MIMO-SAR echo separation method based on image post-processing in complex detection scenarios

Abstract

This invention discloses a MIMO-SAR echo separation method based on image post-processing in complex detection scenarios, belonging to the field of signal processing. The method includes: designing the MIMO-SAR transmit waveform and collecting echo signals using a multi-channel elevation system; performing range and azimuth compression sequentially on the received data from the multi-channel elevation system using a classic imaging algorithm to complete imaging processing; constructing an elevation-dimensional spatial snapshot signal based on the processed multi-channel data and estimating the sample covariance matrix using range samples; performing eigenvalue decomposition on the covariance matrix to estimate the number of sources in the spatial domain; estimating the angle of arrival (AHA) of the sources using the MUSIC algorithm, given a fixed number of sources; calculating the optimal weight vector based on the least squares criterion and processing the spatial snapshot to suppress interference signals in the echo, thus solving the echo separation problem in MIMO-SAR. This method can effectively mitigate the impact of AHA mismatch caused by terrain undulations on echo separation performance and improve the imaging performance of the MIMO-SAR system in complex detection scenarios.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a MIMO-SAR echo separation method based on image post-processing in complex detection scenarios. Background Technology

[0002] Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) combines the principles of MIMO with SAR technology, representing a significant advancement in radar technology. Over the past few decades, SAR has garnered widespread attention for its comprehensive global environmental monitoring capabilities. However, traditional SAR architectures relying on single-transmit or single-receive antennas often face limitations in data acquisition speed, resolution, and adaptability to dynamic environments. To address these issues, MIMO-SAR systems, by deploying multiple transmit and receive antennas, possess virtual aperture expansion capabilities and flexible imaging modes, significantly improving SAR's capabilities in urban monitoring, environmental assessment, and disaster management.

[0003] While the new MIMO-SAR system holds immense potential, it also faces several challenges, such as increased complexity in system design and data processing. Furthermore, due to the deployment characteristics of the transmit and receive antennas, a single receiver will simultaneously receive echo signals from all transmitted waveforms. Since perfectly orthogonal and frequency-matching transmitted waveforms do not exist simultaneously, the resulting cross-correlation energy between waveforms accumulates in the imaging scene, severely degrading imaging performance. Therefore, effectively addressing the echo separation problem in MIMO-SAR is crucial for the engineering application of MIMO-SAR systems.

[0004] To address the echo separation problem in MIMO-SAR systems, existing literature has conducted extensive theoretical research and experimental verification. To provide more azimuth degrees of freedom, a popular MIMO-SAR echo separation scheme is the elevation-dimensional beamforming echo separation scheme based on partially orthogonal waveforms. This scheme suppresses near-end interference through the partially orthogonal characteristics of the waveform, while far-end interference needs to be suppressed through elevation-dimensional beamforming (DBF). However, the processing performance of this scheme depends on the DBF performance. In real-world MIMO-SAR detection scenarios, terrain undulations often exist, causing the actual DOA of signal components to deviate from the theoretical value. This DOA mismatch severely degrades DBF processing performance. To address this issue, traditional robust DBF techniques employ various DOA estimation methods to estimate the incident angle of signal components in the spatial domain. However, during SAR detection, azimuth pulse spread means that the DOA of the desired signal and interference components in a given processing unit is not uniquely determined. In this case, the optimal weight vector calculated based on the DOA estimation results cannot apply a distortion-free response to the desired signal at all angles while simultaneously suppressing interference components. Furthermore, existing literature proposes applying distortion-free response and nulls upwards to all possible components of the desired signal and interference by widening the main lobe and null width of the beam pattern. Such methods can further improve the echo separation performance of MIMO-SAR under terrain undulations, but require significant elevation-dimensional system resources. When MIMO-SAR system resources are insufficient, the processing performance of such methods is also limited. Summary of the Invention

[0005] This invention provides a MIMO-SAR echo separation method based on image post-processing for complex detection scenarios. Image post-processing addresses the impact of azimuth pulse spread on DOA mismatch, and the MUSIC algorithm estimates the incident angle of each signal component in the processing unit, thereby mitigating the impact of terrain undulations on MIMO-SAR echo separation performance. This method avoids the impact of azimuth pulse spread on DOA mismatch in complex detection scenarios, simplifies the signal model, and improves DOA estimation accuracy and MIMO-SAR echo separation performance.

[0006] This invention provides a MIMO-SAR echo separation method based on image post-processing in complex detection scenarios, comprising the following steps:

[0007] Step S1: Design the transmit waveform of the MIMO-SAR system and use an elevation multi-channel system to collect echo signals;

[0008] Step S2: The imaging algorithm is used to compress the echo signals received by the elevation multi-channel system in the range and azimuth directions in sequence to complete the imaging processing.

[0009] Step S3: Construct a pitch-dimensional spatial snapshot signal based on the multi-channel data after imaging processing, and select the sample covariance matrix estimated from the range-direction samples;

[0010] Step S4: Perform eigenvalue decomposition on the sample covariance matrix to estimate the number of information sources in the spatial domain;

[0011] Step S5: Given a certain number of information sources, the MUSIC algorithm is used to estimate the angle of arrival of the information sources.

[0012] Step S6: Calculate the optimal weight vector based on the least squares criterion, and process the spatial snapshot in step S3 to suppress interference signals in the echo.

[0013] Optionally, in one embodiment of the present invention, step S1 specifically includes:

[0014] A partially orthogonal waveform is used as the transmitted waveform of the MIMO-SAR system. The transmitted waveform s m (t) is:

[0015]

[0016] In the formula, t is the fast time, and M is the number of transmitted waveforms. γ is the modulation vector. i Modulation vector The label of the element that is 1, () T For the transpose operation, m is the transmitted waveform index, i is the sub-pulse index, s0(t) represents a signal basis consisting of a set of sub-pulses, and T p The pulse width;

[0017]

[0018]

[0019]

[0020] In the formula, s(t) is a conventional linear frequency modulated signal, rect(t) is a rectangular window function, and the modulation vector is... It is a unit vector, and the γth... i Each element is 1. To ensure that all transmitted waveforms have the same bandwidth, the modulation vector... satisfy:

[0021]

[0022] In the formula, j is the sub-pulse number;

[0023] In a MIMO-SAR system, the same receiver will receive echo signals from all transmitted waveforms. At this time, the echo signal r from the nth receiving channel...n (t,η) is represented as:

[0024]

[0025] In the formula,

[0026] h n (t,η)=[h n,1 (t,η),h n,2 (t,η),…,h n,M (t,η)] T

[0027]

[0028] In the formula, h n,m (t,η) and r n,m (t, η) represent the channel response and echo signal corresponding to the transmission of the m-th transmitter and the reception of the n-th receiver, respectively. η and η represent fast-time convolution operations and slow-time convolution operations, respectively.

[0029] Optionally, in one embodiment of the present invention, in step S2, imaging processing is performed using a range-Doppler algorithm, and range compression is performed using matched filtering.

[0030] Optionally, in one embodiment of the present invention, step S3 specifically includes:

[0031] When a MIMO-SAR system simultaneously transmits M different waveforms, there are at most 2M-1 signal components. In this case, the elevation-dimensional spatial snapshot is represented as:

[0032]

[0033] In the formula, σ(θ) q ) and v(θ) q ) represent the amplitude and guide vector of the signal component q, respectively, and n is the noise guide vector. Represents echo data r n The imaging result of (t,η), where N is the number of elevation dimension receiving channels;

[0034] Sample covariance matrix Represented as:

[0035]

[0036] In the formula, L represents the number of distance-oriented samples selected, (·) H This is the conjugate transpose operation. Quick shot of the airspace.

[0037] Optionally, in one embodiment of the present invention, step S4 specifically includes:

[0038] The sample covariance matrix after eigenvalue decomposition is:

[0039]

[0040] In the formula,

[0041] A = [v(θ0), v(θ1), ..., v(θ)] K-1 )]

[0042] Λ S+I =diag[λ0,λ1,…,λ K-1 ]

[0043] Λ n =diag[λ K ,λ K+1 ,…,λ N-1 ]

[0044] In the formula, R S+I The signal component covariance matrix, Let I be the noise power, I be the identity matrix, and K be the number of information sources. For the complex field, U c and Λ c U is a matrix formed by eigenvectors and eigenvalues. S+I and Λ S+I U is a matrix formed by the signal's eigenvectors and eigenvalues. n and Λ n It is a matrix formed by noise eigenvectors and eigenvalues, where diag(·) is the operation to generate a diagonal matrix, and λ k For eigenvalues;

[0045] The number of information sources is obtained through the sample covariance matrix. In the matrix U formed by the eigenvectors c The projection is estimated, that is:

[0046]

[0047] Define detection factors:

[0048]

[0049] In the formula, u n Let β be the projection vector, and ||·||1 be the modulo operation. n The index n corresponding to the maximum value is the number of information sources in the spatial domain.

[0050] Optionally, in one embodiment of the present invention, step S5 specifically includes:

[0051] The angle of arrival estimation model based on the MUSIC algorithm is expressed as:

[0052]

[0053] In the formula, the set Represents all possible angles of arrival (OA) values ​​for each signal component, for The estimated angle of arrival (AOA) for each signal component is expressed as follows:

[0054]

[0055] In the formula, The angle of incidence is denoted as .

[0056] Optionally, in one embodiment of the present invention, step S6 specifically includes:

[0057] When the incident angle of the signal component is θ q At that time, the signal guidance vector is represented as:

[0058]

[0059] μ p (θ)=2πpd sinθ / λ, p=0,1,…,N-1

[0060] In the formula, μ p (θ) represents the phase component, p is the receiver array index, and d and λ are the channel spacing and operating wavelength, respectively. The optimal weight vector is calculated using the least squares criterion, and the specific calculation process is as follows:

[0061]

[0062] In the formula, W LS For a weighted vector matrix, use weight vectors Weighted processing is performed on the elevation-dimensional spatial snapshot signal to extract the incident angle. The corresponding signal components are suppressed while other signal components are suppressed, ultimately achieving MIMO-SAR echo separation.

[0063] The MIMO-SAR echo separation method based on image post-processing in complex detection scenarios of this invention performs echo separation processing in the image domain, which can avoid the influence of azimuth pulse spread on DOA mismatch, simplify the signal model, improve DOA estimation accuracy, and thus reduce the impact of terrain undulations in the detection scenario on MIMO-SAR echo separation performance. It has good robustness and broad application prospects in practical processing.

[0064] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0065] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 This is a flowchart of a MIMO-SAR echo separation method based on image post-processing in a complex detection scenario according to an embodiment of the present invention;

[0067] Figure 2(a) shows the relationship between the height and azimuth of the point target at different slant distances in an embodiment of the present invention;

[0068] Figure 2(b) shows the relationship between the angle of arrival error and azimuth position of the point target at different slant distances in an embodiment of the present invention.

[0069] Figures 3(a) and 3(b) show the range and azimuth imaging performance of point target 1 at a slant distance of 10 km according to an embodiment of the present invention, respectively.

[0070] Figures 3(c) and 3(d) show the range and azimuth imaging performance of point target 2 at a slant distance of 10 km according to an embodiment of the present invention, respectively.

[0071] Figures 3(e) and 3(f) show the range and azimuth imaging performance of point target 3 at a slant distance of 10 km according to an embodiment of the present invention, respectively.

[0072] Figure 4(a) shows the terrain undulation model corresponding to the MIMO-SAR illumination scene in an embodiment of the present invention;

[0073] Figure 4(b) shows the angle of arrival error at different locations under the terrain undulation model of this embodiment of the invention;

[0074] Figure 5(a) is a truth diagram of the MIMO-SAR illumination scene according to an embodiment of the present invention;

[0075] Figure 5(b) shows the imaging results of the mixed signal before echo separation in an embodiment of the present invention;

[0076] Figure 5(c) shows the echo separation results using LS beamforming technology under the traditional echo separation strategy of this invention.

[0077] Figure 5(d) shows the echo separation results using SMI beamforming technology under the traditional echo separation strategy of this invention embodiment;

[0078] Figure 5(e) shows the echo separation results using LCMV beamforming technology under the traditional echo separation strategy of this invention.

[0079] Figure 5(f) shows the echo separation results using ESB beamforming technology under the traditional echo separation strategy of this invention embodiment;

[0080] Figure 5(g) shows the echo separation results using Rec-Est beamforming technology under the traditional echo separation strategy of this invention embodiment;

[0081] Figure 5(h) shows the echo separation results using LASSO beamforming technology under the traditional echo separation strategy of this invention embodiment;

[0082] Figure 5(i) shows the imaging results after processing by the proposed method according to an embodiment of the present invention. Detailed Implementation

[0083] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0084] Figure 1 This is a flowchart of a MIMO-SAR echo separation method based on image post-processing in a complex detection scenario, according to an embodiment of the present invention.

[0085] like Figure 1 As shown, the MIMO-SAR echo separation method based on image post-processing in this complex detection scenario includes the following steps:

[0086] Step S1: Design the transmit waveform of the MIMO-SAR system and use an elevation multi-channel system to collect echo signals.

[0087] This invention achieves separation of MIMO-SAR echo signals based on partially orthogonal waveforms. The partially orthogonal waveform used as the transmit waveform of the MIMO-SAR system can be considered as composed of linearly shifted or cyclically shifted sub-pulse signals. This transmit waveform can be represented by a set of signal bases s0(t), the specific form of which is:

[0088]

[0089]

[0090] The selected transmit waveform is a short-time shift orthogonal (STSO) waveform. The general form of the MIMO-SAR partially orthogonal transmit waveform can be expressed as:

[0091]

[0092] In the formula, t, M, Tp ,() T s(t) and rect(t) represent fast time, number of transmitted waveforms, pulse width, transpose operation, conventional linear frequency modulation (LFM) signal, and rectangular window function, respectively. s0(t) represents a signal basis consisting of sub-pulses, and the modulation vector is... It is a unit vector, and the γth... i Each element is 1. Furthermore, to ensure that all transmitted waveforms have the same bandwidth, Must meet:

[0093]

[0094] For the transmitted waveform s m (t), whose corresponding modulation matrix can be expressed as:

[0095]

[0096] In the formula, Represents the real number field. To ensure that the designed transmitted waveform has partial orthogonality, the modulation matrices of different transmitted waveforms must satisfy:

[0097] E i ⊙E j =0,1≤i,j≤M,i≠j

[0098] In the formula, 0 represents the zero matrix. Considering that in a MIMO-SAR system, the same receiver simultaneously receives echo signals from all transmitters, the echo signal received by receiver Rxn can be expressed as:

[0099]

[0100] In the formula,

[0101] h n (t,η)=[h n，1 (t,η),h n,2 (t,η),…,h n，M (t,η)] T

[0102]

[0103] In the formula, h n,m (t,η) and r n,m (t, η) represent the channel response and echo signal corresponding to the transmission of the m-th transmitter and the reception of the n-th receiver, respectively. η and h represent the fast-time convolution operation and the slow-time convolution operation, respectively. n,m (t,η) represents the channel response corresponding to transmitter m and receiver n, and its specific form is as follows:

[0104] h n,m (t,η)=σ0δ(tt n,m )exp(-j2πf c t n,m )

[0105] In the formula, σ0, δ(t), t n,m and f c These represent the target amplitude, impulse function, target two-way delay, and carrier frequency, respectively. Pulse compression processing of the echo signal yields:

[0106]

[0107] In the formula, () * This is a conjugate operation. and These are the desired signal component and the interference component, respectively. and β m,j The specific form is:

[0108]

[0109] In the formula, κ{E(j,:)} will return the value of i that satisfies E(j,i)=1.

[0110] Step S2: The imaging algorithm is used to compress the echo signals received by the elevation multi-channel system in the range and azimuth directions in sequence to complete the imaging processing.

[0111] This step selects the Range-Doppler (RDA) algorithm for imaging processing, and range compression can be achieved through matched filtering. Before azimuth compression, due to azimuth pulse spread, a given cell will contain desired signals and interference components from different azimuth directions. Under the influence of terrain undulations, the desired signals and interference components at each azimuth location will have different angles of arrival (DOA) values, i.e.:

[0112] Θ0θ0(α0), θ0(α1), θ0(α2),…

[0113] Θ q θ q (α0),θ q (α1),θ q (α2),…,-M+1≤q≤Ml,q≠0

[0114] In the formula, Θ0 and Θ q The DOA sets for the desired signal and interference components are θ0(α) and θ0(α) respectively. i ) and θ q (α i ) represent the desired signal and the interference component at azimuth angle α, respectively. i DOA at the location.

[0115] After azimuth compression of the echo data, the DOA of the desired signal and interference components in the processing unit can be approximated as having a unique and definite value, which can effectively simplify the signal model and avoid the influence of azimuth pulse spread on DOA mismatch.

[0116] This step uses the classic RDA algorithm to process the echo data. In step S1, the echo data has already been compressed in the range direction. In this step, after the range migration correction is completed, the azimuth direction will be further compressed. The specific process will not be described in detail.

[0117] Step S3: Construct a pitch-dimensional spatial snapshot signal based on the multi-channel data after imaging processing, and select the sample covariance matrix estimated from the range-direction samples.

[0118] Based on the transmission waveform designed in step S1, when the MIMO-SAR system transmits M types of transmission waveforms simultaneously, the processing unit can contain a maximum of 2M-1 signal components. The elevation-dimensional spatial snapshot at this time can be expressed as:

[0119]

[0120] In the formula, σ(θ) q ) and v(θ) q ) represents the amplitude of the signal component q, is the pilot vector, and n is the noise pilot vector. Represents echo data r n The imaging results of (t,η), where N is the number of elevation-dimensional receiving channels. Generally, the sample covariance matrix can be expressed as:

[0121]

[0122] In the formula, L and (·) H These represent the selected number of distance-oriented samples and the conjugate transpose operation, respectively.

[0123] Step S4: Perform eigenvalue decomposition on the sample covariance matrix to estimate the number of information sources in the spatial domain.

[0124] After eigenvalue decomposition, the covariance matrix can be further expressed as:

[0125]

[0126] In the formula,

[0127] A = [v(θ0), v(θ1), ..., v(θ)] K-1 )]

[0128] Λ S+I =diag[λ0,λ1,…,λ K-1 ]

[0129] Λ n =diag[λ K ,λ K+1 ,…,λ N-1 ]

[0130] In the formula, R S+I , I, K and These represent the signal component covariance matrix, noise power, identity matrix, number of sources, and complex domain, respectively. c and Λ c U is a matrix formed by eigenvectors and eigenvalues. S+I and Λ S+I U is a matrix formed by the signal eigenvectors and eigenvalues. n and Λ n It is a matrix formed by noise feature vectors and eigenvalues, and diag(·) is the operation to generate a diagonal matrix.

[0131] To reduce the impact of noise on the estimation of the number of information sources, the sample covariance can be denoised, i.e.:

[0132]

[0133] In the formula, γ 2 The power of the noise can be estimated from the small eigenvalues ​​generated by eigenvalue decomposition. Furthermore, the above equation can be further expressed as:

[0134]

[0135] In the formula,

[0136] Λ′ S+I =diag[λ0-γ 2 ,λ1-γ 2 ,…,λ K-1 -γ 2 ]

[0137] Λ′ n =diag[λ K -γ 2 ,λ K+1 -γ 2 ,…,λ N-1 -γ 2 ]

[0138] In this step, the number of information sources is based on R′ in U. c The projection is obtained from the projection estimation, and the projection can be calculated as follows:

[0139]

[0140] Define detection factors:

[0141]

[0142] In the formula, ||·||1 represents the modulo operation. When β n The index n corresponding to the maximum value is the number of information sources.

[0143] Furthermore, the number of information sources in the airspace can be estimated as follows:

[0144]

[0145] In the formula,

[0146] =[1,β2,…,β N-1 ] T .

[0147] Step S5: Given a certain number of information sources, the MUSIC algorithm is used to estimate the angle of arrival of the information sources.

[0148] When there are topographical undulations, the incident angle of each signal component cannot be determined based on prior information; therefore, DOA estimation is required using the sample covariance matrix. The DOA estimation model based on the MUSIC algorithm can be expressed as:

[0149]

[0150] In the formula, the set This represents all possible DOA values ​​for each signal component. (For...) For each signal component, the corresponding DOA estimate can be expressed as:

[0151]

[0152] Step S6: Calculate the optimal weight vector based on the least squares criterion, and process the spatial snapshot in step S3 to suppress interference signals in the echo and solve the echo separation problem in MIMO-SAR.

[0153] When the incident angle of the signal component is θ q When ′, the signal guidance vector can be expressed as:

[0154]

[0155] μ p (θ)=2πpd sinθ / λ,p=0,1,…,N-1

[0156] In the formula, d and λ represent the channel spacing and operating wavelength, respectively. This step uses the least squares criterion to calculate the optimal weight vector, and the specific calculation process can be expressed as follows:

[0157]

[0158] Using weight vectors By weighting the images taken in the elevation dimension of the airspace, the angle of incidence can be extracted. The corresponding signal components are suppressed while other signal components are suppressed, ultimately achieving MIMO-SAR echo separation.

[0159] Furthermore, simulation data was used to verify the processing performance of the MIMO-SAR echo separation method based on image post-processing in complex detection scenarios proposed in this invention. The simulation experimental parameters involved are shown in Table 1.

[0160] Table 1. Main parameters involved in the simulation data.

[0161]

[0162] This MIMO-SAR system will simultaneously transmit two waveforms, and receive echo signals simultaneously through four channels in elevation. The specific forms of the two transmitted waveforms are as follows:

[0163]

[0164] In the formula, k r The frequency modulation slope is represented. First, the echo separation performance of the proposed method is verified through point target simulation experiments. Figure 2(a) shows the relationship between the height of the point target at 9.5km, 10km, and 10.5km and the azimuth position of the point target; Figure 2(b) shows the relationship between the angle of arrival error (OA) and the azimuth position of the point target at different locations. In actual processing, the true height information of each point target cannot be known a priori, therefore, DOA mismatch will exist in the multi-channel echo signal. After echo separation processing using the proposed method, three point targets at 10km are selected for imaging performance analysis. The imaging results of the three point targets are as follows: Figures 3(a)-3(f) As shown, the range imaging performance of each point target remains unchanged before and after echo separation, indicating that the waveform cross-correlation energy does not affect the target's range imaging performance. Furthermore, the azimuth imaging performance of the point target is significantly improved after echo separation, confirming the effectiveness of the proposed method.

[0165] Furthermore, a surface target simulation experiment was used to verify the performance of the proposed method. Figure 4(a) shows the terrain undulation model of the MIMO-SAR illumination scene, with an altitude variation range of -150m to 150m. The range dimension of the imaging scene is 1.5 km, and the azimuth dimension is 1.2 km. Figure 4(b) shows the angle-of-arrival mismatch model corresponding to this terrain undulation model. To highlight the superiority of the proposed method, traditional least squares (LS) beamforming techniques, sample matrix inversion (SMI) beamforming techniques, linearly constrained minimum variance (LCMV) beamforming techniques, eigenspace (ESB) beamforming techniques, covariance matrix reconstruction (Rec-Est) beamforming techniques, and least absolute shrinkage and selection operator (LASSO) beamforming techniques were used to separate the echo data. Figures 5(a)-5(i) The results of different methods are shown. Figure 5(a) is the true value of the illuminated scene, and Figure 5(b) is the imaging result of the mixed signal before echo separation. It can be seen that the cross-correlation energy between waveforms will seriously degrade the imaging quality of MIMO-SAR. Figures 5(c)-5(i) The echo separation results are shown for LS, SMI, LCMV, ESB, Rec-Est, LASSO, and the proposed technique. It should be noted that the LS, SMI, LCMV, ESB, Rec-Est, and LASSO beamforming techniques perform echo separation processing before azimuth compression. Observation of the processing results shows that LASSO and the proposed method achieve superior processing performance. To quantitatively analyze the echo separation performance of different methods, the Structure Similarity (SSIM) index was used to quantitatively evaluate four regions in the imaging results. Generally, the higher the calculated SSIM value, the better the processing performance of the method. Comparison shows that the proposed method has the highest SSIM values ​​in all four regions, further demonstrating the superiority of the proposed method and its broad application prospects in MIMO-SAR data processing.

[0166] The MIMO-SAR echo separation method based on image post-processing in complex detection scenarios proposed in this invention can effectively mitigate the impact of wave arrival angle mismatch caused by terrain undulations on echo separation performance and improve the imaging performance of MIMO-SAR systems in complex detection scenarios (with terrain undulations).

[0167] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0169] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A MIMO-SAR echo separation method based on image post-processing in complex detection scenarios, characterized in that, Includes the following steps: Step S1: Design the transmit waveform of the MIMO-SAR system and use an elevation multi-channel system to collect echo signals; Step S2: The imaging algorithm is used to compress the echo signals received by the elevation multi-channel system in the range and azimuth directions in sequence to complete the imaging processing. Step S3: Construct a pitch-dimensional spatial snapshot signal based on the multi-channel data after imaging processing, and select the sample covariance matrix estimated from the range-direction samples; Step S4: Perform eigenvalue decomposition on the sample covariance matrix to estimate the number of information sources in the spatial domain; Step S5: Given a certain number of information sources, the MUSIC algorithm is used to estimate the angle of arrival of the information sources. Step S6: Calculate the optimal weight vector based on the least squares criterion, and process the spatial snapshot in step S3 to suppress interference signals in the echo.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: A partially orthogonal waveform is used as the transmitted waveform of the MIMO-SAR system. The transmitted waveform s m (t) is: In the formula, t is the fast time, and M is the number of transmitted waveforms. γ is the modulation vector. i Modulation vector The label for an element that is 1, (·). T For the transpose operation, m is the transmitted waveform index, i is the sub-pulse index, s0(t) represents a signal basis consisting of a set of sub-pulses, and T p The pulse width; In the formula, s(t) is a conventional linear frequency modulated signal, rect(t) is a rectangular window function, and the modulation vector is... It is a unit vector, and the γth... i Each element is 1. To ensure that all transmitted waveforms have the same bandwidth, the modulation vector... satisfy: In the formula, j is the sub-pulse number; In a MIMO-SAR system, the same receiver will receive echo signals from all transmitted waveforms. At this time, the echo signal r from the nth receiving channel... n (t,η) is represented as: In the formula, h n (t,η)=[h n,1 (t,h),h n,2 (t,h),…,h n,M (t,n)] T In the formula, h n,m (t,η) and r n,m (t, η) represent the channel response and echo signal corresponding to the transmission of the m-th transmitter and the reception of the n-th receiver, respectively. η and η represent fast-time convolution operations and slow-time convolution operations, respectively.

3. The method according to claim 1, characterized in that, In step S2, the range-Doppler algorithm is used for imaging processing, and range compression is performed by matched filtering.

4. The method according to claim 3, characterized in that, Step S3 specifically includes: When a MIMO-SAR system simultaneously transmits M different waveforms, there are at most 2M-1 signal components. In this case, the elevation-dimensional spatial snapshot is represented as: In the formula, σ(θ) q ) and v(θ) q ) represent the amplitude and guide vector of the signal component q, respectively, and n is the noise guide vector. Represents echo data r n The imaging result of (t,η), where N is the number of elevation dimension receiving channels; Sample covariance matrix Represented as: In the formula, L represents the number of distance-oriented samples selected, (·) H For the conjugate transpose operation, χ l Quick shot of the airspace.

5. The method according to claim 4, characterized in that, Step S4 specifically includes: The sample covariance matrix after eigenvalue decomposition is: In the formula, A=[v(θ0),v(θ1),…,v(θ K-1 )] L S+I =diag[λ0,λ1,…,λ K-1 ] L n =diag[λ K ,l K+1 ,…,l N-1 ] In the formula, R S+I The signal component covariance matrix, Let I be the noise power, I be the identity matrix, and K be the number of information sources. For the complex field, U c and Λ c U is a matrix formed by eigenvectors and eigenvalues. S+I and Λ S+I U is a matrix formed by the signal's eigenvectors and eigenvalues. n and Λ n It is a matrix formed by noise eigenvectors and eigenvalues, where diag(·) is the operation to generate a diagonal matrix, and λ k For eigenvalues; The number of information sources is obtained through the sample covariance matrix. In the matrix U formed by the eigenvectors c The projection is estimated, that is: Define detection factors: In the formula, u n Let β be the projection vector, and ||·||1 be the modulo operation. n The index n corresponding to the maximum value is the number of information sources in the spatial domain.

6. The method according to claim 5, characterized in that, Step S5 specifically includes: The angle of arrival estimation model based on the MUSIC algorithm is expressed as: In the formula, the set Represents all possible angles of arrival (OA) values ​​for each signal component, for The estimated angle of arrival (AOA) for each signal component is expressed as follows: In the formula, The angle of incidence is denoted as .

7. The method according to claim 6, characterized in that, Step S6 specifically includes: When the incident angle of the signal component is θ' q At that time, the signal guidance vector is represented as: m p (θ)=2πpd sinθ / λ,p=0,1,…,N-1 In the formula, μ p (θ) represents the phase component, p is the receiver array index, and d and λ are the channel spacing and operating wavelength, respectively. The optimal weight vector is calculated using the least squares criterion, and the specific calculation process is as follows: In the formula, W LS For a weighted vector matrix, use weight vectors Weighted processing is performed on the elevation-dimensional spatial snapshot signal to extract the incident angle. The corresponding signal components are suppressed while other signal components are suppressed, ultimately achieving MIMO-SAR echo separation.

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