A small-aperture MIMO radar-based near-range multi-target rapid deformation monitoring method

Abstract

The application particularly relates to a kind of near distance multi-target fast deformation monitoring methods based on small aperture MIMO radar, high azimuth resolution two-dimensional imaging of multiple azimuth adjacent targets is realized by discrete fourier transform combined with MUSIC algorithm, and accurate positioning of multi-target is completed;Secondly, the mutual interference between target side lobe is suppressed using APES filter, and based on the target distance and azimuth information obtained in the previous step, the accurate recovery of target phase is realized;Finally, the time difference interference method is used to accurately estimate the small deformation of target visual distance.The space smoothing (FBSS) idea is introduced into the two-dimensional imaging algorithm, which effectively suppresses the coherent signal interference and realizes high azimuth resolution two-dimensional imaging in complex deformation monitoring scene.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and specifically to a method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar. Background Technology

[0002] In related technologies, obtaining high-precision target deformation measurements is of great significance in structural (building, geological) health monitoring engineering scenarios. MIMO (Multiple-Input, Multiple-Output) radar constructs a virtual array through multiple transmitting and receiving antennas, which can improve angular resolution without increasing the number of physical array elements. Therefore, MIMO antenna arrays have received widespread attention in the fields of two-dimensional imaging and deformation monitoring. In deformation monitoring tasks, due to the requirement of high frame rates, small-aperture MIMO array radar is needed to ensure the flexibility and efficiency of monitoring. However, the inherent finite equivalent aperture of small-aperture arrays brings two technical drawbacks: on the one hand, it is difficult to improve azimuth resolution, resulting in limited ability to distinguish azimuthally adjacent targets in two-dimensional imaging; on the other hand, it generates high sidelobes, causing energy leakage to the target main lobe, introducing significant azimuth coupling phase deviation, and thus affecting the accuracy of subsequent phase recovery. These problems together constitute the key bottleneck for small-aperture MIMO array radar to achieve high-precision deformation monitoring of multiple targets in complex scenarios.

[0003] Existing mainstream MIMO radar imaging solutions for deformation monitoring mostly employ a type of "delay-addition" algorithm, including back projection (BP) and improved Kirchhoff migration algorithms for multi-target imaging in MIMO radar. Their basic principle is to transmit different signals, receive the echo signals using a receiving antenna, and obtain a two-dimensional image of the target through range compression and azimuth dimension processing. Taking the improved BP algorithm as an example, it first performs range compression by matched filtering of the MIMO radar received signal, then divides the imaging area into grids using polar coordinates, analyzes the time delay difference between the pixels of each range unit and the equivalent array element of the MIMO radar, and compensates for it so that the remaining time delay term is the same as the steering vector of the equivalent array element of the MIMO radar. The delay-addition process of the traditional BP algorithm is replaced by beamforming (DBF), completing azimuth-focused imaging. This type of algorithm has data independence, a form similar to matched filtering and beamforming, is simple to implement, and has a high output signal-to-noise ratio, but it is generally suitable for MIMO radar systems with a large number of equivalent array elements and a large array aperture. Under small-aperture array conditions, the limited equivalent aperture leads to insufficient azimuth resolution in 2D images, while the array pattern exhibits high sidelobe characteristics. Traditional sidelobe suppression methods, such as windowing (Hamming window, Hanning window, and Taylor window) or interpolation reconstruction with increased array sampling density, can reduce sidelobe amplitude to some extent, but they also cause problems such as main lobe widening, reduced resolution, or increased model dependence, making it difficult to achieve effective sidelobe suppression while maintaining high resolution.

[0004] In close-range deformation monitoring scenarios using small-aperture MIMO array radar, achieving rapid deformation estimation for multiple targets faces several main constraints: 1) Limited azimuth resolution: Due to the limited effective aperture of small aperture arrays, the azimuth resolution that traditional imaging methods can obtain is limited by the array length, resulting in low azimuth resolution, which is difficult to meet the demand for high azimuth resolution two-dimensional imaging in multi-point deformation monitoring. 2) Large phase extraction error: The inherent high sidelobe characteristics of small aperture arrays introduce significant azimuth interference. When multiple targets appear within the same radial distance, the high sidelobes will interfere with each other's main lobes, contaminating the coherent phase information of the targets, causing phase estimation deviations, and thus affecting the accuracy of deformation inversion; 3) The high sidelobe characteristics of small aperture arrays can produce false targets or artifacts that do not exist near the target or at completely wrong locations, which degrades the image quality and seriously affects subsequent target localization. 4) Existing DOA estimation algorithms, including Capon algorithm, APES algorithm, MUSIC algorithm, etc., are difficult to achieve high resolution and high accuracy phase extraction of azimuth to nearby targets simultaneously in multi-target scenarios.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method and system for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar, a computer program product, and an electronic device, which can effectively overcome the defects existing in the prior art.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to a first aspect of the present invention, a method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar is provided, the method comprising: The target echo signal is received using the receiving antenna of the MIMO radar; the target echo signal is preprocessed to obtain the mixed signal; Range direction processing based on discrete Fourier transform is performed on the mixed signal to obtain target range cells; and azimuth direction processing is performed on the target range cells based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target region. Phase retrieval processing is performed on the two-dimensional distance-azimuth image to obtain the target phase information; Based on the time-difference interferometry method, the target phase information is inverted to obtain the deformation estimation result of the target.

[0009] In some exemplary embodiments, the preprocessing of the target echo signal includes performing at least one of the following processes on the target echo signal: filtering, signal amplification, and signal down-conversion.

[0010] In some exemplary embodiments, range-direction processing of the mixing signal based on the discrete Fourier transform includes: The mixed signal is processed by a fast Fourier transform in the range direction to obtain a range spectrum that represents the target range information; One-dimensional constant false alarm rate (CFAR) detection is performed on the range spectrum to obtain the target range cell.

[0011] In some exemplary embodiments, azimuth processing of target range cells based on the FBSS-MUSIC method includes: Data from the same range cell is extracted from the virtual array elements of the MIMO radar to form a single snapshot vector; A sliding window is used to partition a single snapshot vector to obtain multiple forward overlapping sub-vectors; and a sliding window is used to partition a single snapshot vector from back to front to construct backward conjugate overlapping sub-vectors. Construct the forward sampling covariance matrix based on the forward overlapping sub-vectors. ; and, constructing the backward sampling covariance matrix based on the backward conjugate overlapping sub-vectors. ; The combined covariance matrix after forward and backward spatial smoothing is determined based on the forward sampling covariance matrix and the backward sampling covariance matrix. ; For the comprehensive covariance matrix Perform eigenvalue decomposition to obtain the corresponding eigenvector matrix and eigenvalue vector; The eigenvalue vectors are sorted from largest to smallest according to their magnitude, and the eigenvector matrix is ​​then split into a signal subspace and a noise subspace based on the sorting result. An azimuth spectrum function is constructed based on the noise subspace, and the target azimuth is estimated using the azimuth spectrum norm.

[0012] In some exemplary embodiments, constructing an azimuth spectrum function based on a noise subspace includes:

[0013] in, Represents the noise subspace; For the forward subarray 1 at angle The guide vector at that location.

[0014] In some exemplary embodiments, phase retrieval processing is performed on a two-dimensional distance-azimuth image to obtain target phase information, including: Based on the distance and orientation information of the target in the two-dimensional range-azimuth image, the sidelobe interference is suppressed using the FBSS-APES filter to obtain the target phase information.

[0015] In some exemplary embodiments, the method further includes: Based on the comprehensive covariance matrix Configure the covariance matrix of noise and interference corresponding to the FBSS-APES filter. .

[0016] In some exemplary embodiments, the method further includes: The optimal weighted complex coefficients of the target are configured for the target angle direction in the current frame based on the APES filter coefficients, so as to be used for phase estimation based on the optimal weighted complex coefficients.

[0017] According to a second aspect of the present invention, a short-range multi-target rapid deformation monitoring system based on small aperture MIMO radar is provided, the system comprising: The signal receiving and preprocessing module is used to receive target echo signals using the receiving antenna of the MIMO radar; and to preprocess the target echo signals to obtain mixed signals. The two-dimensional imaging module is used to perform range processing on the mixed signal based on discrete Fourier transform to obtain the target range cell; and to perform azimuth processing on the target range cell based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target area. The phase extraction module is used to perform phase recovery processing on two-dimensional distance-azimuth images to obtain target phase information; The displacement inversion module is used to invert the target phase information based on the time-difference interferometry method to obtain the target deformation estimation results.

[0018] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the above-described method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar is implemented.

[0019] According to a fourth aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar.

[0020] According to a fifth aspect of the present invention, an electronic device is provided, comprising: A processor; and a memory for storing executable instructions of the processor; The processor is configured to implement the above-described method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar by executing the executable instructions.

[0021] The near-range multi-target rapid deformation monitoring method based on small-aperture MIMO radar provided in this invention preprocesses the received target echo signals and then uses a two-dimensional imaging algorithm based on DFT range compression and FBSS-MUSIC angle estimation to obtain a two-dimensional range-azimuth image of the target area, thereby achieving target localization. Subsequently, the phase of each target is recovered using the APES algorithm. Finally, the displacement inversion module uses time-difference interferometry to invert the target deformation based on the target phase information, completing the full-link signal processing for multi-target deformation estimation. By introducing the super-resolution angle measurement MUSIC algorithm into MIMO radar imaging, high-resolution two-dimensional range-azimuth imaging of small-aperture MIMO array radar is achieved. This method overcomes the limitations of array aperture, significantly improves the azimuth resolution of two-dimensional images, enhances the separability of nearby targets, and provides a more accurate target detection basis for deformation monitoring. Furthermore, based on the FBSS-MUSIC algorithm, high-resolution two-dimensional images are obtained to acquire the distance and orientation information of target points. Leveraging the amplitude-phase preservation characteristics of the FBSS-APES algorithm, the target phase is robustly recovered on a constrained set of spatial coordinates, effectively suppressing sidelobe contamination and phase errors caused by high sidelobes in small-aperture arrays, providing reliable phase information for deformation monitoring. This algorithm simultaneously achieves high-resolution two-dimensional imaging of multiple nearby targets and accurate recovery of target phases, precisely locating deformed target points and extracting their phases. It also accurately inverts minute deformations (sub-millimeter level) of target points, ensuring the accuracy of deformation monitoring in complex scenarios.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0024] Figure 1 The illustration shows a schematic diagram of a method for rapid deformation monitoring of multiple targets at close range based on a small aperture MIMO radar, an exemplary embodiment of the present invention. Figure 2 This schematic diagram illustrates a forward-backward spatial smooth subarray partitioning of an exemplary embodiment of the present invention; Figure 3 This diagram illustrates a signal processing flow for multi-target fast deformation estimation, as exemplified by an embodiment of the present invention. Figure 4This diagram illustrates a comparison of resolution simulation results between two algorithms, representing an exemplary embodiment of the present invention. Figure 5 This diagram schematically illustrates a measurement result of target A according to an exemplary embodiment of the present invention; Figure 6 This diagram schematically illustrates a measurement result of target B according to an exemplary embodiment of the present invention. Figure 7 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0026] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0027] In existing technologies, traditional deformation monitoring methods, such as GPS (single-point monitoring), InSAR (long measurement cycle), and total station (manual operation), are time-consuming, labor-intensive, and difficult to achieve real-time monitoring. While some new technologies and methods can perform high-precision real-time measurements, they have drawbacks such as complex installation, susceptibility to environmental interference, large equipment size, and high price, making it impossible to achieve large-scale widespread application.

[0028] When using MIMO radar to measure target deformation in structural (building, geological) health monitoring engineering scenarios, the following problems exist: First, due to the limited physical size of the antenna array, traditional MIMO imaging methods for small-aperture MIMO array radars in engineering deployment scenarios (such as buildings, tunnels) can often only achieve high range resolution (dependent on the bandwidth of frequency-modulated continuous wave), but cannot achieve high azimuth resolution. This results in the inability to effectively distinguish multiple azimuthally adjacent targets in the obtained two-dimensional range-azimuth image, making it difficult to meet the needs of precision monitoring. Second, the limited number of array elements and small equivalent aperture of small-aperture arrays result in weak sidelobe suppression in the radiation pattern. High azimuth sidelobes have significant negative impacts: they can generate false targets or artifacts that do not exist near the target or at completely incorrect locations, leading to image quality degradation and interfering with subsequent target localization; they can also introduce phase errors in azimuth coupling, reducing the accuracy of subsequent phase extraction, and thus affecting the accuracy of multi-point deformation monitoring. Third, existing direction of arrival (DOA) estimation methods, including Capon beamforming, amplitude and phase estimation (APES), and multi-signal classification (MUSIC), can improve azimuth angular resolution, but if multiple targets are close in azimuth, their phases will interfere with each other. Therefore, the above-mentioned single DOA estimation algorithms cannot simultaneously meet the requirements of high resolution of azimuth neighboring targets and high-precision estimation of target phase in multi-target scenarios.

[0029] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a method for rapid deformation monitoring of multiple targets at close range based on small-aperture MIMO radar, referencing... Figure 1 As shown, the method includes: Step S11: Receive the target echo signal using the receiving antenna of the MIMO radar; preprocess the target echo signal to obtain a mixed signal; Step S12: Perform range processing on the mixed signal based on discrete Fourier transform to obtain target range cells; and perform azimuth processing on the target range cells based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target region. Step S13: Perform phase recovery processing on the two-dimensional distance-azimuth image to obtain target phase information; Step S14: Based on the time-difference interferometry method, the target phase information is inverted to obtain the deformation estimation result of the target.

[0030] The method provided by this invention achieves the following objectives: 1) By improving the MIMO radar imaging algorithm, the problem of insufficient azimuth resolution under small aperture array conditions is effectively overcome, and high-resolution two-dimensional range-azimuth imaging results are obtained. 2) To address the high sidelobe problem of small aperture arrays, the phase changes of the target are accurately extracted by effectively suppressing the mutual interference of the target sidelobes, thereby achieving high-precision inversion of the target's millimeter-level or even micrometer-level deformation. 3) Through the designed deformation monitoring signal processing link, high azimuth resolution two-dimensional imaging and high-precision phase extraction of multi-target deformation scenes are realized simultaneously; 4) It can robustly process coherent signals from multiple targets in complex scenarios, improving the high-precision positioning of nearby targets; 5) By optimizing the signal processing link and reducing computational complexity, the real-time processing capability of the monitoring system is improved, meeting the requirements for high frame rate measurement in engineering applications.

[0031] The following will describe in more detail each step of a method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0032] In step S11, the target echo signal is received using the receiving antenna of the MIMO radar; the target echo signal is preprocessed to obtain a mixed signal.

[0033] For example, a smart terminal device can be used to connect to the MIMO radar, control the working status of the MIMO radar, and process the data of the MIMO radar.

[0034] Specifically, a signal receiving and preprocessing module can be provided to control the transmitting antenna of the MIMO radar to transmit detection signals and to receive the target echo signals reflected by the target using the receiving antenna.

[0035] For example, the preprocessing of the target echo signal includes performing at least one of the following processes on the target echo signal: filtering, signal amplification, and signal down-conversion.

[0036] Specifically, the received target echo signal can be preprocessed, such as by sequentially filtering, amplifying, and downconverting the target echo signal to obtain a mixed signal.

[0037] In step S12, the mixed signal is processed in the range direction based on discrete Fourier transform to obtain the target range cell; and the target range cell is processed in the azimuth direction based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target area.

[0038] For example, range processing of a mixing signal based on discrete Fourier transform includes: Step S21: Perform a range-direction Fast Fourier Transform on the mixed signal to obtain a range spectrum representing the target range information; Step S22: Perform one-dimensional constant false alarm detection on the range spectrum to obtain the target range cell.

[0039] Specifically, a radar imaging module can be provided to perform a range-direction FFT (fast Fourier transform) on the mixed signal to obtain a range spectrum for acquiring target range information. By analyzing the spectrum after the range-direction FFT, it can be observed that the range cell containing the target will produce obvious peaks. Therefore, one-dimensional constant false alarm rate (CFAR) is used to detect peaks in the range spectrum to filter out range cells containing the target. Subsequently, only these range cells containing the target are processed for azimuth direction.

[0040] For example, azimuth processing of target range cells based on the FBSS-MUSIC method includes: Step S31: Extract data from the same range cell of the virtual array elements of the MIMO radar to form a single snapshot vector; Step S32: Divide the single snapshot vector using a sliding window to obtain multiple forward overlapping sub-vectors; and divide the single snapshot vector from back to front using a sliding window to construct backward conjugate overlapping sub-vectors. Step S33: Construct the forward sampling covariance matrix based on the forward overlapping sub-vectors. ; and, constructing the backward sampling covariance matrix based on the backward conjugate overlapping sub-vectors. ; Step S34: Determine the comprehensive covariance matrix after forward and backward spatial smoothing based on the forward sampling covariance matrix and the backward sampling covariance matrix. ; Step S35, perform a comprehensive analysis of the covariance matrix. Perform eigenvalue decomposition to obtain the corresponding eigenvector matrix and eigenvalue vector; Step S36: Sort the eigenvalue vectors from smallest to largest according to the size of the eigenvalues, and split the eigenvector matrix into a signal subspace and a noise subspace according to the sorting result; Step S37: Construct the azimuth spectrum function based on the noise subspace, and use the azimuth spectrum norm to estimate the target azimuth.

[0041] Specifically, for MIMO radar, it can include M*N virtual array elements obtained through MIMO technology, consisting of M transmitting elements and N receiving elements. During azimuth processing, data from the same range cell of the virtual array elements can be extracted to form a single snapshot vector. However, the covariance matrix estimation of the MUSIC algorithm is usually obtained by (time) averaging several independent snapshots. Therefore, a spatial smoothing method can be used to divide a long vector into several shorter sub-vectors to construct more "virtual snapshots." Simultaneously, combined with spatial smoothing techniques, the entire uniform linear array is divided into several overlapping equal-length subarrays through a moving average. The covariance matrix of each subarray is calculated, and then the arithmetic mean of the output covariance matrices of each subarray is taken to obtain a new covariance matrix.

[0042] The expression for a long snapshot vector of the MIMO array is as follows: (1) Where M is the number of transmitting array elements and N is the number of receiving array elements.

[0043] For details, please refer to Figure 2 As shown, a decoherence algorithm can be used to partition the forward and backward spatial smooth subarrays.

[0044] The length of the vector formed by applying a sliding window operation to a single snapshot vector is... The forward overlapping subvectors are expressed by the formula: (2) in, .

[0045] For a single snapshot vector, a sliding window operation is used to construct a backward conjugate overlapping subvector from back to front. The formula is as follows: (3) in, ;(·) * Indicates conjugate.

[0046] Then, from the forward overlapping subvectors Construct the forward sampling covariance matrix The formula is expressed as: (4) Then, from the backward conjugate overlapping subvectors Construct the backward sampling covariance matrix The formula is expressed as: (5) Where L is the number of equal-length subarrays, ;(·) H This indicates the conjugate transpose.

[0047] Obtaining the forward sampling covariance matrix Backsampling covariance matrix Then, the combined covariance matrix R after forward and backward spatial smoothing can be constructed, expressed by the formula: (6) Among them, the comprehensive covariance matrix Both forward and backward spatial smoothing have been completed simultaneously, enabling the MUSIC algorithm to effectively suppress coherence caused by strong scattering points of the target under single snapshot conditions, thereby restoring the statistical independence between array snapshots.

[0048] Then, the comprehensive covariance matrix R can be decomposed into eigenvectors and eigenvalues, as shown in the formula: (7) in, This represents the eigenvector matrix.

[0049] Furthermore, based on the descending order of eigenvalues, the eigenvector matrix can be split into a signal subspace and a noise subspace, as follows: (8) in, Represents the noise subspace; This represents the signal subspace.

[0050] Furthermore, the azimuth spectrum function of the MUSIC algorithm is configured as follows: (9) in, For the forward subarray 1 at angle The guide vector at that location.

[0051] Specifically, the aforementioned azimuth focusing process is performed on each of the selected range cells that may contain targets, and the imaging cells without targets are set to 0. The final imaging focusing result yields a two-dimensional range-azimuth image of the target area. The formula is expressed as: (10) Because the noise subspace is strictly orthogonal to the true target direction vector, the MUSIC spectral function can automatically suppress the energy projection in the noise subspace during construction, thus forming deep concave points in non-true angular directions and producing obvious peaks only at true angular locations. This mechanism does not rely on the explicit weighting of sidelobes by traditional windowing methods, but achieves "non-windowed" suppression of sidelobe energy through the inherent orthogonality between the signal and noise subspaces. While achieving high-resolution angle estimation of two-dimensional images, it effectively reduces false scattering or artifacts caused by sidelobes, improving the realism of imaging and overall image quality.

[0052] In step S13, phase recovery processing is performed on the two-dimensional distance-azimuth image to obtain the target phase information.

[0053] For example, phase recovery processing is performed on a two-dimensional range-azimuth image to obtain target phase information, including: using an FBSS-APES filter to suppress sidelobe interference based on the target's range and azimuth information in the two-dimensional range-azimuth image to obtain target phase information.

[0054] For example, the method further includes: based on the comprehensive covariance matrix Configure the covariance matrix of noise and interference corresponding to the FBSS-APES filter. .

[0055] For example, the method further includes: configuring the target's optimal weighted complex coefficients in the target angle direction of the current frame based on the APES filter coefficients, for use in phase estimation based on the optimal weighted complex coefficients.

[0056] Specifically, a phase extraction module can be provided to perform phase recovery processing on two-dimensional range-azimuth images. The set of spatial coordinates of the target point obtained from the aforementioned two-dimensional image in the current frame is defined as follows: .

[0057] Since the results of the MUSIC algorithm are usually pseudospectral and cannot directly extract the phase of the target point, after obtaining the distance and orientation information of the target point in the 2D image, the FBSS-APES filter is used to suppress the interference of other target sidelobes, and the phase of the target is robustly recovered on the constrained spatial coordinate set. The integrated covariance matrix R calculated by the FBSS-MUSIC algorithm is the same as the noise and interference covariance matrix Q in the FBSS-APES algorithm, so it can be directly used in the FBSS-APES design, greatly reducing the computational load.

[0058] Specifically, the covariance matrix of noise and interference can be configured as follows: (11) in, ; and The normalized Fourier transforms of the forward and backward data subvectors are respectively expressed by the following formula: (12) (13) Furthermore, based on the approximate maximum likelihood (ML) method, the APES filter coefficients are configured as follows: (14) Correspondingly, the least squares estimate of the complex coefficients of this target in the current frame can be configured as follows: (15) Based on the above, the phase of the target in the current frame can be obtained as follows: (16) in, This indicates that a complex number is returned in [ The phase angle within the interval [π, π].

[0059] In addition, the phase of multiple target frames can be unwrapped to eliminate phase ambiguity and restore the true phase information.

[0060] In step S14, the target phase information is inverted based on the time difference interferometry method to obtain the deformation estimation result of the target.

[0061] For example, a displacement inversion module can be provided, which, based on the phase information obtained in the previous step and combined with the radar's operating parameters, uses time-difference interferometry to accurately invert the displacement changes of the target.

[0062] Specifically, the phase difference of the echo signals between two adjacent frames is measured using time-difference interferometry, and the minute deformation of the target point along the radar line-of-sight is calculated within one frame time interval. The target displacement inversion is achieved using the following formula: (17) in, The target phase difference extracted from adjacent frames; Indicates wavelength.

[0063] In one exemplary embodiment, this invention introduces the super-resolution angle measurement MUSIC algorithm into MIMO radar imaging, combining it with the concept of spatial smoothing to achieve azimuth resolution beyond the limitations of physical aperture. This method can achieve high-resolution two-dimensional imaging in complex multi-target scenarios, effectively distinguishing azimuth-oriented nearby targets, and improving upon the shortcomings of traditional "delay-addition" algorithms, such as wide main lobe and low resolution under small aperture array conditions.

[0064] The proposed MUSIC-APES algorithm and the commonly used improved BP algorithm are compared and analyzed in terms of resolution through simulation. The case of multiple targets within a range cell is considered, where the echo signals from multiple targets have the same frequency and strong coherence. The simulation experiment uses an antenna array consisting of three transmitting elements and four receiving elements, with an angular resolution of [missing information]. The number of targets is set to 4, namely... , , , The resolution simulation results of the two algorithms are as follows: Figure 4 As shown.

[0065] It can be seen that both the commonly used improved BP algorithm and the MIMO radar two-dimensional imaging algorithm proposed in this invention can distinguish the azimuth interval at a distance of 8m. The two objectives; however, the improved BP algorithm, limited by the antenna array, cannot distinguish the directional spacing at a distance of 6m. The two targets can be clearly distinguished by the MIMO radar two-dimensional imaging algorithm proposed in this invention.

[0066] Furthermore, based on the acquisition of high-resolution two-dimensional images, this invention utilizes the amplitude preservation characteristics of the FBSS-APES filter to construct the optimal weighted complex coefficients of the target in the target angular direction. This can effectively counteract the amplitude attenuation and phase coupling caused by the mutual interference of multiple target sidelobes, enabling the phase of the target echo to be accurately recovered, and providing high-quality input for the subsequent inversion of small deformations.

[0067] The proposed MUSIC-APES algorithm and the improved BP algorithm are compared and analyzed through simulation. The simulation experiment sets up two targets, A (0°, 8m) and B (20°, 8m). Target A performs a round-trip motion of ±1.5mm, with an absolute movement distance of 0.5mm per frame, while target B remains stationary. The proposed MUSIC-APES algorithm and the improved BP algorithm, combined with time-of-flight interferometry, are used to estimate the deformation of both targets. The measurement results for target A are shown below. Figure 5 (a) Figure 5 As shown in (b), the measured deformation value and measurement error value are respectively; the measurement results of target B are as follows: Figure 6 (a) Figure 6 As shown in (b), the measured deformation value and the measurement error value are respectively.

[0068] Table 1 shows a comparison of the deformation estimation errors of the MUSIC-APES algorithm and the improved BP algorithm. Analysis of Table 1 reveals that the MUSIC-APES algorithm provided by this invention effectively suppresses the mutual interference of target sidelobes, accurately recovers the deformation values ​​of two targets, and achieves sub-millimeter level measurement accuracy. While the improved BP algorithm can distinguish between two targets, its measurement accuracy is less precise than the method proposed in this invention due to the influence of high sidelobes.

[0069] Table 1

[0070] The method provided by this invention introduces the super-resolution angle measurement MUSIC algorithm into MIMO radar imaging, realizing high-resolution two-dimensional range-azimuth imaging of small-aperture MIMO array radar. This method overcomes the limitations of array aperture, significantly improves the azimuth resolution of two-dimensional images, enhances the separability of nearby targets, and provides a more accurate target detection basis for deformation monitoring.

[0071] The MUSIC-APES algorithm proposed in this invention first acquires the distance and orientation information of the target point through a high-resolution two-dimensional image. Then, based on the amplitude-phase preservation characteristic of the FBSS-APES algorithm, it robustly recovers the target phase on a constrained set of spatial coordinates, effectively suppressing sidelobe contamination and phase errors caused by high sidelobes in small-aperture arrays, and providing reliable phase information for deformation monitoring. This algorithm simultaneously achieves high-resolution two-dimensional imaging of multiple nearby targets and accurate recovery of the target phase, precisely locating deformed target points and extracting their phase, and accurately inverting minute deformations (sub-millimeter level) of the target points, ensuring the accuracy of deformation monitoring in complex scenes.

[0072] In this scheme, the length of the FBSS-APES filter is configured to match the subarray length of FBSS-MUSIC in the two-dimensional imaging algorithm. The smooth covariance matrix R, already calculated during the FBSS-MUSIC process, is reused to construct the noise and interference covariance matrix in the FBSS-APES design. This matrix reuse mechanism optimizes the signal processing link and significantly reduces the computational complexity of the algorithm.

[0073] This invention enables high-precision real-time measurement while also allowing for low-cost deformation monitoring that is miniaturized, portable, and portable.

[0074] Specifically, in the monitoring of geological disasters such as landslides, debris flows, and earthquakes, this invention can accurately monitor minute surface deformations, promptly detect potential hazards, provide accurate information for disaster early warning, and reduce casualties and property losses. For example, in landslide monitoring, this invention can achieve high frame rate measurements through a small-aperture MIMO array radar, distinguishing deformation differences between different crack zones on the landslide body, accurately capturing sub-millimeter-level surface deformations, and obtaining real-time information on landslide deformation to predict the timing and extent of landslides, thus buying valuable time for evacuating people and implementing preventative measures. Furthermore, in the safety monitoring of engineering structures such as bridges, tunnels, high-rise buildings, and large factories, especially in the field of micro-deformation monitoring in tunnels, it can monitor structural deformation in real time and assess the safety and stability of the structure. For instance, in tunnel monitoring, it can accurately monitor tunnel deformation parameters, promptly detect tunnel structural damage and potential risks, and ensure the safe operation of the tunnel. Furthermore, for immovable cultural relics such as ancient pagodas, grottoes, and city walls, this invention enables non-contact monitoring. Its millimeter-level phase extraction capability can capture subtle deformations in brick and stone structures caused by weathering and uneven foundation settlement. For example, in monitoring the tilt of an ancient pagoda, it can distinguish the tilt differences between different levels of the pagoda, assess the overall structural stability, and provide data support for restoration plans.

[0075] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0076] For example, a short-range multi-target rapid deformation monitoring system based on small-aperture MIMO radar is provided, the system comprising: The signal receiving and preprocessing module is used to receive target echo signals using the receiving antenna of the MIMO radar; and to preprocess the target echo signals to obtain mixed signals. The two-dimensional imaging module is used to perform range processing on the mixed signal based on discrete Fourier transform to obtain the target range cell; and to perform azimuth processing on the target range cell based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target area. The phase extraction module is used to perform phase recovery processing on two-dimensional distance-azimuth images to obtain target phase information; The displacement inversion module is used to invert the target phase information based on the time-difference interferometry method to obtain the target deformation estimation results.

[0077] For example, the MIMO radar receives the target echo signal, preprocesses it, and then sends it to the two-dimensional imaging module. The two-dimensional imaging algorithm based on DFT (Discrete Fourier Transform) range compression and FBSS-MUSIC angle estimation is used to obtain a two-dimensional range-azimuth image of the target area, thereby achieving target localization. Subsequently, the phase extraction module recovers the phase of each target using the APES algorithm. Finally, the displacement inversion module uses time-difference interferometry to invert the deformation of the target based on the target phase information, thus completing the full-link signal processing for multi-target deformation estimation.

[0078] The specific implementation of each module is as described in the above method, and will not be repeated in this embodiment.

[0079] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0080] Figure 7 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0081] It should be noted that, Figure 7 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0082] like Figure 7 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0083] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0084] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0085] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0087] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0088] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0089] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0090] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0091] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0092] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for rapid deformation monitoring of multiple targets at close range based on small-aperture MIMO radar, characterized in that, The method includes: The target echo signal is received using the receiving antenna of the MIMO radar; the target echo signal is preprocessed to obtain the mixed signal; Range direction processing based on discrete Fourier transform is performed on the mixed signal to obtain target range cells; and azimuth direction processing is performed on the target range cells based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target region. Phase retrieval processing is performed on the two-dimensional distance-azimuth image to obtain the target phase information; Based on the time-difference interferometry method, the target phase information is inverted to obtain the deformation estimation result of the target.

2. The method according to claim 1, characterized in that, The preprocessing of the target echo signal includes performing at least one of the following processes on the target echo signal: filtering, signal amplification, and signal down-conversion.

3. The method according to claim 1, characterized in that, Range-direction processing of mixing signals based on Discrete Fourier Transform includes: The mixed signal is processed by a range-oriented Fast Fourier Transform to obtain a range spectrum representing the target range information; One-dimensional constant false alarm rate (CFAR) detection is performed on the range spectrum to obtain the target range cell.

4. The method according to claim 3, characterized in that, Azimuth processing of target range cells based on the FBSS-MUSIC method includes: Data from the same range cell is extracted from the virtual array elements of the MIMO radar to form a single snapshot vector; A sliding window is used to partition a single snapshot vector to obtain multiple forward overlapping sub-vectors; and a sliding window is used to partition a single snapshot vector from back to front to construct backward conjugate overlapping sub-vectors. Construct the forward sampling covariance matrix based on the forward overlapping sub-vectors. ; and, constructing the backward sampling covariance matrix based on the backward conjugate overlapping sub-vectors. ; The combined covariance matrix after forward and backward spatial smoothing is determined based on the forward sampling covariance matrix and the backward sampling covariance matrix. ; For the comprehensive covariance matrix Perform eigenvalue decomposition to obtain the corresponding eigenvector matrix and eigenvalue vector; The eigenvalue vectors are sorted from largest to smallest according to their magnitude, and the eigenvector matrix is ​​then split into a signal subspace and a noise subspace based on the sorting result. An azimuth spectrum function is constructed based on the noise subspace, and the target azimuth is estimated using the azimuth spectrum norm.

5. The method according to claim 4, characterized in that, Constructing the azimuth spectrum function based on the noise subspace includes: in, Represents the noise subspace; For the forward subarray 1 at angle The guide vector at that location.

6. The method according to claim 1, characterized in that, Phase retrieval processing is performed on the two-dimensional range-azimuth image to obtain the target phase information, including: Based on the distance and orientation information of the target in the two-dimensional range-azimuth image, the sidelobe interference is suppressed using the FBSS-APES filter to obtain the target phase information.

7. The method according to claim 6, characterized in that, The method further includes: Based on the comprehensive covariance matrix Configure the covariance matrix of noise and interference corresponding to the FBSS-APES filter. .

8. The method according to claim 6, characterized in that, The method further includes: The optimal weighted complex coefficients of the target are configured for the target angle direction in the current frame based on the APES filter coefficients, so as to be used for phase estimation based on the optimal weighted complex coefficients.

9. A short-range multi-target rapid deformation monitoring system based on small-aperture MIMO radar, characterized in that, The system includes: The signal receiving and preprocessing module is used to receive target echo signals using the receiving antenna of the MIMO radar; and to preprocess the target echo signals to obtain mixed signals. The two-dimensional imaging module is used to perform range processing on the mixed signal based on discrete Fourier transform to obtain the target range cell; and to perform azimuth processing on the target range cell based on the FBSS-MUSIC method to obtain a two-dimensional range-azimuth image of the target area. The phase extraction module is used to perform phase recovery processing on two-dimensional distance-azimuth images to obtain target phase information; The displacement inversion module is used to invert the target phase information based on the time-difference interferometry method to obtain the target deformation estimation results.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for rapid deformation monitoring of multiple targets at close range based on small aperture MIMO radar as described in any one of claims 1 to 8.

Images