Single-point near-field joint localization and velocity estimation method based on ultra-large scale array

Abstract

The application provides a single-point near-field joint positioning and velocity estimation method based on a super-large array, and relates to the technical field of wireless communication. The method comprises the following steps: dividing a transmitting array and a receiving array of an XL-MIMO system of a perception node respectively, and obtaining a plurality of transmitting-receiving subarray pairs; in a perception beam scanning mode, processing a received signal of a target reflection by using a segmented far-field model to obtain a processed received signal, the target being located in a far field of each transmitting-receiving subarray, and the target being located in a near field of the transmitting array and the receiving array; obtaining a spatial characteristic vector and a time characteristic vector of the target according to the processed received signal; obtaining position information of the target based on a spatial sparse representation model and the spatial characteristic vector; and obtaining velocity information of the target based on a velocity sparse representation model, the time characteristic vector and the position information of the target. The scheme of the application significantly reduces the position and velocity estimation complexity of a near-field target.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, specifically to a single-point near-field joint localization and velocity estimation method based on a very large-scale array. Background Technology

[0002] XL-MIMO (Extremely Large-scale Multiple-input and Multiple-Output) systems refer to communication systems equipped with a massive number of antenna elements, typically hundreds, capable of forming high-resolution spatial beams, improving communication capacity, and achieving precise sensing. Near-field conditions refer to the situation where, when a target is within the Rayleigh distance of the antenna array, the wavefront of the electromagnetic wave exhibits spherical wave characteristics, resulting in a nonlinear relationship between the signal phase and the antenna position. Joint localization and velocity estimation refers to simultaneously acquiring the target's position and velocity by transmitting a signal through a single sensing node and receiving the signal reflected back from the target, using signal processing techniques. Position typically includes the target's coordinates in two-dimensional space; velocity refers to the target's speed in two-dimensional space, including its direction and magnitude, i.e., the vector characteristics of the target's motion in space. Therefore, under near-field conditions, due to the spherical characteristics of the electromagnetic wave front, the target's position and velocity are reflected in the received signal phase in a nonlinear and complex coupled manner, leading to severe coupling between the near-field target's position and velocity, resulting in high signal processing complexity and making position and velocity estimation extremely complex. Summary of the Invention

[0003] At least one embodiment of this application provides a single-point near-field joint localization and velocity estimation method based on a very large-scale array, in order to solve the problem that the position and velocity estimation of near-field targets is extremely complex in the prior art.

[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 single-point near-field joint localization and velocity estimation method based on a very large-scale array, including:

[0006] The transmit array and receive array of the ultra-large-scale multiple input-output (XL-MIMO) system of the sensing node are divided to obtain multiple transmit-receive subarray pairs, each of which includes a transmit subarray and a receive subarray.

[0007] In the sensing beam scanning mode, a segmented far-field model is used to process the received signal reflected by the target to obtain the processed received signal. The target is located in the far field of each of the transmit-receive subarrays, and the target is located in the near field of the transmit array and the receive array.

[0008] Based on the processed received signal, the spatial feature vector and the temporal feature vector of the target are obtained. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor.

[0009] The location information of the target is obtained based on the spatial sparse representation model and the spatial feature vector;

[0010] The velocity information of the target is obtained based on the velocity sparse representation model, the time feature vector, and the position information of the target.

[0011] Optionally, in the single-point near-field joint localization and velocity estimation method based on a very large-scale array, the segmented far-field model is obtained based on the transmit power, the antenna gain of the transmit array, the antenna gain of the receive array, the complex reflection coefficient of the target, the time-varying propagation distance from the transmit subarray to the target, the time-varying propagation distance from the target to the receive subarray, the time delay from the transmit subarray to the target, the time delay from the target to the receive subarray, the angle between the target and the transmit subarray, the angle between the target and the receive subarray, and additive Gaussian noise.

[0012] Optionally, the single-point near-field joint localization and velocity estimation method based on a very large-scale array, wherein obtaining the spatial feature matrix and temporal feature matrix of the target based on the processed received signal includes:

[0013] Perform matched filtering on the processed received signal to obtain the peak value after matched filtering;

[0014] Based on the autocorrelation function and the bibasal Doppler frequency shift associated with the transmit-receive subarray pair, the peak value after matched filtering is simplified to obtain the observation data matrix of the target;

[0015] Singular value decomposition is performed on the observation data matrix of the target to obtain the spatial feature vector and temporal feature vector of the target.

[0016] Optionally, the single-point near-field joint localization and velocity estimation method based on ultra-large-scale arrays further includes:

[0017] The region of interest to which the target belongs is discretized to obtain multiple discrete grid points at different locations;

[0018] For each of the transmit-receive subarray pairs, an oversampling dictionary matrix is ​​constructed based on the multiple discrete grid points at the locations;

[0019] The spatially sparse representation model is constructed based on the oversampling dictionary matrix and the complex gain of the received signal.

[0020] Optionally, the single-point near-field joint localization and velocity estimation method based on ultra-large-scale arrays further includes:

[0021] The velocity range to which the target belongs is discretized to obtain multiple discrete velocity grid points;

[0022] Based on the target's position information and multiple velocity discrete grid points, an overcomplete Doppler steering matrix is ​​constructed;

[0023] Based on the overcomplete Doppler steering matrix, the velocity sparse representation model is constructed.

[0024] Optionally, the single-point near-field joint localization and velocity estimation method based on a very large-scale array, wherein obtaining the target's position information based on a spatial sparse representation model and the spatial feature vector includes:

[0025] Construct a dimensionality-reduced vector based on the aforementioned spatial feature vectors;

[0026] The first position information of the target is obtained by utilizing the spatial sparse representation model, the dimensionality reduction vector, the joint sparsity between different transmit-receive subarray pairs, and the mixture norm minimization method.

[0027] The second location information of the target is obtained by using the first location information of the target, the network-free gradient descent optimization method, and the maximum likelihood criterion.

[0028] Optionally, the single-point near-field joint localization and velocity estimation method based on a very large-scale array, wherein obtaining the velocity information of the target based on the velocity sparse representation model, the time feature vector, and the target's position information includes:

[0029] Based on the aforementioned time feature vector, the noise subspace is obtained;

[0030] The velocity information of the target is obtained based on the velocity sparse representation model, the target's position information, and the noise subspace.

[0031] Secondly, embodiments of this application also provide a single-point near-field joint localization and velocity estimation device based on a very large-scale array, comprising:

[0032] The first acquisition module is used to divide the transmit array and receive array of the ultra-large-scale multiple input-output (XL-MIMO) system of the sensing node to obtain multiple transmit-receive subarray pairs, each of which includes a transmit subarray and a receive subarray.

[0033] The second acquisition module is used to process the received signal reflected by the target using a segmented far-field model in the sensing beam scanning mode to obtain the processed received signal, wherein the target is located in the far field of each of the transmit-receive subarrays and the target is located in the near field of the transmit array and the receive array.

[0034] The third acquisition module is used to obtain the spatial feature vector and temporal feature vector of the target based on the processed received signal. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor.

[0035] The fourth acquisition module is used to obtain the location information of the target based on the spatial sparse representation model and the spatial feature vector;

[0036] The fifth acquisition module is used to obtain the velocity information of the target based on the velocity sparse representation model, the time feature vector, and the position information of the target.

[0037] Thirdly, embodiments of this application provide a single-point near-field joint localization and velocity estimation device based on a very large-scale array, comprising: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in the first aspect.

[0038] Fourthly, embodiments of this application provide a readable storage medium storing a program that, when executed by a processor, implements the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in the first aspect.

[0039] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which, when executed by a processor, implement the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in the first aspect.

[0040] Compared with existing technologies, this application provides a single-point near-field joint localization and velocity estimation method based on a very large-scale array (XL-MIMO). The method includes: dividing the transmit and receive arrays of the XL-MIMO system of a sensing node to obtain multiple transmit-receive subarray pairs, each including one transmit subarray and one receive subarray; processing the received signal reflected from the target using a segmented far-field model in sensing beam scanning mode to obtain a processed received signal, wherein the target is located in the far field of each transmit-receive subarray and in the near field of both the transmit and receive arrays; obtaining the target's spatial feature vector and temporal feature vector based on the processed received signal, wherein the spatial feature vector includes an angle factor and the temporal feature vector includes a Doppler factor; obtaining the target's position information based on a spatial sparse representation model and the spatial feature vector; and obtaining the target's velocity information based on a velocity sparse representation model, the temporal feature vector, and the target's position information. This effectively decouples the target's position and velocity, reduces signal processing complexity, and solves the problem of extremely complex position and velocity estimation. Attached Figure Description

[0041] 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:

[0042] Figure 1 This is a flowchart illustrating the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in the embodiments of this application.

[0043] Figure 2 This is a schematic diagram of a perception scenario for the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in the embodiments of this application;

[0044] Figure 3 This is a schematic diagram of the single-point near-field joint localization and velocity estimation system based on a very large-scale array as described in the embodiments of this application;

[0045] Figure 4 This is a schematic diagram of the single-point near-field joint localization and velocity estimation device based on a very large-scale array as described in the embodiments of this application;

[0046] Figure 5 This is a hardware block diagram of the single-point near-field joint localization and velocity estimation device based on a very large-scale array as described in the embodiments 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] Please refer to Figure 1 This application provides a single-point near-field joint localization and velocity estimation method based on a very large-scale array, including:

[0049] Step 101: Divide the transmit array and receive array of the XL-MIMO system of the sensing node to obtain multiple transmit-receive subarray pairs, each of which includes a transmit subarray and a receive subarray.

[0050] This application embodiment focuses on a single sensing node. Here, the sensing node can be a static sensing node, such as a base station, which performs target sensing in a complex near-field environment. Figure 2 This is a schematic diagram of a perception scenario for the single-point near-field joint localization and velocity estimation method based on a very large-scale array described in an embodiment of this application. Figure 2 As shown, the sensing node emits a sensing signal, which directly reaches the target and is reflected back by the target, forming a LoS (Line of Sight) connection.

[0051] Here, for Figure 2 The parameters of the perceived scene are explained as follows:

[0052] The sensing nodes are equipped with ultra-large-scale transmit and receive ULAs (Uniform Linear Arrays), symmetrically deployed along the x-axis, with the center as the origin, and widely spatially spaced to suppress transmitter leakage and receive clear sensing echoes reflected from the target. The total number of antenna elements in the transmit and receive arrays are N. t and N r The element spacing is half a wavelength. The distance between the transmitting array and the receiving array is D0. A target is located in the near field of the base station. The initial position of the target can be represented as two-dimensional coordinates p0 = (x0, y0), and its velocity can be represented as a two-dimensional vector v = (v0, y ... x ,v y ).

[0053] The transmitter employs a partially connected hybrid architecture, in which the transmission array is divided into K... t The receiver employs a fully digital architecture to enhance sensing performance, and the receiver array is also divided into K non-overlapping transmitter subarrays, each connected to a single RF (Radio Frequency) signal chain. r Each subarray has a receiver subarray. The small size of the subarrays results in a Rayleigh distance of only a few meters, allowing the target to typically be located in the far field of each transmitter and receiver subarray, while remaining in the near field of the entire transmitter and receiver arrays. The first antenna of each transmitter and receiver subarray is selected as a reference antenna to represent the position of the transmitter and receiver subarrays.

[0054] The position of the i-th transmitting subarray can be represented as:

[0055] The position of the j-th receiving subarray can be represented as:

[0056] Step 102: In the sensing beam scanning mode, for each of the transmit-receive subarray pairs, the received signal reflected by the target is processed using a segmented far-field model to obtain the processed received signal. The target is located in the far field of each of the transmit-receive subarrays and in the near field of the transmit array and the receive array.

[0057] In this embodiment of the application, in the sensing beam scanning mode, the transmitter of the sensing node generates a sensing beam to explore the entire region of interest within a certain time period and scanning interval. The region of interest is represented by the following formula (1):

[0058] P={(x,y)|x min ≤x≤x max ,y min ≤y≤y max}(1)

[0059] Simultaneously, targets within the region of interest can be detected, thereby allowing estimation of their position and velocity information. This application's embodiments achieve high-precision sensing based on a single received signal from each transmit-receive subarray pair, independent of multiple consecutive frames of data, making it suitable for scenarios requiring rapid initial target detection.

[0060] Since the sensing node is a stationary base station, assuming the sensing node is located at (0,0), each transmitting subarray transmits mutually orthogonal waveforms. The transmitted signal of the i-th transmitting subarray is represented by the following formula (2):

[0061] x i (t)=f is i (t)(2)

[0062] in, s is the beamforming vector of the i-th transmitting subarray pointing towards the target; i (t) is an orthogonal waveform that satisfies

[0063] Optionally, the segmented far-field model is obtained based on the transmit power, the antenna gain of the transmit array, the antenna gain of the receive array, the complex reflection coefficient of the target, the time-varying propagation distance from the transmit subarray to the target, the time-varying propagation distance from the target to the receive subarray, the time delay from the transmit subarray to the target, the time delay from the target to the receive subarray, the angle between the target and the transmit subarray, the angle between the target and the receive subarray, and additive Gaussian noise.

[0064] After being reflected by a single target located in the near field, the transmitted signal reaches the receiver of the sensing node. The received signal of the j-th receiving subarray of the receiver is represented by the following formula (3):

[0065]

[0066] Among them, P T It is the transmission power;

[0067] G T It is the antenna gain of the transmitting array, G R It is the antenna gain of the receiving array;

[0068] σ is the complex reflection coefficient related to the radar cross-section of the target;

[0069] It is the time-varying propagation distance from the i-th transmitting subarray to the target. It is the time-varying propagation distance from the target to the j-th receiving subarray;

[0070] It is the time delay from the i-th transmitting subarray to the target. It is the time delay from the target to the j-th receiving subarray;

[0071] The initial distance between the target and the i-th transmitting subarray is denoted as: The initial distance between the target and the j-th receiving subarray is denoted as:

[0072] It is the angle between the target and the i-th transmitting subarray. It is the angle between the target and the j-th receiving subarray;

[0073] n j(t) is additive Gaussian noise;

[0074] a t (θ i ) is the steering vector of the i-th transmitting subarray, expressed by the following formula (4):

[0075]

[0076] a r (φ j ) is the steering vector of the j-th receiving subarray, expressed by the following formula (5):

[0077]

[0078] It should be noted that the above formula (3) can represent the received signal reflected by the target in the embodiment of this application.

[0079] The change in the target's position over time can be approximated by a first-order Taylor expansion: p0(t) ≈ p0 + v0t. From the perspective of the receiving subarray, the channel response from the i-th transmitting subarray to the target can be simplified to a constant. To simplify the analysis, assume the target is exactly at the position pointed to by the transmitted beam, i.e., α. i =M, Therefore, the received signal can be simplified to the following formula (6):

[0080]

[0081] It should be noted that the above formula (6) is the received signal after approximating the received signal reflected by the target using a piecewise far-field model.

[0082] Step 103: Based on the processed received signal, obtain the spatial feature vector and temporal feature vector of the target. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor.

[0083] In an optional embodiment, obtaining the spatial feature matrix and temporal feature matrix of the target based on the processed received signal includes:

[0084] Perform matched filtering on the processed received signal to obtain the peak value after matched filtering;

[0085] Based on the autocorrelation function and the bibasal Doppler frequency shift associated with the transmit-receive subarray pair, the peak value after matched filtering is simplified to obtain the observation data matrix of the target;

[0086] Singular value decomposition is performed on the observation data matrix of the target to obtain the spatial feature vector and temporal feature vector of the target.

[0087] In this embodiment, considering L pulses within the CPI (Coherent Processing Interval), the L discrete slow-time sampling points can be represented as t l = lT, l = 0, ..., L-1, where T represents PRI (Pulse Repetition Interval).

[0088] For a specific region observed by the transmit-receive subarray pair (i,j) (including the i-th transmit subarray and the j-th receive subarray), the matched filter output of the n-th range cell during the l-th pulse is expressed by the following formula (7):

[0089]

[0090] Within a short CPI, the received signal reflected by the target will not experience distance migration between different pulses. By selecting an appropriate delay, the received signal reflected by the target can be aligned with the reference signal at the center of the CPI. Here, the selected delay can be expressed by the following formula (8):

[0091]

[0092] The corresponding time delay estimate can be obtained by searching for the peak value of the matched filter output. This leads to the bistatic distance estimate between the target and the transmit-receive subarray pair (i,j) as follows:

[0093] It should be noted that the peak value after matched filtering can be simplified to the following formula (9):

[0094]

[0095] in, It is an autocorrelation function, and ρ i =ρ i (0);

[0096] f ij The bibasal Doppler frequency shift associated with the transmit-receive subarray pair (i,j) is expressed by the following formula (10):

[0097]

[0098] The slow-time sample, i.e., the simplified peak z ij (t l ),t l =lT,l=0,1,...,L-1 are stacked to obtain the target observation data matrix, as shown in the following formula (11):

[0099]

[0100] Among them, b(f) ij ) corresponds to the Doppler frequency shift f ij The Doppler steering vector.

[0101] Each transmit-receive subarray pair (i,j) constitutes a bistatic radar system, where the Doppler steering vector b(f) ij The target's temporal behavior is encoded, receiving the turning vector a. r (φ j The spatial characteristics of the target were captured. Therefore, the problem of near-field moving target parameter estimation can be transformed into extracting and fusing the spatiotemporal characteristics of the target from multiple bistatic radar systems.

[0102] To reduce computational complexity and improve noise robustness, the embodiments of this application use the target observation data matrix Z ij Perform SVD (singular-value decomposition) operation:

[0103] from In this context, the spatial feature vector of the target corresponds to the left singular vector, and the temporal feature vector of the target corresponds to the right singular vector.

[0104] Step 104: Obtain the location information of the target based on the spatial sparse representation model and the spatial feature vector.

[0105] In an optional embodiment, the method further includes:

[0106] The region of interest to which the target belongs is discretized to obtain multiple discrete grid points at different locations;

[0107] For each of the transmit-receive subarray pairs, an oversampling dictionary matrix is ​​constructed based on the multiple discrete grid points at the locations;

[0108] The spatially sparse representation model is constructed based on the oversampling dictionary matrix and the complex gain of the received signal.

[0109] In this embodiment of the application, in order to estimate the location information of the target, the region of interest P to which the target belongs is discretized into P×Q grid points, that is, multiple discrete grid points, wherein the coordinates of the (p,q)th discrete grid point are (x,q) p ,y q ), where p = 1,...,P, q = 1,...,Q.

[0110] Next, for the transmit-receive subarray pair (i,j), construct the oversampling dictionary matrix.

[0111] in, P represents ij The g pq Column vector,

[0112] Furthermore, based on the oversampling dictionary matrix and the complex gain of the received signal, the observed data matrix Z is... ij The model is rewritten to construct a spatially sparse representation model, which is expressed by the following formula (12):

[0113]

[0114] in, It is a sparse data matrix, with only one non-zero entry in each column, representing the complex gain of the received signal reflected by the target, including Doppler frequency shift information.

[0115] In an optional embodiment, obtaining the target's location information based on a spatial sparse representation model and the spatial feature vector includes:

[0116] Construct a dimensionality-reduced vector based on the aforementioned spatial feature vectors;

[0117] The first position information of the target is obtained by utilizing the spatial sparse representation model, the dimensionality reduction vector, the joint sparsity between different transmit-receive subarray pairs, and the mixture norm minimization method.

[0118] The second location information of the target is obtained by using the first location information of the target, the network-free gradient descent optimization method, and the maximum likelihood criterion.

[0119] In this embodiment, since only one target exists, a dimensionality reduction vector is constructed based on the spatial feature vector. Among them, [U ij ] :,1 U ij The first column corresponds to the principal left singular vector, i.e., the spatial eigenvector. This represents the maximum singular value.

[0120] Then, by leveraging the joint sparsity between different transmit-receive subarray pairs, the dimensionality-reduced vector is... Stacked to form M×K t K r Observation matrix Z p And construct an M×K model based on the spatial sparse representation model. t K r Measurement Matrix B P .

[0121] Define PQ×K tK r Sparse data matrix S p Calculate the l2 norm of each row vector to form a new column vector. Based on the joint sparsity between different transmit-receive subarray pairs, the target localization problem can be expressed as a mixture norm minimization problem as shown in the following formula (13):

[0122]

[0123] The reconstructed sparse data matrix is ​​obtained by solving the mixture norm minimization problem. Identify non-zero rows with the largest l2 norm The corresponding grid points provide the target's first location information, i.e., the target's location estimate.

[0124] Furthermore, in order to eliminate grid errors, a gridless gradient descent optimization method and a maximum likelihood optimization criterion are used to refine the first position information of the target for position estimation. The maximum likelihood optimization criterion is shown in the following formula (14):

[0125]

[0126] in, By iteratively updating the position coordinates and complex channel gain, the second position information of the target is obtained, thus achieving high-precision position estimation.

[0127] It should be noted that the maximum likelihood problem can be simplified to the following formula (15):

[0128]

[0129] In the nth iteration, the update rule for the target's position coordinates is expressed by the following formulas (16) and (17):

[0130]

[0131] Based on the above update rules, the second location information of the target is obtained. This second location information is a high-precision location estimation information, which has higher accuracy than the first location information.

[0132] Step 105: Obtain the velocity information of the target based on the velocity sparse representation model, the time feature vector, and the position information of the target.

[0133] In an optional embodiment, the method further includes:

[0134] The velocity range to which the target belongs is discretized to obtain multiple discrete velocity grid points;

[0135] Based on the target's position information and multiple velocity discrete grid points, an overcomplete Doppler steering matrix is ​​constructed;

[0136] Based on the overcomplete Doppler steering matrix, the velocity sparse representation model is constructed.

[0137] In this embodiment of the application, based on the same construction method as the spatial sparse representation model, the velocity sparse representation model shown in the following formula (18) is constructed:

[0138]

[0139] in, It is the overcomplete Doppler steering matrix associated with the discretized velocity grid. It is a velocity discrete grid point (v) xp ,v yq The Doppler shift caused by this.

[0140] In one optional embodiment, obtaining the velocity information of the target based on the velocity sparse representation model, the temporal feature vector, and the target's position information includes:

[0141] Based on the aforementioned time feature vector, the noise subspace is obtained;

[0142] The velocity information of the target is obtained based on the velocity sparse representation model, the target's position information, and the noise subspace.

[0143] In this embodiment of the application, the target's velocity information is estimated using a noise subspace method based on the target's second position information. In the middle, the right singular matrix V ij Includes target velocity information, noise subspace

[0144] Target velocity information (v) x ,v y The MUSIC (Multiple Signal Classification) spectral function can be estimated from the following formula (19):

[0145]

[0146] in, It is a candidate velocity component (v) x,p ,v y,q This method is based on the principle of decoupling position and velocity, and effectively achieves high-precision velocity estimation by utilizing accurate position estimation and projection of the noisy subspace.

[0147] Figure 3 This is a schematic diagram of the single-point near-field joint localization and velocity estimation system based on a very large-scale array, as described in an embodiment of this application. Figure 3 As shown, this application provides a single-point near-field joint localization and velocity estimation system based on a very large-scale array, including:

[0148] The subarray receiving signal processing module is used to acquire the received signal reflected by the target. Since the transmitter transmits a sensing signal, the received signal is a sensing echo signal. The received signal is approximated using a piecewise far-field model to obtain the processed received signal. The processed received signal is then subjected to matched filtering to obtain the peak value after matched filtering. The peak value after matched filtering is simplified to obtain the target's observation data matrix. The target's observation data matrix is ​​then subjected to singular value decomposition to obtain the target's spatial feature vector containing the angle factor and the temporal feature vector containing the Doppler factor.

[0149] It should be noted that the segmented far-field model in this application embodiment can transform the near-field joint positioning and velocity estimation problem into a far-field multi-bistatic radar parameter estimation problem, and solve it using gradient descent and MUSIC spectrum estimation to achieve sub-decimeter-level position accuracy and high-precision velocity estimation.

[0150] The target location estimation module is used to obtain a coarse-grained location estimate of the target based on the spatial feature vector and the minimization of the grid-based L1 mixture norm. The coarse-grained location estimate is further refined by a gridless gradient descent optimization to obtain a fine-grained location estimate of the target.

[0151] The target velocity estimation module is used to construct the bistatic radar Doppler relationship of the transmit-receive subarray pair based on the time feature vector. Based on the fine-grained position estimation of the target and the bistatic radar Doppler relationship, a two-dimensional MUSIC spectrum function is constructed and a two-dimensional spectral peak search is performed to obtain the target velocity estimation result.

[0152] In summary, the single-point near-field joint localization and velocity estimation method based on ultra-large-scale arrays described in this application includes: using a subarray partitioning strategy and a segmented far-field model to approximate the received signals of each transmit-receive subarray pair, effectively reducing computational complexity from being proportional to the total number of antennas to being proportional to the number of transmit-receive subarray pairs, making near-field sensing under XL-MIMO systems possible; through spatial and temporal feature vectors based on SVD dimensionality reduction, position estimation and position refinement based on the joint sparsity between different transmit-receive subarray pairs, and velocity estimation based on the noise subspace, effective decoupling of position and velocity is achieved, improving noise resistance performance, avoiding the computational bottleneck of high-dimensional joint parameter search, and maintaining high-precision parameter estimation performance.

[0153] Please refer to Figure 4 This application also provides a single-point near-field joint localization and velocity estimation device based on a very large-scale array, comprising:

[0154] The first acquisition module 401 is used to divide the transmit array and receive array of the ultra-large-scale multiple input-output XL-MIMO system of the sensing node to obtain multiple transmit-receive subarray pairs, each of the transmit-receive subarray pairs including a transmit subarray and a receive subarray;

[0155] The second acquisition module 402 is used to process the received signal reflected by the target using a segmented far-field model in the sensing beam scanning mode to obtain the processed received signal, wherein the target is located in the far field of each of the transmit-receive subarrays and the target is located in the near field of the transmit array and the receive array.

[0156] The third acquisition module 403 is used to obtain the spatial feature vector and the temporal feature vector of the target based on the processed received signal. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor.

[0157] The fourth acquisition module 404 is used to obtain the location information of the target based on the spatial sparse representation model and the spatial feature vector;

[0158] The fifth acquisition module 405 is used to obtain the velocity information of the target based on the velocity sparse representation model, the time feature vector, and the position information of the target.

[0159] Optionally, in the single-point near-field joint localization and velocity estimation device based on a very large-scale array, the segmented far-field model is obtained based on the transmit power, the antenna gain of the transmit array, the antenna gain of the receive array, the complex reflection coefficient of the target, the time-varying propagation distance from the transmit subarray to the target, the time-varying propagation distance from the target to the receive subarray, the time delay from the transmit subarray to the target, the time delay from the target to the receive subarray, the angle between the target and the transmit subarray, the angle between the target and the receive subarray, and additive Gaussian noise.

[0160] Optionally, in the single-point near-field joint localization and velocity estimation device based on a very large-scale array, the third acquisition module 403 is specifically used for:

[0161] Perform matched filtering on the processed received signal to obtain the peak value after matched filtering;

[0162] Based on the autocorrelation function and the bibasal Doppler frequency shift associated with the transmit-receive subarray pair, the peak value after matched filtering is simplified to obtain the observation data matrix of the target;

[0163] Singular value decomposition is performed on the observation data matrix of the target to obtain the spatial feature vector and temporal feature vector of the target.

[0164] Optionally, the single-point near-field joint localization and velocity estimation device based on a very large-scale array further includes:

[0165] The sixth acquisition module is used to discretize the region of interest to which the target belongs to obtain multiple discrete grid points at different locations;

[0166] The first construction module is used to construct an oversampling dictionary matrix for each of the transmit-receive subarray pairs based on multiple discrete grid points at the locations;

[0167] The second construction module is used to construct the spatially sparse representation model based on the oversampling dictionary matrix and the complex gain of the received signal.

[0168] Optionally, the single-point near-field joint localization and velocity estimation device based on a very large-scale array further includes:

[0169] The seventh module is used to discretize the velocity range to which the target belongs, and obtain multiple velocity discrete grid points;

[0170] The third construction module is used to construct an overcomplete Doppler steering matrix based on the target's position information and multiple velocity discrete grid points;

[0171] The fourth construction module is used to construct the velocity sparse representation model based on the overcomplete Doppler steering matrix.

[0172] Optionally, in the single-point near-field joint localization and velocity estimation device based on a very large-scale array, the fourth obtaining module 404 is specifically used for:

[0173] Construct a dimensionality-reduced vector based on the aforementioned spatial feature vectors;

[0174] The first position information of the target is obtained by utilizing the spatial sparse representation model, the dimensionality reduction vector, the joint sparsity between different transmit-receive subarray pairs, and the mixture norm minimization method.

[0175] The second location information of the target is obtained by using the first location information of the target, the network-free gradient descent optimization method, and the maximum likelihood criterion.

[0176] Optionally, in the single-point near-field joint localization and velocity estimation device based on a very large-scale array, the fifth obtaining module 505 is specifically used for:

[0177] Based on the aforementioned time feature vector, the noise subspace is obtained;

[0178] The velocity information of the target is obtained based on the velocity sparse representation model, the target's position information, and the noise subspace.

[0179] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0180] This application also provides a single-point near-field joint localization and velocity estimation device based on a very large-scale array, such as... Figure 5 As shown, it includes:

[0181] The processor 501, memory 502, transceiver 503, and programs or instructions stored in the memory 502 and executable on the processor 501; when the processor 501 executes the programs or instructions, it implements the various processes of the above-described embodiments of the single-point near-field joint localization and velocity estimation method based on ultra-large-scale arrays, and can achieve the same technical effect. To avoid repetition, these will not be described again here.

[0182] The transceiver 503 is used to receive and send data under the control of the processor 501.

[0183] Among them, Figure 5 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 501 and memory represented by memory 502 together. 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. Transceiver 503 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different user equipment, the user interface 504 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0184] The processor 501 is responsible for managing the bus architecture and general processing, while the memory 502 can store the data used by the processor 501 when performing operations.

[0185] 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 embodiments of the single-point near-field joint localization and velocity estimation method based on a very large-scale array, and achieves the same technical effect. 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, etc.

[0186] 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 embodiments of the single-point near-field joint localization and velocity estimation method based on a very large-scale array, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0187] 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.

[0188] 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.

[0189] 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 single-point near-field joint localization and velocity estimation method based on a very large-scale array, characterized in that, include: The transmit array and receive array of the ultra-large-scale multiple input-output (XL-MIMO) system of the sensing node are divided to obtain multiple transmit-receive subarray pairs, each of which includes a transmit subarray and a receive subarray. In the sensing beam scanning mode, a segmented far-field model is used to process the received signal reflected by the target to obtain the processed received signal. The target is located in the far field of each of the transmit-receive subarrays, and the target is located in the near field of the transmit array and the receive array. Based on the processed received signal, the spatial feature vector and the temporal feature vector of the target are obtained. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor. The location information of the target is obtained based on the spatial sparse representation model and the spatial feature vector; Based on the velocity sparse representation model, the time feature vector, and the target's position information, the velocity information of the target is obtained; The segmented far-field model is obtained based on the transmit power, the antenna gain of the transmit array, the antenna gain of the receive array, the complex reflection coefficient of the target, the time-varying propagation distance from the transmit subarray to the target, the time-varying propagation distance from the target to the receive subarray, the time delay from the transmit subarray to the target, the time delay from the target to the receive subarray, the angle between the target and the transmit subarray, the angle between the target and the receive subarray, and additive Gaussian noise. The spatial feature matrix and temporal feature matrix of the target are obtained based on the processed received signal, including: Perform matched filtering on the processed received signal to obtain the peak value after matched filtering; Based on the autocorrelation function and the bibasal Doppler frequency shift associated with the transmit-receive subarray pair, the peak value after matched filtering is simplified to obtain the observation data matrix of the target; Singular value decomposition is performed on the observation data matrix of the target to obtain the spatial feature vector and temporal feature vector of the target.

2. The single-point near-field joint localization and velocity estimation method based on a very large-scale array according to claim 1, characterized in that, The method further includes: The region of interest to which the target belongs is discretized to obtain multiple discrete grid points at different locations; For each of the transmit-receive subarray pairs, an oversampling dictionary matrix is ​​constructed based on the multiple discrete grid points at the locations; The spatially sparse representation model is constructed based on the oversampling dictionary matrix and the complex gain of the received signal.

3. The single-point near-field joint localization and velocity estimation method based on a very large-scale array according to claim 1, characterized in that, The method further includes: The velocity range to which the target belongs is discretized to obtain multiple discrete velocity grid points; Based on the target's position information and multiple velocity discrete grid points, an overcomplete Doppler steering matrix is ​​constructed; Based on the overcomplete Doppler steering matrix, the velocity sparse representation model is constructed.

4. The single-point near-field joint localization and velocity estimation method based on a very large-scale array according to claim 1, characterized in that, The location information of the target is obtained based on the spatial sparse representation model and the spatial feature vector, including: Construct a dimensionality-reduced vector based on the aforementioned spatial feature vectors; The first position information of the target is obtained by utilizing the spatial sparse representation model, the dimensionality reduction vector, the joint sparsity between different transmit-receive subarray pairs, and the mixture norm minimization method. The second location information of the target is obtained by using the first location information of the target, the network-free gradient descent optimization method, and the maximum likelihood criterion.

5. The single-point near-field joint localization and velocity estimation method based on a very large-scale array according to claim 1, characterized in that, Based on the velocity sparse representation model, the time feature vector, and the target's position information, the velocity information of the target is obtained, including: Based on the aforementioned time feature vector, the noise subspace is obtained; The velocity information of the target is obtained based on the velocity sparse representation model, the target's position information, and the noise subspace.

6. A single-point near-field joint localization and velocity estimation device based on a very large-scale array, characterized in that, include: The first acquisition module is used to divide the transmit array and receive array of the ultra-large-scale multiple input-output (XL-MIMO) system of the sensing node to obtain multiple transmit-receive subarray pairs, each of which includes a transmit subarray and a receive subarray. The second acquisition module is used to process the received signal reflected by the target using a segmented far-field model in the sensing beam scanning mode to obtain the processed received signal, wherein the target is located in the far field of each of the transmit-receive subarrays and the target is located in the near field of the transmit array and the receive array. The third acquisition module is used to obtain the spatial feature vector and temporal feature vector of the target based on the processed received signal. The spatial feature vector includes an angle factor, and the temporal feature vector includes a Doppler factor. The fourth acquisition module is used to obtain the location information of the target based on the spatial sparse representation model and the spatial feature vector; The fifth acquisition module is used to obtain the velocity information of the target based on the velocity sparse representation model, the time feature vector, and the position information of the target; The segmented far-field model is obtained based on the transmit power, the antenna gain of the transmit array, the antenna gain of the receive array, the complex reflection coefficient of the target, the time-varying propagation distance from the transmit subarray to the target, the time-varying propagation distance from the target to the receive subarray, the time delay from the transmit subarray to the target, the time delay from the target to the receive subarray, the angle between the target and the transmit subarray, the angle between the target and the receive subarray, and additive Gaussian noise. The third obtaining module is specifically used for: Perform matched filtering on the processed received signal to obtain the peak value after matched filtering; Based on the autocorrelation function and the bibasal Doppler frequency shift associated with the transmit-receive subarray pair, the peak value after matched filtering is simplified to obtain the observation data matrix of the target; Singular value decomposition is performed on the observation data matrix of the target to obtain the spatial feature vector and temporal feature vector of the target.

7. A single-point near-field joint localization and velocity estimation device based on a very large-scale array, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the single-point near-field joint localization and velocity estimation method based on a very large array as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that, The readable storage medium stores a program that, when executed by a processor, implements the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the single-point near-field joint localization and velocity estimation method based on a very large-scale array as described in any one of claims 1 to 5.

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