 As shown, it includes a transceiver 910, a processor 900, a memory 920, and a program or instructions stored in the memory 920 and executable on the processor 900; when the processor 900 executes the program or instructions, it implements the various processes of the above-described target location information determination method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0165] The transceiver 910 is used to receive and send data under the control of the processor 900.

[0166] Among them, Figure 9In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 900) and memory (memory 920). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 910 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 900 is responsible for managing the bus architecture and general processing, and the memory 920 can store data used by the processor 900 during operation.

[0167] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described target location information determination method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0168] This application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the various processes of the above-described target location information determination method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0169] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0170] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0171] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for determining the location information of a target object, applied to a base station, characterized in that, include: The orthogonal frequency division multiplexing echo signal reflected from the target object at the first moment is subjected to frequency domain point division processing to obtain the equivalent channel response matrix; The equivalent channel response matrix is ​​a two-dimensional matrix relating to the round-trip time and the initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexed signal between the base station and the target, and the initial distance is the distance between the target and the base station determined based on the round-trip time; The time axis of the equivalent channel response matrix is ​​resampled to obtain the first matrix; A time-reversal transformation is performed on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix. A target matrix is ​​determined based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object. The target object's position information and motion parameters at the first time are determined using a multi-signal classification algorithm based on the target matrix.

2. The method according to claim 1, characterized in that, Before performing frequency domain point division processing on the received orthogonal frequency division multiplexed echo signal reflected from the target object, the method further includes: Orthogonal frequency division multiplexing signals are sent to the target object using a separate uniform planar array; The received orthogonal frequency division multiplexed echo signal reflected from the target object is subjected to frequency domain point division processing to obtain the equivalent channel response matrix, including: The frequency domain point division process is performed on the orthogonal frequency division multiplexing echo signal based on the complex modulation data of the orthogonal frequency division multiplexing signal to obtain the waveform characteristics of the orthogonal frequency division multiplexing echo signal; Based on the waveform characteristics of the orthogonal frequency division multiplexing echo signal, the equivalent channel response matrix is ​​constructed by inverse fast Fourier transform.

3. The method according to claim 1, characterized in that, The time axis of the equivalent channel response matrix is ​​resampled to obtain a first matrix, which includes: The resampling operator is coupled and mapped with the round-trip time to obtain the first sampling frequency; The time axis of the equivalent channel response matrix is ​​resampled according to the first sampling frequency to obtain the first matrix.

4. The method according to claim 1, characterized in that, The location information includes the azimuth and elevation angles of the target object, as well as the distance between the target object and the base station; The motion state parameters include the velocity and acceleration of the target object.

5. The method according to claim 1, characterized in that, The method further includes: Obtain location information at multiple time points predicted by at least one of the base stations to obtain location information data; Based on the location information data, the motion trend of the target object is predicted through a state-space model to obtain the trajectory prediction result of the target object; the state-space model includes a model part for predicting the motion state of the target object and a model part for calculating the observed values ​​of the motion state of the target object.

6. The method according to claim 5, characterized in that, Before predicting the motion trend of the target object using a state-space model based on the location information data, the method further includes: The accuracy of the location information at each time point in the location information data is determined based on the covariance matrix; the covariance matrix is ​​determined based on the covariance matrix of the predicted value of the motion state and the covariance matrix of the observed value. Remove the target location information corresponding to the target time point from the location information data; the target location information is the location information whose accuracy is less than the judgment threshold.

7. A target location information determination device, applied to a base station, characterized in that, include: The processing module is used to perform frequency domain point division processing on the orthogonal frequency division multiplexing echo signal reflected from the target object in the first time to obtain the equivalent channel response matrix; The equivalent channel response matrix is ​​a two-dimensional matrix relating to the round-trip time and the initial distance; the round-trip time is the round-trip time of the orthogonal frequency division multiplexed signal between the base station and the target, and the initial distance is the distance between the target and the base station determined based on the round-trip time; A resampling module is used to resample the time axis of the equivalent channel response matrix to obtain a first matrix; The inversion module is used to perform a time-reversal transformation on the first time signal sequence to obtain a second sequence; the first time signal sequence is obtained by sampling the time axis of the first matrix; A first determining module is configured to determine a target matrix based on the first time signal sequence and the second sequence; the target matrix is ​​used to characterize the distance between the base station and the target object; The second determining module is used to determine the position information and motion state parameters of the target object at the first time using a multi-signal classification algorithm based on the target matrix.

8. A network device, characterized in that, include: Transceiver, processor, memory, and programs or instructions stored in the memory and executable on the processor; When the processor executes the program or instructions, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

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