Integrated target positioning method for MIMO-OFDM radar communication based on distributed compressed sensing

Abstract

This invention relates to a method for integrated target positioning using MIMO-OFDM radar communication based on distributed compressed sensing. This method utilizes a dual-base centralized MIMO radar to transmit and receive signals. The transmitted signal is an OFDM radar communication integrated signal. By designing a new radar communication integrated signal waveform, this method achieves both target positioning and data communication. Furthermore, to better utilize inter-element signal correlation, distributed compressed sensing technology is employed to process the radar communication integrated echo signal. By sampling the echo signal at a rate far below the Nyquist theorem, when the signal is sparse or compressible, only a small number of samples can be used to accurately or approximately reconstruct the original signal, significantly reducing computational complexity.

Description

Technical Field

[0001] The present invention belongs to the field of radar communication technology, and in particular relates to a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing. Background Art

[0002] With the advancement of information technology, systems such as multi-function radars, smart cars, and 5G antennas are becoming increasingly complex. The demand for both radar detection and wireless communication capabilities is increasing, leading to widespread application of radar and communications equipment in both military and civilian applications. To achieve these two functions, the traditional approach is to simply integrate radar and communications equipment independently, allowing each to perform its own function. However, due to a lack of unified scheduling and management, this subsystem approach often hinders overall system performance due to issues such as energy consumption, size, and spectrum interference.

[0003] Existing technologies primarily target target positioning using traditional radar signals, but fail to consider integrating radar and communication functions into a single signal waveform to achieve both target positioning and data communication. Furthermore, conventional signal processing, based on the Nyquist sampling theorem, requires a sampling rate twice the maximum frequency of the original signal. In actual engineering, this multiple is much higher, typically reaching four or five times. Therefore, when dealing with wide-bandwidth signals like millimeter waves, recovering the original signal requires acquiring a large amount of sampled data, which presents numerous challenges. On the one hand, complete data sampling is unnecessary for common signal processing methods, resulting in a significant amount of redundant information in the received echo data. Furthermore, high-speed sampling receivers are relatively expensive, resulting in a significant waste of hardware system resources. On the other hand, high-rate sampling directly increases the amount of sampled data, posing significant challenges in subsequent transmission and storage. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0005] The present invention provides a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, which uses a dual-base centralized MIMO radar to realize signal transmission and reception, wherein the transmission signal is an OFDM radar communication integrated signal, and the waveform of the OFDM radar communication integrated signal contains N p pulses, each pulse consists of N s OFDM symbols, where the number of subcarriers in an OFDM symbol is N c, the subcarrier spacing is Δf, and the signal length of an OFDM is T s , the pulse repetition period is T p ;

[0006] The method comprises:

[0007] Acquire the received signal of the bistatic centralized MIMO radar;

[0008] Constructing a joint sparse positioning model, sparsely representing the received signal, and selecting a measurement matrix to compress the sparse signal to obtain a compressed signal;

[0009] The compressed signal is reconstructed using a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, thereby achieving target positioning.

[0010] In one embodiment of the present invention, the OFDM radar communication integrated signal is represented as:

[0011]

[0012] Where, d p,s,n represents the communication information carried by the nth subcarrier in the pth pulse and the sth OFDM symbol, c represents the speed of light, Represents a rectangular window function, when 0≤t≤T s When , its value is 1, otherwise it is 0, the total bandwidth of the signal B=N c Δf.

[0013] In one embodiment of the present invention, the bistatic centralized MIMO radar includes N T transmit antennas and N R The receiving antennas are spaced d apart. T and d R ,and

[0014] The received signal of the bistatic centralized MIMO radar is expressed as:

[0015]

[0016] Where K represents the number of targets, σ k represents the reflection coefficient of the kth target, represents the receiving steering vector of the kth target, represents the arrival angle of the kth target, a(θ k ) The launch guidance vector of the kth target, θ k represents the departure angle of the kth target, S represents the OFDM radar communication integrated signal received by all receiving antennas, W is the additive white Gaussian noise, (·)T represents transpose;

[0017] The received signal of the nth receiving antenna of the bistatic centralized MIMO radar is expressed as:

[0018]

[0019] Among them, w n represents the additive white Gaussian noise received by the nth receiving antenna, and s represents the OFDM radar communication integrated signal received by the nth receiving antenna.

[0020] In one embodiment of the present invention, a joint sparse positioning model is constructed to sparsely represent the received signal, and a measurement matrix is ​​selected to compress the sparse signal to obtain a compressed signal, including:

[0021] The plane where the MIMO radar is located is divided into L1×L2 angular positions, expressed as:

[0022]

[0023] Where θ represents the departure angle, represents the angle of arrival;

[0024] Define the reflection coefficient and sparse basis in the plane, where the reflection coefficient is:

[0025]

[0026] The position set consisting of all angles of the plane is used as the selected angle domain sparse basis. Under this angle domain sparse model, the received signal of the nth receiving antenna is expressed as:

[0027]

[0028] The sparse basis of the nth receiving antenna in the joint positioning sparse model is:

[0029]

[0030] According to the sparse basis and the reflection coefficient, the received signal of the nth receiving antenna is expressed as:

[0031] r n =(Ψ n σ n ) T ;

[0032] σ n =[σ 11 σ 12 … σ l1l2 … σ L1L2 ];

[0033] Where, σ n Indicates the received signal r n In the sparse basis n The sparse vector under ;

[0034] A Gaussian random matrix is ​​selected as the measurement matrix. The received signal of each receiving antenna is projected onto the measurement matrix to obtain the corresponding compressed signal. The compressed signal corresponding to the received signal of the nth receiving antenna is expressed as:

[0035]

[0036] Where, Φ n represents the measurement matrix, Θ n represents the perception matrix, Θ n =Φ n Ψ n .

[0037] In one embodiment of the present invention, a joint sparse reconstruction algorithm is used to reconstruct the compressed signal to obtain a target position estimation result and a sparse vector estimation result of the signal to achieve target positioning, including:

[0038] Using the joint sparse reconstruction algorithm, the compressed signal corresponding to each receiving antenna is iterated cyclically to obtain the corresponding target position estimation result and sparse vector estimation result;

[0039] The target position estimation results and sparse vector estimation results corresponding to all receiving antennas are averaged to obtain the target position estimation results and sparse vector estimation results of the signal to achieve target positioning.

[0040] In one embodiment of the present invention, for the compressed signal y corresponding to the nth receiving antenna n , the cyclic iterative process of the joint sparse reconstruction algorithm includes:

[0041] Step a: Initialize the parameters of the joint sparse reconstruction algorithm, let t = 1, where r n,t Represents the compressed signal y n The difference after the tth iteration, represents the empty set, P n,t represents the column index determined after each iteration, Ω n,t Represents the compressed signal y n The index set after t iterations, where t represents the number of iterations;

[0042] Step b: Calculate the perception matrix Θ according to the following formula n and the difference r n,t The correlation coefficient vector of :

[0043]

[0044] In the formula, <·> represents the inner product of vectors, and |·| represents the absolute value;

[0045] Step c: Select the correlation coefficient vector u t The column index p corresponding to the maximum value in n,t is added to the column index P n,t in, and Ω n,t and Θ

[0046] Ω n,t = Ω n,t-1 ∪P n,t , Θ n,t = Θ n,t-1 ∪γ p ;

[0047] Among them, Θ n,t represents the column set selected from the sensing matrix Θ n,t according to the index Ω n [[ID=P=39]] p p γ n,t represents the p-th column of the sensing matrix Θ

[0048] Step d: Calculate y according to the following formula n = Θ n,t σ n,t The least-squares solution is:

[0049]

[0050] Step e: Calculate a new measurement signal value according to the obtained σ n,t Update the difference r according to the new measurement signal value n,t is

[0051]

[0051] Step f: Let t = t + 1. If t < K, return to step b to continue the iteration. If t ≥ K, stop the iteration. The index set Ω n,t after the iteration is used as the target position estimation result Ω <to= n of the compressed signal y n , and the σ n,t calculated during the iteration is stored in the set to obtain the sparse vector estimation result n of the compressed signal y Among them, K represents the sparsity of the signal.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention's distributed compressed sensing-based target positioning method for integrated MIMO-OFDM radar communication combines the current OFDM communication frame structure to design a new OFDM radar communication integrated signal. This method not only increases the data rate of the transmitted signal but also makes the designed signal more similar to the communication frame structure, thereby facilitating synchronization. Furthermore, considering the dual-base MIMO-OFDM radar communication integrated system, the proposed reconstruction algorithm based on distributed compressed sensing can significantly reduce the amount of data required for echo sampling and accurately obtain target location information.

[0054] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic diagram of a traditional OFDM radar signal provided by an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of an OFDM radar communication integrated signal provided by an embodiment of the present invention;

[0057] Figure 3 is a target positioning schematic diagram provided by an embodiment of the present invention;

[0058] Figure 4 Schematic diagram of a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing provided by an embodiment of the present invention;

[0059] Figure 5 is a schematic diagram of an actual target position provided by an embodiment of the present invention;

[0060] Figure 6 is a schematic diagram of an estimated target position provided by an embodiment of the present invention;

[0061] Figure 7 is the reconstruction probability of the algorithm provided by the embodiment of the present invention;

[0062] Figure 8 This is a graph showing how the relative positioning error of a target changes with the signal-to-noise ratio using the distributed compressed sensing method and the traditional compressed sensing method provided by an embodiment of the present invention;

[0063] Figure 9 This is a communication bit error rate curve diagram of the OFDM radar communication integrated signal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing proposed in accordance with the present invention, in combination with the accompanying drawings and specific implementation methods.

[0065] The aforementioned and other technical contents, features, and effects of the present invention are clearly presented in the following detailed description of the specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the intended purpose can be obtained. However, the accompanying drawings are provided for reference and illustration purposes only and are not intended to limit the technical solutions of the present invention.

[0066] Example 1

[0067] The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method of this embodiment utilizes a dual-base centralized MIMO radar to transmit and receive signals, wherein the transmitted signal is an OFDM radar communication integrated signal.

[0068] Orthogonal Frequency Division Multiplexing (OFDM) signals are widely used due to their suitability for high-speed data transmission, high spectrum utilization, and strong anti-fading capabilities. However, in traditional methods, OFDM signals have significant differences in communication and radar applications, and their waveform designs are also different. Therefore, it is necessary to design an integrated signal based on the requirements of the millimeter-wave radar communication integrated system.

[0069] See Figure 1 , Figure 1 This is a schematic diagram of a traditional OFDM radar signal provided by an embodiment of the present invention. In a traditional OFDM radar signal, each pulse transmits only one OFDM symbol. This limits the data rate of the transmitted signal and significantly differs from the communication frame structure, making it incompatible with existing data link networks.

[0070] To this end, the present invention designs an OFDM radar communication integrated signal, such as Figure 2The diagram shows an integrated OFDM radar communication signal provided by an embodiment of the present invention. This OFDM radar communication signal uses a pulse waveform, where each pulse is composed of multiple OFDM signals. This improves the signal's data transmission rate within the same bandwidth. Furthermore, a pulse composed of multiple OFDM signals can be treated as a single time slot in communication, making it more similar to a communication signal. Therefore, communication can be completed within a single pulse, and synchronization is easier to achieve than with traditional OFDM radar waveforms.

[0071] In this embodiment, it is assumed that the waveform of the OFDM radar communication integrated signal contains N p pulses, each pulse consists of N s OFDM symbols, where the number of subcarriers in an OFDM symbol is N c , the subcarrier spacing is Δf, and the signal length of an OFDM is T s , the pulse repetition period is T p , then the OFDM radar communication integrated signal is expressed as:

[0072]

[0073] Where, d p,s,n represents the communication information carried by the nth subcarrier in the pth pulse and the sth OFDM symbol, c represents the speed of light, Represents a rectangular window function, when 0≤t≤T s When , its value is 1, otherwise it is 0, the total bandwidth of the signal B=N c Δf.

[0074] The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method of this embodiment uses a distributed compressed sensing method to achieve target positioning. First, the distributed compressed sensing method is described.

[0075] The distributed compressed sensing (DCS) method is to achieve joint compression and reconstruction of multiple signals by establishing a joint sparse model (JSM) and utilizing the correlation between signals and between signals. Compared with traditional compressed sensing methods, it can save a certain number of measurement points and improve measurement accuracy. The DCS method is based on the joint sparsity of the signal group. In the joint sparse model adopted by the present invention, there is no common part in the signal group. All signals can be represented by the same sparse basis, and the sparsity of each signal is the same, but with different coefficients. The signals in the signal group can be represented as:

[0076] x j =Ψsj ,j∈{1,2,…,J} (2);

[0077] If x j The sparsity is K, and the set Ω is used to represent the sparse vector s j The index position of the non-zero element in , then ||Ω||0=K. In this joint sparse model, Ω is the same for all signals, so Ω is also called the sparse structure of the signal group.

[0078] A practical situation simulated by this model is that the antenna array receives signals reflected by the target at the same time. Due to the multipath effect, the signals have different phase shifts and channel fading in spatial propagation, but they are sparse in sparse bases such as the frequency domain and the angle domain.

[0079] Further, the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing of this embodiment is described in detail. Figure 4 FIG. 1 is a schematic diagram of a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, the method comprising:

[0080] Step 1: Obtain the received signal of the bistatic centralized MIMO radar;

[0081] In this embodiment, the bistatic centralized MIMO radar includes N T transmit antennas and N R The receiving antennas are spaced d apart. T and d R ,and

[0082] Assume that there are K targets in the far field, and the azimuth of the kth target is k=1,2,…,K. Where θ is DOD, is DOA. In both the transmitting and receiving antenna arrays, the first antenna is used as the reference unit. Then:

[0083] The launch guidance vector of the kth target is:

[0084]

[0085] The receiving steering vector of the kth target is:

[0086]

[0087] Then, the received signal of the bistatic centralized MIMO radar is expressed as:

[0088]

[0089] Where K represents the number of targets, σ k represents the reflection coefficient of the kth target, represents the receiving steering vector of the kth target, represents the arrival angle of the kth target, S(θ k ) The launch guidance vector of the kth target, θ k represents the departure angle of the kth target, S represents the OFDM radar communication integrated signal received by all receiving antennas, W is the additive white Gaussian noise, (·) T Indicates transpose.

[0090] The received signal of the nth receiving antenna of the bistatic centralized MIMO radar is expressed as:

[0091]

[0092] Among them, w n represents the additive white Gaussian noise received by the nth receiving antenna, and s represents the OFDM radar communication integrated signal received by the nth receiving antenna.

[0093] It should be noted that in a centralized MIMO-OFDM radar system, the data received by each receiving antenna is the echo data after being reflected by the same target. If the angular space of the plane where the transmitting antenna and the receiving antenna are located is regarded as a sparse basis, the received signal satisfies the joint sparse model.

[0094] Step 2: Build a joint sparse positioning model, sparsely represent the received signal, select a measurement matrix to compress the sparse signal, and obtain a compressed signal;

[0095] In this embodiment, step 2 includes:

[0096] Step 2.1: Build a joint sparse positioning model to sparsely represent the received signal;

[0097] In this embodiment, the two-dimensional plane composed of the Direction of Departure (DOD) and the Direction of Arrival (DOA) is regarded as a grid structure, that is, the divided plane is regarded as consisting of DODs and DOAs, such as Figure 3 As shown, the plane where the MIMO radar is located is divided into L1×L2 angular positions, expressed as:

[0098]

[0099] Where θ represents the departure angle, represents the angle of arrival.

[0100] Step 2.2: Define the reflection coefficient and sparse basis in the plane, where the reflection coefficient is:

[0101]

[0102] Assuming that the target is an ideal point target in space, since the angle of each grid is known, the position of the target in the two-dimensional plane can be expressed by a matrix (the matrix element 0 means there is no target, and the reflection coefficient is Matrix elements that are not 0 indicate the presence of a target, and the reflection coefficient

[0103] The position set consisting of all angles of the plane is used as the selected angle domain sparse basis. Under this angle domain sparse model, the received signal of the nth receiving antenna is expressed as:

[0104]

[0105] The sparse basis of the nth receiving antenna in the joint positioning sparse model is:

[0106]

[0107] Step 2.3: Based on the sparse basis and the reflection coefficient, the received signal of the nth receiving antenna is expressed as:

[0108] r n =(Ψ n σ n ) T (11);

[0109]

[0110] Where, σ n Indicates the received signal r n In the sparse basis n The sparse vector below.

[0111] In this embodiment, σ n The non-zero elements ρ k The index is the angular position of the target.

[0112] It should be noted that since the transmitted OFDM radar communication integrated signal waveform is millimeter wave, its wavelength is relatively small, and the array spacing is generally taken as half a wavelength. This will make the spacing between each array element in the centralized MIMO radar relatively small, so the reflection coefficient of the same target in each array element can be regarded as the same.

[0113] Step 2.4: Select a Gaussian random matrix as the measurement matrix and project the received signal of each receiving antenna onto the measurement matrix to obtain the corresponding compressed signal. The compressed signal corresponding to the received signal of the nth receiving antenna is expressed as:

[0114]

[0115] Where, Φ n represents the measurement matrix, Θ n represents the perception matrix, Θ n =Φ n Ψ n .

[0116] Step 3: Use the joint sparse reconstruction algorithm to reconstruct the compressed signal to obtain the target position estimation result and sparse vector estimation result of the signal to achieve target positioning.

[0117] In an optional embodiment, step 3 includes:

[0118] Step 3.1: Use the joint sparse reconstruction algorithm to iterate the compressed signal corresponding to each receiving antenna to obtain the corresponding target position estimation result and sparse vector estimation result;

[0119] In this embodiment, for the compressed signal y corresponding to the nth receiving antenna n , the input of the joint sparse reconstruction algorithm includes: M×N dimensional perception matrix Θ n , M×1 dimensional compressed signal y n And the sparsity of the signal K. The output results include: the target position estimation result Ω of the signal n And sparse vector estimation results The specific iterative process includes:

[0120] Step a: Initialize the parameters of the joint sparse reconstruction algorithm, let t = 1, where r n,t Represents the compressed signal y n The difference after the tth iteration, represents the empty set, P n,t represents the column index determined after each iteration, Ω n,t Represents the compressed signal y n The index set after t iterations, where t represents the number of iterations;

[0121] Step b: Calculate the perception matrix Θ according to the following formula n and the difference r n,t The correlation coefficient vector of :

[0122]

[0123] In the formula, <·> means finding the inner product of vectors, |·| means finding the absolute value;

[0124] Step c: Select the correlation coefficient vector u t The column number p corresponding to the maximum value in is added to the column index P n,t In the update Ω n,t and Θ n,t for:

[0125] Ω n,t =Ω n,t-1 ∪P n,t ,Θ n,t =Θ n,t-1 ∪γ p (15);

[0126] Among them, Θ n,t Indicates that according to index Ω n,t From the perception matrix Θ n The set of columns selected from , γ p Represents the perception matrix Θ n The pth column of

[0127] In step c, the correlation coefficient vector u t The column corresponding to the maximum value in represents the perception matrix Θ n Mean and difference r n,t The column with the strongest correlation, the correlation coefficient vector u t The position of the maximum value p is stored in P n,t In the container, each iteration P n,t A number will be added and the P found will be n,t Deposit Ω n,t At the same time, in the perception matrix Θ n The column with the strongest correlation found is stored in Θ n,t .

[0128] In this embodiment, the designed Ω n,t The length is L1*L2, Ω n,t The initial values ​​are all 0, and P n,t Is a number corresponding to a position on a grid, stored in Ω n,t After that, Ω n,t The 0 at the corresponding position becomes 1.

[0129] Step d: Calculate y according to the following formula n =Θ n,t σ n,t The least squares solution of is:

[0130]

[0131] Step e: According to the obtained σ n,t Calculate a new measured signal value Update the difference r according to the new measured signal value n,t For

[0132] Step f: Let t = t + 1. If t < K, return to step b to continue the iteration. If t ≥ K, stop the iteration, and use the index set Ω n,t As the estimated result of the target position of the compressed signal y n Ω n During the iteration process, store the calculated σ n,t Into the set to obtain the estimated result of the sparse vector of the compressed signal y n Where K represents the sparsity of the signal. In this embodiment, when the algorithm runs K times, the algorithm loop of a receiving antenna ends. At this time, K position information is obtained, that is, there are K 1s in Ω

[0133] And the rest of the values are 0. At these K positions, store the corresponding values of the calculated σ n Into the set to obtain the estimated result of the sparse vector n,t

[0134] Step 3.2: For the estimated results of the target positions and the sparse vector estimated results corresponding to all receiving antennas, calculate the average to obtain the estimated results of the target position and the sparse vector of the signal, and achieve target positioning.

[0135] In this embodiment, by calculating the average of the estimated results of the target positions and the sparse vector estimated results corresponding to all receiving antennas, the estimated target position can be made more accurate.

[0136] The MIMO-OFDM radar communication integrated target positioning method based on distributed compressive sensing of the present invention combines the current OFDM communication frame structure, designs a new OFDM radar communication integrated signal, which not only improves the data rate of the transmitted signal, but also makes the designed signal more similar to the communication frame structure, so it is easier to achieve synchronization. And considering the bistatic MIMO-OFDM radar communication integrated system, the proposed reconstruction algorithm based on distributed compressive sensing can greatly reduce the amount of data required for echo sampling and accurately obtain the position information of the target.

[0137] Embodiment 2

[0138] In this embodiment, the effect of the MIMO-OFDM radar communication integrated target positioning method based on distributed compressive sensing in Embodiment 1 is illustrated through simulation experiments. The simulation parameters are shown in Table 1.

[0139] Table 1. MIMO-OFDM radar communication integrated system simulation parameters

[0140]

[0141]

[0142] See Figure 5 and Figure 6 , Figure 5 is a schematic diagram of an actual target position provided by an embodiment of the present invention; Figure 6 Figure 2 is a schematic diagram of the estimated target position provided by an embodiment of the present invention. The actual coordinates of the target can also be calculated using the positions of the MIMO transmitting and receiving radar stations. These two figures demonstrate that the distributed compressed sensing method used to sample and process the echo signals of the integrated MIMO-OFDM radar communication system can accurately determine the target's actual position. Furthermore, only a small number of sampling points is used during the simulation, significantly reducing the amount of data compared to traditional Nyquist sampling, significantly lowering computational complexity.

[0143] See Figure 7 The reconstruction probability of the algorithm provided by the embodiment of the present invention shown in the figure shows that when the sparsity of the signal is constant, its reconstruction probability will increase with the increase in the number of measurement samples; when the reconstruction probability is constant, the number of measurement samples required to restore the signal with greater sparsity to the corresponding reconstruction probability will also increase, which is in line with theoretical expectations.

[0144] See Figure 8 The embodiment of the present invention shown provides a method for further reducing the target positioning reconstruction time, that is, after expanding the DOA grid spacing, a graph of the change in target relative positioning error with the signal-to-noise ratio using distributed compressed sensing and traditional compressed sensing methods. During the simulation process of this graph, 100 Monte Carlo experiments were performed at each signal-to-noise ratio, and then the average value of the relative positioning error was calculated. Through simulation, it can be found that the robustness of the traditional compressed sensing method in this case is poor, and the estimated value often deviates greatly from the actual value. The proposed algorithm based on distributed compressed sensing makes full use of the correlation between and within signals, and the reconstructed target position is more robust, and more accurate target position information can be obtained in practical applications.

[0145] See Figure 9 The communication bit error rate curve of the OFDM radar communication integrated signal provided by an embodiment of the present invention is shown. As the signal-to-noise ratio increases, the communication bit error rate of the OFDM integrated signal decreases; and as the signal modulation order increases, the communication bit error rate also increases, both of which are in line with theoretical expectations.

[0146] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element.

[0147] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, characterized in that: The dual-base centralized MIMO radar is used to transmit and receive signals, wherein the transmitted signal is an OFDM radar communication integrated signal, and the waveform of the OFDM radar communication integrated signal contains N p pulses, each pulse consists of N s OFDM symbols, where the number of subcarriers in an OFDM symbol is N c , the subcarrier spacing is Δf, and the signal length of an OFDM is T s , the pulse repetition period is T p ; The method comprises: Acquire the received signal of the bistatic centralized MIMO radar; Constructing a joint sparse positioning model, sparsely representing the received signal, and selecting a measurement matrix to compress the sparse signal to obtain a compressed signal; The compressed signal is reconstructed using a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, thereby achieving target positioning.

2. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to claim 1 is characterized in that: The OFDM radar communication integrated signal is expressed as: Where, d p,s,n represents the communication information carried by the nth subcarrier in the pth pulse and the sth OFDM symbol, c represents the speed of light, Represents a rectangular window function, when 0≤t≤T s When , its value is 1, otherwise it is 0, the total bandwidth of the signal B=N c Δf.

3. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to claim 1 is characterized in that: The bistatic centralized MIMO radar includes N T transmit antennas and N R The receiving antennas are spaced d apart. T and d R ,and The received signal of the bistatic centralized MIMO radar is expressed as: Where K represents the number of targets, σ k represents the reflection coefficient of the kth target, represents the receiving steering vector of the kth target, represents the arrival angle of the kth target, a(θ k ) The launch guidance vector of the kth target, θ k represents the departure angle of the kth target, S represents the OFDM radar communication integrated signal received by all receiving antennas, W is the additive white Gaussian noise, (·) T represents transpose; The received signal of the nth receiving antenna of the bistatic centralized MIMO radar is expressed as: Among them, w n represents the additive white Gaussian noise received by the nth receiving antenna, and s represents the OFDM radar communication integrated signal received by the nth receiving antenna.

4. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to claim 3 is characterized in that: Constructing a joint sparse positioning model, sparsely representing the received signal, and selecting a measurement matrix to compress the sparse signal to obtain a compressed signal, including: The plane where the MIMO radar is located is divided into L1×L2 angular positions, expressed as: Where θ represents the departure angle, represents the angle of arrival; Define the reflection coefficient and sparse basis in the plane, where the reflection coefficient is: The position set consisting of all angles of the plane is used as the selected angle domain sparse basis. Under this angle domain sparse model, the received signal of the nth receiving antenna is expressed as: The sparse basis of the nth receiving antenna in the joint positioning sparse model is: According to the sparse basis and the reflection coefficient, the received signal of the nth receiving antenna is expressed as: r n =(Ψ n s n ) T ; Where, σ n Indicates the received signal r n In the sparse basis n The sparse vector under ; A Gaussian random matrix is ​​selected as the measurement matrix. The received signal of each receiving antenna is projected onto the measurement matrix to obtain the corresponding compressed signal. The compressed signal corresponding to the received signal of the nth receiving antenna is expressed as: Where, Φ n represents the measurement matrix, Θ n represents the perception matrix, Θ n =Φ n Ψ n .

5. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to claim 4 is characterized in that: The compressed signal is reconstructed using a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal to achieve target positioning, including: Using the joint sparse reconstruction algorithm, the compressed signal corresponding to each receiving antenna is iterated cyclically to obtain the corresponding target position estimation result and sparse vector estimation result; The target position estimation results and sparse vector estimation results corresponding to all receiving antennas are averaged to obtain the target position estimation results and sparse vector estimation results of the signal to achieve target positioning.

6. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to claim 5 is characterized in that: For the compressed signal y corresponding to the nth receiving antenna n , the cyclic iterative process of the joint sparse reconstruction algorithm includes: Step a: Initialize the parameters of the joint sparse reconstruction algorithm, let r n,t =y n , t = 1, where r n,t Represents the compressed signal y n The difference after the tth iteration, represents the empty set, P n,t represents the column index determined after each iteration, Ω n,t Represents the compressed signal y n The index set after t iterations, where t represents the number of iterations; Step b: Calculate the perception matrix Θ according to the following formula n and the difference r n,t The correlation coefficient vector of : In the formula, <·> means finding the inner product of vectors, |·| means finding the absolute value; Step c: Select the correlation coefficient vector u t The column number p corresponding to the maximum value in is added to the column index P n,t In the update Ω n,t and Θ n,t for: Oh n,t =Oh n,t-1 ∪P n,t ,I n,t =Θ n,t-1 ∪γ p ; Among them, Θ n,t Indicates that according to index Ω n,t From the perception matrix Θ n The set of columns selected from , γ p Represents the perception matrix Θ n,t The pth column of Step d: Calculate y according to the following formula n =Θ n,t σ n,t The least squares solution of is: Step e: According to the solved σ n,t Calculate the new measurement signal value Update the difference value r according to the new measurement signal value n,t for Step f: Let t = t + 1. If t < K, return to step b for continued iteration. If t ≥ K, stop the iteration, and use the index set Ω after the iteration is completed n,t as the estimated result of the target position Ω n of the compressed signal y n , and store the σ calculated during the iteration n,t into the set to obtain the estimated result of the sparse vector of the compressed signal y n . Among them, K represents the sparsity of the signal.

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