Multi-moving target state anti-interference detection method based on MIMO OFDM communication system

Abstract

The invention relates to the technical field of target state detection, and discloses a multi-moving target state anti-interference detection method based on an MIMO OFDM communication system, which comprises the following steps: a base station based on the MIMO OFDM communication system transmits a downlink OFDM detection signal, and if the signal is reflected by a moving target and an interference source, receiving data is obtained; estimating the angles of the moving target and the interference source; designing a receiving beam based on the estimated moving target angle and interference source angle through a beam former, enabling the receiving beam to be aligned with a target signal direction, introducing an interference receiving gain upper bound as a constraint, and obtaining a receiving signal only containing all target information; and demodulated receiving signals are processed by using a target state joint solving method based on subspace decomposition and rotation invariance, and joint estimation of the speed and the distance of each moving target is completed respectively. According to the method, the problem of insufficient precision of joint detection in a multi-dimensional domain in the prior art is solved, and the method has the characteristics of low complexity and high precision.

Description

Technical Field

[0001] The present invention relates to the technical field of target state detection, and more specifically to a multi-mobile target state anti-interference detection method based on a MIMO OFDM communication system. Background Art

[0002] In recent years, the increasingly complex electromagnetic environment has caused traditional, single-function radar and communication systems to gradually fall behind the stringent requirements of applications. At the same time, the frequency bands used by emerging wireless communication systems are expanding to higher frequency bands such as millimeter waves, terahertz, and visible light, gradually overlapping with traditional sensing bands. These conflicts and technological developments are driving the application and advancement of integrated communication and sensing technologies in Sixth Generation Mobile Communication Technology (6G). As a key technology for integrating sensing and communication, the Integrated Sensing and Communications (ISAC) system supported by 6G demonstrates broad application prospects in areas such as military defense, unmanned driving, and automated aviation. It integrates target perception, positioning and tracking, and efficient communication functions, significantly improving the system's overall performance in high-density, multi-target, and complex interference environments. However, multi-target and strong interference remain pressing challenges in real-world scenarios. The signals from multiple targets interact with each other, and the superposition of interference in the environment further weakens the system's ability to identify useful signals. Therefore, the presence of multiple targets in an interference environment poses greater challenges to traditional target state detection (TSD) methods.

[0003] Early radar detection methods primarily relied on pulse radar and continuous wave radar. Pulse radar transmits short pulses and receives the target's reflected echo, while continuous wave radar transmits a continuous electromagnetic wave signal. Both are widely used in target detection and tracking tasks. Furthermore, frequency modulated continuous wave (FMCW) radar has also attracted considerable attention. FMCW radar transmits a continuous wave whose frequency is modulated by a specific signal and measures target state parameters by analyzing the frequency difference between the transmitted and received signals. Due to its advantages such as high-precision distance measurement, low power consumption, and high-resolution target discrimination, FMCW radar has been extensively studied for its applications in intelligent transportation and collision warning.

[0004] With the growing demand for target detection and the increasing complexity of detection scenarios, traditional radar perception systems face challenges in urban and indoor environments, such as occlusion and reflection. Their detection accuracy is particularly limited in scenarios with complex multipath propagation and high target density. To address these issues, communication-based perception methods have been introduced, relying on existing communication infrastructure to perform perception tasks and improving the flexibility and adaptability of system signal processing. This research is attracting widespread attention. Early ISAC systems primarily relied on wireless communication signals such as Wireless Fidelity (Wi-Fi) and Global System for Mobile Communications (GSM) for target detection. Their significant advantage lies in the fact that they do not require the design or deployment of new sensing infrastructure, improving the efficiency of existing resources and reducing system hardware costs. Furthermore, communication-based perception systems do not require the transmission of dedicated detection signals, reducing system design complexity and power consumption. In recent years, numerous state detection algorithms have been proposed that combine Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) technologies, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Minimum Variance Unbiased Estimation, Capon, and Discrete Fourier Transform (DFT). These methods utilize signal subspace, array structure, or frequency domain features for state estimation, each with its own advantages but also limitations. DFT-based detection methods in the time-frequency and spatial domains are highly dependent on the sampling rate, significantly increasing the computational burden. Some algorithms require a tedious state pairing process, resulting in high algorithmic complexity. Furthermore, some proposed detection methods can only extract partial state information of the target while ignoring the estimation of other state parameters, limiting overall detection capabilities.

[0005] For example, the prior art provides an anti-interference frequency offset estimation method and system, comprising: obtaining a control symbol of a transmission frame, demodulating its data portion to obtain subcarrier information corresponding to each OFDM symbol; the subcarrier information comprises reference subcarrier information and data subcarrier information; performing phase rotation on each subcarrier information respectively, and then normalizing the phase information of each subcarrier; performing data expectation on the normalized subcarrier phase information to obtain a phase difference between each OFDM symbol; and calculating the frequency offset value of the transmission frame based on the phase difference.

[0006] However, the existing technology still has the problem of insufficient accuracy of joint detection in multi-dimensional domains. Therefore, how to invent a high-precision anti-interference detection method that takes into account the randomness of communication symbols, complex channel models and environmental interference is a technical problem that urgently needs to be solved in this technical field. Summary of the Invention

[0007] In order to solve the problem of insufficient accuracy of joint detection in a multi-dimensional domain in the prior art, the present invention provides a multi-mobile target state anti-interference detection method based on a MIMO OFDM communication system, which has the characteristics of low complexity and high accuracy.

[0008] In order to achieve the above-mentioned purpose of the present invention, the technical solutions adopted are as follows:

[0009] The anti-interference detection method for the motion state of multiple mobile targets based on a MIMO OFDM communication system comprises the following steps:

[0010] In a MIMO-based OFDM communication system, a base station transmits an OFDM detection signal to the surrounding environment. If the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data.

[0011] estimating an angle between the mobile target and the interference source based on the received data;

[0012] The beamformer designs a receiving beam based on the estimated moving target angle and interference source angle, aligning the receiving beam with the estimated target signal direction. The upper bound of the interference signal receiving gain is introduced as a constraint to optimize the received signal, resulting in a received signal that only contains all moving targets.

[0013] The optimized received signals are processed using a target state joint solution method based on subspace decomposition and rotation invariance to complete the joint estimation of the speed and distance of each moving target.

[0014] Preferably, in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system, the base station transmits an OFDM detection signal to the surrounding environment, specifically in the following steps:

[0015] Assume that in the MIMO OFDM communication system, the antenna spacing is half a wavelength and the number of transmitting antennas is N. T , the number of receiving antennas is N R , suppose there is K in the environment S There are K=K mobile targets to be measured and J interference sources. S +J wave arrival directions; suppose there are K detection objects in the environment, and its angular range is divided into K sub-areas, each area covers a detection object, uses different center frequencies within a specific angular range, and allocates N non-overlapping C subcarriers;

[0016] The communication system transmits M OFDM detection signals into each angular region; the communication system has N C K subcarriers, within the κth angle range, the center frequency of the OFDM detection signal transmitted by the base station is f C (κ) ,in, The total system bandwidth is KB S , the bandwidth allocated in each angle range is B S , the system sampling period is T S , the OFDM symbol time length is P S =N C T S , the subcarrier spacing is f diff =1 / P S .

[0017] Furthermore, if the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and discretizes the sampling to obtain the received data. The specific steps are as follows:

[0018] Assume that the mth time domain baseband OFDM detection signal sent by the base station within the κth angle range is for:

[0019]

[0020] in, Θ κ =[(κ-1)N C ,...,κN C -1], x κ,m,n is the mth downlink OFDM symbol on the nth subcarrier sent within the κth angle range;

[0021] In the direction of arrival θ κ The reflected echo reaching the reference element of the array within the mth OFDM symbol period is Expressed as:

[0022]

[0023] Among them, h κ is the channel fading coefficient, which is constant within the system coherence time, is the transmit beam steering vector within the κth angle range, ρ κ and v κ Indicates that it is located in the direction θ κ The distance and speed of the detected object, f κ =2v κ f C (κ) / c is located in the direction of arrival θ κ The Doppler shift of the reflected echo on C (κ) is the center frequency assigned by the base station to the corresponding direction, c represents the speed of light, ε κ,m (t) is additive white Gaussian noise with mean 0, Indicates the angle θ of the transmitter κ Array response vector of ;

[0024] Get the direction of arrival θ κ On the mth OFDM symbol period, the echo signal arriving at the reference element of the receiving array is N C A vector of signal values ​​at sampling time points Simplify the expression:

[0025]

[0026] Among them, mat[·] means arranging the vectors into a matrix form in order. Indicates the direction θ κ N on the kth sampling point C The frequency modulation vector of the subcarrier is Combine by row

[0027] is the phase offset vector,

[0028] make Indicates the direction θ κ The echo signal N C A set of M sampling values ​​is obtained, and the echo sampling values ​​from K wave arrival directions are combined into

[0029]

[0030] The echo signal reaches the observation base station and is received by the antenna array, and the array's receiving steering vector is obtained.

[0031]

[0032] The received steering vectors of K wave arrival directions are arranged in columns to obtain the array manifold matrix

[0033] A=mat[α R (θ κ )|κ=1,...,K]

[0034] Get received data

[0035]

[0036] in, represents the channel noise, Indicates the N received by the rth antenna C M echo signal values.

[0037] Furthermore, we can get the direction of arrival θ κ On the mth OFDM symbol period, the echo signal arriving at the receiving array is N C A vector of signal values ​​at sampling time points Expression, the specific steps are:

[0038] In the direction of arrival θ κ The signal value at the kth sampling time point in the mth OFDM symbol period arriving at the receiving array is The expression:

[0039]

[0040] Assumptions:

[0041] The system is a narrowband system. The bandwidth of the system is relatively small compared to the center carrier frequency. S / f C (κ) <<1 / M;

[0042] The target moves at a low speed, and the displacement caused within the duration of M OFDM symbols is much smaller than the spatial resolution cT of the system. S , that is, v κ P S MB S / c<<1,

[0043] The Doppler shift of the target is much smaller than the subcarrier spacing f diff , that is, f κ <<f diff ,

[0044] Consider fκ <<f diff , so N C v κ f C (κ) / c<<N C f diff =B S , that is, N C v κ / c<<B S / f C (κ) ; Due to B S / f C (κ) <<1,N C v κ / c<<1, so kv κ / c<<1; and because f diff P S =1, get

[0045] Consider v κ P S MB S / c<<1, and B S =1 / T S , 2v κ / c<<T S / MP S =1 / N C M, so Because P S =N C T S and B S =1 / T S , so get

[0046] Because f κ <<f diff <B S =1 / T S , there is f κ T s <<1, so get

[0047] Based on the above assumptions, The expression simplifies to:

[0048]

[0049] Furthermore, a beamformer is used to design a receive beam based on the estimated moving target angle and interference source angle. The steps are as follows:

[0050] The beamformer designs the receiving beam based on the estimated target angle and interference source angle, and obtains the signal of the receiving beam after processing.

[0051]

[0052] in, is the beam weight vector, ||ω||=1, where Y is the output signal of the receiving antenna array,

[0053] Furthermore, the received signal is optimized to minimize the deviation between the receiving beam and the estimated target signal direction. The upper bound of the interference signal receiving gain is introduced as a constraint to obtain a received signal that only contains all moving target echoes. The specific steps are as follows:

[0054] Optimization problem of constructing the receive beam:

[0055]

[0056] in, is the target angle estimated by the base station, is the estimated interference wave angle, and γ is the interference receiving gain constraint value set according to actual needs;

[0057] Using the Lagrange multiplier method, construct the objective function L(ω,λ):

[0058]

[0059] Where λ is the Lagrange multiplier;

[0060] Let L(ω,λ) be the value of ω * Taking the partial derivative and setting it to 0, we get:

[0061]

[0062] The closed-form expression of the beamforming weight vector ω is obtained:

[0063]

[0064] The value of λ is determined according to the requirements of interference power suppression in the constraints:

[0065]

[0066] Substitute the closed-form expression of the weight vector ω into the constraint conditions to obtain a nonlinear equation about λ. Solve the nonlinear equation numerically and finally determine the optimal ω that meets the requirements. Substitute the optimal ω into The expression is used to get the received signal after filtering out the interference signal.

[0067] Furthermore, the optimized received signals are processed using a joint target state solution method based on subspace decomposition and rotation invariance to complete the joint estimation of the speed and distance of each moving target. The specific steps are as follows:

[0068] make N represents the output of the beamformer in the mth OFDM symbol period C Values:

[0069]

[0070] in, represents the channel noise;

[0071] Using the transmitted symbols and the discrete Fourier transform matrix Perform coherent detection on the received signal to obtain the frequency domain baseband signal

[0072]

[0073] make

[0074]

[0075] in, is the noise matrix, is the phase shift caused by the Doppler effect, expressed as:

[0076]

[0077] Use a sliding window in the time-frequency domain, from Z (l) Extract submatrix from

[0078]

[0079] where N w ≤N C , M w ≤M,N w and M w Corresponding to the frequency domain and time domain length of the sub-matrix respectively; at the same time, i and j are the lengths of each sub-matrix respectively. In Z (l) The starting row and column indices in ,

[0080] make The matrix The elements of K are arranged in columns. L=N w M w For a snapshot size, K snap =(N C -N w +1)(MM w +1) is the number of reconstructed snapshots, It's a quick sample The matrix form of the set;

[0081] make Indicated by In the snapshot matrix Z (l) The phase shift caused by the time delay is determined by the position in Indicated by The phase shift caused by the Doppler effect is determined by the position of:

[0082]

[0083] make Represents a submatrix The local phase shift of each signal value in is caused by the time delay, so Represents the local phase shift caused by the Doppler effect:

[0084]

[0085] make In the submatrix Z (i,j) The local phase shift of each signal value caused by time delay and Doppler effect is:

[0086]

[0087] Bundle Expressed as:

[0088]

[0089] in, is the corresponding noise;

[0090] make is the propagation weight vector determined by the target state and signal propagation parameters:

[0091]

[0092]

[0093] Snapshot Matrix Expressed as:

[0094]

[0095] in, is the zero-mean noise matrix, let Represents the snapshot matrix The correlation matrix of Perform eigenvalue decomposition:

[0096]

[0097] in, is the angle θ l The characteristic value of the target signal, is the corresponding signal space, is the noise space obtained by separation;

[0098] Furthermore, the state subspace extraction is performed to extract the subspace containing the moving target speed-distance state information. The specific steps are as follows:

[0099] Construct two selection matrices

[0100] E N =[I N ,0 N×1 ]

[0101]

[0102] With the help of these two selection matrices, the transformation matrix for extracting the subspace is constructed:

[0103]

[0104] Among them, F UE 、F v and Using these three transformation matrices, we can transform u l Three signal subspaces are extracted from:

[0105]

[0106] Furthermore, the speed and distance of all moving targets are estimated jointly based on the Doppler effect and time delay. The specific steps are as follows:

[0107] make is the propagation model weight matrix The variance of

[0108]

[0109] The snapshot signal matrix Correlation matrix Expressed as:

[0110]

[0111] because is a unitary matrix, so Further we get:

[0112]

[0113] in, Defining a vector

[0114]

[0115] get:

[0116]

[0117] make represents the phase offset of the lth target caused by the Doppler effect during adjacent OFDM symbol cycles; let Indicates the phase offset of the lth target caused by the delay between adjacent subcarriers,

[0118]

[0119] β l (τ l ,f l )Using the transformation matrix {F v ,F ρ}get:

[0120]

[0121] Construction variables

[0122]

[0123] get:

[0124]

[0125] according to Get the speed of the lth moving target and distance

[0126]

[0127] The anti-interference detection system for the motion state of multiple mobile targets based on the MIMO OFDM communication system includes a data receiving module, an angle estimation module, a beam filtering module, and a target detection module;

[0128] The data receiving module is used in a MIMO OFDM communication system, where the base station transmits an OFDM detection signal to the surrounding environment; if the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data;

[0129] The angle estimation module is used to estimate the angle between the mobile target and the interference source based on the received data;

[0130] The beam filtering module is used to design a receiving beam based on the estimated moving target angle and interference source angle through a beamformer, so that the receiving beam is aligned with the estimated target signal direction. At the same time, the upper bound of the interference signal receiving gain is introduced as a constraint to optimize the received signal to obtain a received signal that only contains all moving targets.

[0131] The target detection module is used to process the received signal after the spatial filtering to complete the joint estimation of the speed and distance of each moving target.

[0132] The beneficial effects of the present invention are as follows:

[0133] The present invention discloses an anti-interference detection method for multiple mobile target states based on a MIMO OFDM communication system, which solves the problems of randomness of transmission symbols in actual communication scenarios and complex calculation of the state matching process in the multi-target motion state estimation task. The upper bound of the interference signal reception gain is introduced as a constraint to achieve a more stable performance trade-off between signal enhancement and interference suppression. A target state joint solution method based on subspace decomposition and rotation invariance is proposed, which optimizes the signal time-frequency domain feature extraction. Therefore, the present invention solves the problem of insufficient accuracy of joint detection in the multi-dimensional domain in the existing technology, and has the characteristics of low complexity and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 It is a flow chart of a method for detecting multi-mobile target states with anti-interference based on a MIMO OFDM communication system according to the present invention.

[0135] Figure 2 Schematic diagram of the detection result of the moving target in Example 1.

[0136] Figure 3 Schematic diagram of distance and speed estimation errors under different signal-to-noise ratio conditions using the method proposed in the present invention in Example 2.

[0137] Figure 4 Schematic diagram of distance and speed estimation errors under different signal-to-noise ratio conditions using the non-anti-interference method in Example 2.

[0138] Figure 53 is a schematic diagram comparing the distance estimation accuracy of the method proposed in the present invention and the traditional target state detection method under different signal-to-noise ratios in Example 3.

[0139] Figure 6 3 is a schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different signal-to-noise ratios in Example 3.

[0140] Figure 7 3 is a schematic diagram comparing the distance estimation accuracy of the method of the present invention and the traditional target state detection method under different target distance differences in Example 4.

[0141] Figure 8 3 is a schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different target speed differences in Example 4.

[0142] Figure 9 3 is a schematic diagram comparing the distance estimation accuracy of the method of the present invention and the traditional target state detection method under different target numbers in Example 5.

[0143] Figure 10 3 is a schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different target numbers in Example 5.

[0144] Figure 11 This is the beam pattern obtained by applying the beam design method proposed in the present invention in Example 6.

[0145] Figure 12 Schematic diagram of the experimental results of the optimized beam design method in Example 6. DETAILED DESCRIPTION

[0146] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0147] Example 1

[0148] like Figure 1 As shown, a multi-mobile target state anti-interference detection method based on a MIMO OFDM communication system includes the following steps:

[0149] In a MIMO OFDM-based communication system, a base station transmits an OFDM detection signal to the surrounding environment. If the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data.

[0150] estimating an angle between the mobile target and the interference source based on the received data;

[0151] The beamformer designs a receiving beam based on the estimated moving target angle and interference source angle. The upper bound of the interference power is introduced as a constraint to optimize the received signal to obtain a received signal that only contains all moving targets.

[0152] The optimized received signals are processed using a target state joint solution method based on subspace decomposition and array rotation invariance to complete the joint estimation of the speed and distance of all moving targets.

[0153] In this embodiment, there are J=2 interference sources and K S = 5 mobile targets to be detected. Assume that the observation base station can distinguish the interference signal from the target signal; the target and the interference source are regarded as detection objects. There are K = 7 detection objects in total, that is, there are K echo arrival directions, located in the direction θ κ The speed and distance of the detection object on the κ and ρ κ The actual state parameters of the detection object are shown in Table 1 and Table 2:

[0154] Table 1

[0155]

[0156]

[0157] Table 2

[0158] interference Distance (m) Angle (°) Speed ​​(m / s) 1 30 60 0 2 60 -40 0

[0159] In the MIMO OFDM communication system of this embodiment, the antenna spacing is half a wavelength, and the number of transmitting antennas is N. T =16, the number of receiving antennas N R =16. Signal-to-noise ratio (SNR) = 20dB. The observation base station transmits an OFDM waveform detection signal into the environment. Specifically, the observation base station divides the spatial angle into 7 sub-areas, each covering a detection object. The number of OFDM symbols transmitted to each angular area is M = 64, and the number of subcarriers divided into one OFDM symbol is N. C =256, for the first sub-angle region, the center frequency of the transmitted detection signal Subcarrier spacing f diff =120kHz.

[0160] In this embodiment, the base station estimates the angle between the mobile target and the interference source based on the received signal using the multi-signal spectrum estimation algorithm MUSIC. The specific steps are as follows:

[0161] Calculate the covariance of the received signal Y

[0162]

[0163] in σ 2 is the noise variance, is the identity matrix; for the covariance matrix R Y Perform eigenvalue decomposition to obtain signal subspace and noise subspace respectively;

[0164] R Y Perform eigendecomposition and transform R Y Expressed as:

[0165]

[0166] in, represent the signal subspace and its corresponding eigenvalue matrix respectively, Represent the noise subspace and its eigenvalue matrix respectively. The noise eigenvalue is the noise variance σ 2 ;

[0167] Consider the eigenvector of the noise subspace Satisfy R Y u ξ =λ ξ u ξ ,in, is the eigenvector u ξ The corresponding eigenvalues ​​are further obtained:

[0168]

[0169]

[0170] Constructing MUSIC spectrum function based on orthogonal relationship

[0171]

[0172] Among them, θ∈(-90°,90°);

[0173] In the direction of arrival θ κ Up, P MUSIC (θ κ ) appears a peak, and the environmental target signal angle is obtained according to the peak of the search space spectrum and interference signal angle

[0174] In this embodiment, the sub-matrix In, N w =M w =9. i and j are the In Z (l)The starting row and column indices in ,

[0175] In this embodiment, the detection results of 5 moving targets in the environment are as follows: Figure 2 As shown in the figure, the red mark indicates the target position estimated by the system, and the blue mark indicates the actual target position. The results show that the target state detection method based on the MIMO OFDM system proposed in this paper can obtain a solution close to the actual target state.

[0176] Example 2

[0177] In this embodiment, there are J=2 interference sources and K S = 3 moving targets to be detected. Assume that the observation base station can distinguish the interference signal direction from the target signal direction; the target and the interference source are uniformly regarded as detection objects. There are K = 5 detection objects in total, that is, there are K echo arrival directions, located in the direction θ κ The speed and distance of the detection object on the κ and ρ κ The actual state parameters of each detection object are shown in Table 3 and Table 4:

[0178] Table 3

[0179] Target Distance (m) Angle (°) Speed ​​(m / s) 1 30 20 25 2 31 30 40 3 32 40 55

[0180] Table 4

[0181] interference Distance (m) Angle (°) Speed ​​(m / s) 1 30 60 0 2 60 -40 0

[0182] In the MIMO OFDM communication system of this embodiment, the antenna spacing is half a wavelength, and the number of transmitting antennas is N. T =16, the number of receiving antennas N R =16. Signal-to-noise ratio (SNR) = 20dB. The observation base station transmits a detection signal of an OFDM waveform into the environment. Specifically, the observation base station divides the angular space into 5 sub-areas, each covering a detection object. The number of OFDM symbols transmitted to each angular area is M = 64, and the number of subcarriers divided into one OFDM symbol is N. C =256, the center frequency of the angle area where the first detection object is located Subcarrier spacing f diff =120kHz. Figure 1 The system implements methods to estimate the angle, speed and distance of the target.

[0183] In this embodiment, after estimating the moving target angle and the interference source angle, target state detection is performed again.

[0184] After receiving the echo signal, the array outputs the signal matrix Y, which is expressed as:

[0185]

[0186] make express N in the mth OFDM symbol period C A value, expressed as:

[0187]

[0188] in, Represents channel noise; uses the transmission symbols sent in the target direction and the corresponding inverse discrete Fourier transform matrix Perform coherent detection on the received signal to obtain the frequency domain baseband signal received on the rth antenna

[0189]

[0190] Further, let The expression is:

[0191]

[0192] Use a sliding window, from Z (r,l) Extract submatrix from Submatrix The composition is as follows:

[0193]

[0194] Let K L =N w M w For a snapshot size, K snap =(N C -N w +1)(MM w +1) is the number of reconstructed snapshots. Each sub-matrix extracted The elements of are arranged into vectors by columns, and K snap Snapshot signal Further, the obtained Combine into a new matrix That is, the reconstructed snapshot signal matrix of the lth target on the rth antenna is

[0195] Calculate the snapshot signal matrix of each antenna The covariance of is expressed as:

[0196]

[0197] Complete coherent detection on the signal received by each antenna and calculate the correlation matrix to obtain Put N R The correlation matrices are superimposed and averaged to obtain:

[0198]

[0199] right Performing eigenvalue decomposition can separate the signal space and the noise space:

[0200]

[0201] Perform state subspace extraction, extract the subspace containing the moving target speed-distance state, and calculate the variable Thus, the speed and distance of each detected object are obtained:

[0202]

[0203] In Example 2, beamforming anti-interference and non-anti-interference schemes are used to complete target state detection. The distance and speed estimation errors of the two methods under different signal-to-noise ratio conditions are as follows: Figure 3 、 Figure 4 It can be seen that the anti-interference solution proposed in the present invention has good anti-interference performance and can significantly improve the state detection accuracy of the system.

[0204] Example 3

[0205] In this embodiment, there are two detection targets, and their actual state parameters are shown in Table 5.

[0206] Table 5

[0207] Target Distance (m) Angle (°) Speed ​​(m / s) 1 48 28 40 2 50 30 42

[0208] The multi-mobile target state anti-interference detection method proposed in the present invention is used and repeated 7 times to calculate the distance and speed estimation errors of the state detection method proposed in the present invention under signal-to-noise ratios of 0, 5, 10, 15, 20, 25, and 30 dB. Figure 5 A schematic diagram comparing the distance estimation accuracy of the method proposed in this invention and the traditional target state detection method under different signal-to-noise ratios is given. Figure 6 A schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different signal-to-noise ratios is given.

[0209] according to Figure 5 、 Figure 6The results show that the proposed algorithm achieves higher range and velocity estimation accuracy than the ESPRIT and MUSIC algorithms. Furthermore, its performance improves with increasing signal-to-noise ratios, demonstrating greater robustness and estimation accuracy across a wide range of signal-to-noise ratios. In particular, it provides more reliable target state detection results under low SNR conditions.

[0210] Example 4

[0211] In Example 4, there are two detection targets, and their actual state parameters are shown in Table 7.

[0212] Table 7

[0213] Target Distance (m) Angle (°) Speed ​​(m / s) 1 48 28 40 2 50 30 42

[0214] According to the multi-moving target state anti-interference detection method proposed in the present invention, it is repeated 5 times. Each state detection continuously increases the distance difference between the two targets, and the distance estimation error is calculated when the distance difference between the two targets is 2, 4, 6, 8, and 10 meters.

[0215] according to Figure 1 The state detection step proposed in the present invention is repeated 5 times, and each state detection continuously increases the speed difference between the two targets, and calculates the speed estimation error when the speed difference between the two targets is 2, 4, 6, 8, and 10 m / s respectively.

[0216] Figure 7 A schematic diagram comparing the distance estimation accuracy of the method of the present invention and the traditional target state detection method under different target distance differences is given. Figure 8 A schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different target speed differences is given.

[0217] Depend on Figure 7 、 Figure 8 It can be seen that when the speed or distance between targets increases, the estimation errors of each algorithm show a downward trend. The estimation error of the MUSIC algorithm decreases more significantly. However, the algorithm proposed in this study can still provide better detection accuracy than the baseline method, indicating that it has better robustness in high-resolution target detection tasks.

[0218] Example 5

[0219] The proposed method for detecting multiple moving targets with anti-interference was repeated five times, with the number of targets in the environment increasing with each iteration. In each experiment, the target angles ranged from -40° to 40° and were evenly spaced. Each target's speed was 30 m / s, and its relative distance from the base station was 40 m.

[0220] Figure 9 A schematic diagram comparing the distance estimation accuracy of the method of the present invention and the traditional target state detection method under different target numbers is given. Figure 10 A schematic diagram comparing the speed estimation accuracy of the method of the present invention and the traditional target state detection method under different target numbers is given.

[0221] Depend on Figure 9 、 Figure 10 It can be seen that as the number of targets in the environment increases, the interference between targets increases, the detection difficulty increases, the detection performance of each algorithm decreases, and the estimation error increases. As the number of targets in the environment increases, the error growth rate of the ESRPIT algorithm is faster, and its adaptability in high-density target detection tasks is limited. However, under the influence of inter-target interference, the error growth rate of the algorithm proposed in this invention is significantly lower than that of the baseline method, indicating that this method can maintain better performance in high-target density scenarios. This advantage makes the state detection method proposed in this invention of great value in complex signal environment scenarios and applications such as high-density target resolution.

[0222] Example 6

[0223] In this embodiment, it is assumed that there is a target to be measured and two interference sources in the environment, the target angle is -20°, and the interference source angles are -45° and 10°. The corresponding receiving beam is designed according to the beam design method proposed in the present invention. Figure 11 The beam pattern obtained by applying the LCMV beamforming method is given. Figure 12 The beam pattern obtained by applying the beam design method proposed in the present invention is given.

[0224] observe Figure 11 , the interference suppression capability of LCMV is well demonstrated, and it can form a clear and accurate beam null in the direction of the interference wave to suppress the interference signal gain. This shows that LCMV can provide effective anti-interference performance when the direction of the interference source is known, and is suitable for application scenarios with multiple interference sources. However, its main lobe beam direction is not accurate enough. Although it can provide higher gain than the direction of the interference source, its main lobe width is large, which is not conducive to the system's precise focusing of useful signals. At the same time, it can be seen from the results that the sidelobe leakage problem is more significant, and the sidelobe gain has not been reduced to an acceptable level, which may weaken the system's anti-interference performance. In the direction of non-target signal sources or interference sources, its gain is still higher than the ambient noise level and may even exceed the signal gain in the target direction. Although there are no interference sources in these directions, this is not what the receiver needs, and it will also reduce system performance due to the reception of more invalid signals such as noise.

[0225] Observe the experimental results of the optimized beam design method, such as Figure 12As shown, the peak of the main lobe of the beam can be aligned in the direction of the target signal. The results show that the newly proposed algorithm can effectively enhance the received signal power in the target direction. This enhanced directionality is crucial for improving signal detection performance and communication quality. At the same time, it can be seen that a deep null point is formed in the direction of each interfering signal, indicating that the algorithm can effectively form a strong suppression zone in the known interference direction. Compared with the sidelobe leakage problem of LCMV, the sidelobe level formed by this algorithm is generally maintained below -20dB, and in some areas even below -40dB. This feature helps to suppress interference signals and environmental noise from other non-specified directions, further improving the quality of the received useful signal.

[0226] Example 7

[0227] A multi-mobile target state anti-interference detection system based on a MIMO OFDM communication system includes a data receiving module, an angle estimation module, a beam filtering module, and a target detection module;

[0228] The data receiving module is used in a MIMO OFDM communication system, where the base station transmits an OFDM detection signal to the surrounding environment; if the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data;

[0229] The angle estimation module is used to estimate the angle between the mobile target and the interference source based on the received data;

[0230] The beam filtering module is used to design a receiving beam based on the estimated moving target angle and interference source angle through a beamformer, so that the receiving beam direction is as close as possible to the estimated target signal direction. At the same time, the upper bound of the interference signal receiving gain is introduced as a constraint to optimize the received signal to obtain a received signal that only contains all moving targets.

[0231] The target detection module is used to process the received signal and complete the joint estimation of the speed and distance of each moving target.

[0232] Obviously, the above embodiments of the present invention are merely examples for the purpose of illustrating the present invention, and are not intended to limit the embodiments of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the motion state of multiple mobile targets based on a MIMO OFDM communication system, characterized in that: The following steps are involved: In a MIMO-based OFDM communication system, a base station transmits an OFDM detection signal to the surrounding environment. If the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data. estimating an angle between the mobile target and the interference source based on the received data; The beamformer designs a receiving beam based on the estimated moving target angle and interference source angle, aligning the receiving beam with the estimated target signal direction. The upper bound of the interference signal receiving gain is introduced as a constraint to optimize the received signal, resulting in a received signal that only contains all moving targets. The optimized received signals are processed using a target state joint solution method based on subspace decomposition and rotation invariance to complete the joint estimation of the speed and distance of each moving target.

2. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 1, wherein: In a MIMO-based OFDM communication system, a base station transmits an OFDM detection signal to the surrounding environment. The specific steps are as follows: Assume that in the MIMO OFDM communication system, the antenna spacing is half a wavelength and the number of transmitting antennas is N. T , the number of receiving antennas is N R , suppose there is K in the environment S There are K=K mobile targets to be measured and J interference sources. S +J wave arrival directions; suppose there are K detection objects in the environment, and its angular range is divided into K sub-areas, each area covers a detection object, uses different center frequencies within a specific angular range, and allocates N non-overlapping C subcarriers; The communication system transmits M OFDM detection signals into each angular region; the communication system has N C K subcarriers, within the κth angle range, the center frequency of the OFDM detection signal transmitted by the base station is f C (κ) ,in, The total system bandwidth is KB S , the bandwidth allocated in each angle range is B S , the system sampling period is T S , the OFDM symbol time length is P S =N C T S , the subcarrier spacing is f diff =1 / P S .

3. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 2, wherein: If the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and discretizes the sampling to obtain the received data. The specific steps are as follows: Assume that the mth time domain baseband OFDM detection signal sent by the base station within the κth angle range is for: in, Θ κ =[(κ-1)N C ,...,κN C -1], x κ,m,n is the mth downlink OFDM symbol on the nth subcarrier sent within the κth angle range; In the direction of arrival θ κ The reflected echo reaching the reference element of the array within the mth OFDM symbol period is Expressed as: in, is the channel fading coefficient, which is constant within the system coherence time, is the transmit beam steering vector within the κth angle range, ρ κ and v κ Indicates that it is located in the direction θ κ The distance and speed of the detected object, f κ =2v κ f C (κ) / c is located in the direction of arrival θ κ The Doppler shift of the reflected echo on C (κ) is the center frequency assigned by the base station to the corresponding direction, c represents the speed of light, is additive white Gaussian noise with mean 0, Indicates the angle θ of the transmitter κ Array response vector of ; Get the direction of arrival θ κ On the mth OFDM symbol period, the echo signal arriving at the reference element of the receiving array is N C A vector of signal values ​​at sampling time points Simplify the expression: Among them, mat[·] means arranging the vectors into a matrix form in order. Indicates the direction θ κ N on the kth sampling point C The frequency modulation vector of the subcarrier is Combine by row is the phase offset vector, make Indicates the direction θ κ The echo signal N C A set of M sampling values ​​is further obtained to obtain a set of echo sampling values ​​arriving at the reference array element from K wave arrival directions. The echo signal reaches the observation base station and is received by the antenna array, and the array's receiving steering vector is obtained. The received steering vectors of K wave arrival directions are arranged in columns to obtain the array manifold matrix A=mat[a R (i κ )|κ=1,...,K] Get antenna array receiving data in, represents the channel noise, Indicates the N received by the rth antenna C M echo signal values.

4. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 3, wherein: Get the direction of arrival θ κ On the mth OFDM symbol period, the echo signal arriving at the receiving array is N C A vector of signal values ​​at sampling time points Expression, the specific steps are: In the direction of arrival θ κ The signal value at the kth sampling time point in the mth OFDM symbol period arriving at the receiving array is The expression: Assumptions: The system is a narrowband system. The bandwidth of the system is relatively small compared to the center carrier frequency. S / f C (κ) <<1 / M; The target moves at a low speed, and the displacement caused within the duration of M OFDM symbols is much smaller than the spatial resolution cT of the system. S ,Right now The Doppler shift of the target is much smaller than the subcarrier spacing f diff ,Right now Consider f κ < <f diff , so N C v κ f C (κ) / c<<N C f diff =B S , that is, N C v κ / c< S / f C (κ) ; Due to B S / f C (κ) <<1, N C v κ / c<<1, so kv κ / c<<1; and because f diff P S =1, get ​ Consider v κ P S MB S / c<<1, and B S =1 / T S , 2v κ / c<<T S / MP S =1 / N C M, so Because P S =N C T S and B S =1 / T S , so get Because f κ <<f diff <B S =1 / T S , there is f κ T S <<1, so get Based on the above assumptions, The expression simplifies to:

5. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 4, characterized in that: The beamformer designs the receive beam based on the estimated moving target angle and interference source angle. The steps are: The beamformer designs the receiving beam based on the estimated target angle and interference source angle, and obtains the signal of the receiving beam after processing. in, is the beam weight vector, ||ω||=1, where Y is the output signal of the receiving antenna array, 6. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 5, characterized in that: Considering minimizing the deviation between the receiving beam and the estimated target signal direction, while introducing the upper bound of the interference signal receiving gain as a constraint, the received signal is optimized to obtain a received signal that only contains all mobile target echoes. The specific steps are as follows: Optimization problem of constructing the receive beam: in, is the target angle estimated by the base station, is the estimated interference wave angle, and γ is the interference receiving gain constraint value set according to actual needs; Using the Lagrange multiplier method, construct the objective function L(ω,λ): Where λ is the Lagrange multiplier; Let L(ω,λ) be the value of ω * Taking the partial derivative and setting it to 0, we get: The closed-form expression of the beamforming weight vector ω is obtained: The value of λ is determined according to the requirements of interference power suppression in the constraints: Substituting the closed-form expression of the weight vector ω into the constraints, a nonlinear equation about λ is obtained. The nonlinear equation is solved numerically to ultimately determine the optimal received signal beam ω that meets the requirements.

7. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 5, wherein: The optimized received signal is processed using a target state joint solution method based on subspace decomposition and rotation invariance to complete the joint estimation of the speed and distance of each moving target. The specific steps are as follows: make N represents the output of the beamformer in the mth OFDM symbol period C Values: in, represents the channel noise; Using the transmitted symbols and the discrete Fourier transform matrix Perform coherent detection on the received signal to obtain the frequency domain baseband signal make in, is the noise matrix, is the phase shift caused by the Doppler effect, expressed as: Use a sliding window in the time-frequency domain, from Z (l) Extract submatrix from where N w ≤N C , M w ≤M,N w and M w Corresponding to the frequency domain and time domain length of the sub-matrix respectively; at the same time, i and j are the lengths of each sub-matrix respectively. In Z (l) The starting row and column indices in , make The matrix The elements of K are arranged in columns. L =N w M w For a snapshot size, K snap =(N C -N w +1)(MM w +1) is the number of reconstructed snapshots, It's a quick sample The matrix form of the set; make Indicated by In the snapshot matrix Z (l) The phase shift caused by the time delay is determined by the position in Indicated by The phase shift caused by the Doppler effect is determined by the position of: make Represents a submatrix The local phase shift of each signal value in is caused by the time delay, so Represents the local phase shift caused by the Doppler effect: make Represented in the submatrix In the equation, the local phase shift of each signal value caused by time delay and Doppler effect is: Bundle Expressed as: in, is the corresponding noise; make is the propagation weight vector determined by the target state and signal propagation parameters: Snapshot Matrix Expressed as: in, Let be the zero-mean noise matrix, Represents the snapshot matrix The correlation matrix of Perform eigenvalue decomposition: in, is the angle θ l The characteristic value of the target signal, is the corresponding signal space, is the noise space obtained by separation; Perform state subspace extraction, respectively from u l Three signal subspaces are extracted from The first N targets of the lth target signal are retained w -1 frequency domain component and post-M w -1 signal subspace consisting of time domain components; After retaining the lth target signal, N w -1 frequency domain component and the first M w -1 signal subspace composed of time domain component information; The first N targets of the lth target signal are retained w -1 frequency domain component and the first M w -1 signal subspace composed of time domain component information; The subspace containing the speed-range state information of the moving targets is extracted, and the speed and range of all moving targets are jointly estimated based on the Doppler effect and time delay.

8. The method for detecting the motion state of multiple mobile targets based on a MIMO OFDM communication system according to claim 1, wherein: To extract the state subspace, the specific steps are: Construct two selection matrices E N =[I N ,0 N×1 ] With the help of these two selection matrices, the transformation matrix for extracting the subspace is constructed: Among them, F UE 、F v and Using these three transformation matrices, we can transform u l Three signal subspace matrices related to speed and distance information are extracted:

9. The method for detecting multiple mobile target states with anti-interference based on a MIMO OFDM communication system according to claim 7, wherein: Extract the subspace containing the speed-range state of the moving targets, and jointly estimate the speed and range of all moving targets based on the Doppler effect and time delay. The specific steps are as follows: make is the propagation model weight matrix The variance of The snapshot signal matrix Correlation matrix Expressed as: because is a unitary matrix, so Further we get: in, Defining a vector get: make represents the phase offset of the lth target caused by the Doppler effect during adjacent OFDM symbol cycles; let Indicates the phase offset of the lth target caused by the delay between adjacent subcarriers, β l (τ l ,f l )Use the transformation matrix {F v ,F ρ }get: because Right now get: Construction variables get: So we get: according to Get the speed of the lth moving target and distance 10. A multi-mobile target motion state anti-interference detection system based on a MIMO OFDM communication system, characterized by: It includes data receiving module, angle estimation module, beam filtering module and target detection module; The data receiving module is used in a MIMO OFDM communication system, where the base station transmits an OFDM detection signal to the surrounding environment; if the signal is reflected by several mobile targets and interference sources to form an echo, the observation base station receives the echo and obtains the received data; The angle estimation module is used to estimate the angle between the mobile target and the interference source based on the received data; The beam filtering module is used to design a receiving beam based on the estimated moving target angle and interference source angle through a beamformer, so that the receiving beam is close to the moving target angle. At the same time, the upper bound of the interference signal receiving gain is introduced as a constraint to optimize the received signal to obtain a received signal that only contains all moving targets; The target detection module is used to process the received signal after the spatial domain filtering to complete the joint estimation of the speed and distance of each moving target.

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