DOA estimation method for distributed millimeter wave radar network, medium and equipment

Abstract

The invention provides a distributed millimeter wave radar network-oriented DOA estimation method, a medium and equipment, and belongs to the technical field of radar signal processing. According to the method, after a large-aperture MIMO radar system is formed based on a plurality of small millimeter wave radars, a two-dimensional single-snapshot sparse array signal model is constructed by using a steering vector of a two-dimensional MIMO equivalent virtual plane array, and data interpolation and extrapolation processing are performed on aperture missing existing in a sparse array by constructing a block Hankel matrix and applying a matrix completion technology. On the basis, a fast two-dimensional angle estimation algorithm based on phase coherence factor weighting is adopted, and efficient joint estimation of the azimuth angle and the pitch angle of the target is achieved. The method can be flexibly applied to different types of distributed millimeter wave radar networks, effectively overcomes the interference effect of DOA estimation sidelobes and grating lobes in the prior art, and improves the signal-to-noise ratio level of array response.

Description

Technical Field

[0001] The present invention belongs to the field of radar signal processing, and particularly relates to a DOA estimation method, medium and device for a distributed millimeter-wave radar network. Background Art

[0002] As an emerging detection technology, the distributed millimeter-wave radar network exhibits great application potential in fields such as autonomous driving and intelligent security due to its high angular resolution, strong anti-interference ability, and flexible networked deployment characteristics. Among them, the direction of arrival (DOA) estimation, as the core link of radar signal processing, directly affects the accuracy of target positioning and tracking. However, in a distributed millimeter-wave radar network, DOA estimation faces many challenges: First, due to the sparse spatial distribution of each radar unit in the network, traditional array signal processing methods are difficult to directly construct a complete virtual aperture, resulting in limited angular resolution; Second, the sparse array structure is prone to introducing grating lobe and sidelobe interference, reducing the robustness of target angle estimation. Existing methods are mostly based on centralized arrays or fixed topology designs, and it is difficult to adapt to the dynamic characteristics and high real-time requirements of distributed networks, seriously restricting the performance of millimeter-wave radar networks in multi-target scenarios. Summary of the Invention

[0003] Aiming at the problems of aperture loss, severe grating lobe and sidelobe interference, and insufficient angle estimation accuracy in multi-target scenarios caused by the sparse array structure in a distributed millimeter-wave radar network, the present invention provides a DOA estimation method, medium and device for a distributed millimeter-wave radar network. Through the innovation of sparse array signal reconstruction and efficient angle estimation algorithms, the bottleneck of aperture loss and interference suppression in the existing technology is broken through, and at the same time, the flexibility and adaptability of the algorithm are improved, providing key technical support for high-precision and low-complexity target angle estimation in a distributed millimeter-wave radar network.

[0004] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a DOA estimation method for a distributed millimeter-wave radar network, including the following steps: S1: Based on a multiple-input multiple-output (MIMO) radar system formed by multiple millimeter-wave radars, obtain the steering vector of a two-dimensional MIMO equivalent virtual plane array, and then construct a two-dimensional single-snapshot sparse array signal model; S2: Introduce a uniform two-dimensional array with the same aperture size as the two-dimensional single-snapshot sparse array signal model, and construct a block Hankel matrix; S3: Construct a matrix completion method based on the block Hankel matrix, and use the matrix completion method to complete the aperture missing in the two-dimensional single-snapshot sparse array signal model. After completion, the full-array echo signal is obtained; S4: For the full-array echo signal, adopt a two-dimensional angle estimation algorithm based on phase coherence factor weighting to jointly estimate the target azimuth angle and elevation angle.

[0005] Optionally, in step S1, the process of obtaining the steering vector of the two-dimensional MIMO equivalent virtual planar array is as follows: Based on the MIMO radar system, according to the far-field assumption conditions, the steering vectors of the transmitting array and the receiving array are respectively derived according to the echo signal and , and then the azimuth angle and the elevation angle to the steering vector of the two-dimensional MIMO equivalent virtual planar array : ; where represents the Kronecker product.

[0006] Optionally, in step S1, the process of constructing the two-dimensional single-snapshot sparse array signal model is as follows: Set a single-snapshot array containing targets. The direction angle and elevation angle of the th target are , and the backscattering coefficient is , . In the case of no noise, construct the following two-dimensional single-snapshot sparse array signal model: ; where is the th element of the two-dimensional single-snapshot array response , represents the steering vector from the direction angle and the elevation angle to the two-dimensional MIMO equivalent virtual planar array, represents the position of the th element in the two-dimensional MIMO equivalent virtual planar array, represents the echo wavelength, is the exponential function.

[0007] Optionally, the process of step S2 is as follows: Introduce a uniform two-dimensional array with the same aperture size as , and construct a Block Hankel matrix : ; ; wherein, , , is a Hankel matrix, is the th element in

[0008] Optionally, when and , has a rank of , there exists the following Vandermonde factorization: ; wherein, is the subarray popularity matrix, is the sparse diagonal matrix characterizing the target scattering characteristics, and the superscript T represents the transpose.

[0009] Optionally, in step S3, the matrix completion method is formulated as the following matrix completion problem: ; wherein, and are regularization parameters for balancing, represents the L1 norm, represents the nuclear norm, represents the F norm, is the sampling operator, represents extracting the known entries in matrix according to the observation set , is the regularization parameter for controlling the noise level, represents the constraint condition.

[0010] Optionally, the process of step S4 is: Construct the two-dimensional DBF output response for the full-array echo signal as: ; ; wherein, represents the azimuth-elevation two-dimensional spatial spectrum amplitude distribution, , , the observation airspace is discretized into A uniform azimuth-elevation grid, represents a four-dimensional manifold matrix of a two-dimensional matrix with respect to the azimuth angle and the elevation angle The superscript H represents the conjugate transpose, denotes the th azimuth angle and the th elevation angle of the two-dimensional matrix, th element, denotes the th element in, is a calculation variable; Based on the standard deviation of the phase, generate the direction phase coherence factor weighting coefficient of is: ; ; wherein, is a complex signal phase extraction operator, is the standard deviation operation; Utilize to perform the following correction on : ; wherein, represents the weighted spatial spectrum, is a control factor; By performing a two-dimensional peak search on the weighted spatial spectrum to obtain the azimuth angle and elevation angle of the target.

[0011] In a second aspect, the present invention provides a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute the DOA estimation method for a distributed millimeter-wave radar network as described in the first aspect.

[0012] In a third aspect, the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the DOA estimation method for a distributed millimeter-wave radar network as described in the first aspect.

[0013] The beneficial effects of the present invention are: (1) The present invention obtains the reconstructed uniform array data through matrix completion, not only effectively filling the missing data of the sparse array, but also realizing the following three core improvements by restoring the complete array manifold structure: suppressing the sidelobe and grating lobe effects; improving the array signal-to-noise ratio; maintaining the array aperture gain.

[0014] (2) Based on the fast two-dimensional angle estimation algorithm, the present invention greatly reduces the computational complexity while ensuring high-precision DOA estimation, meets the stringent requirements of the distributed millimeter-wave radar network for low power consumption and real-time performance, and is convenient for hardware implementation and engineering deployment.

[0015] (3) The present invention can flexibly adapt to different types of distributed millimeter-wave radar networks, support the collaborative work of heterogeneous radar units (such as different numbers of antennas or hardware capabilities), adapt to the dynamic changes of the network topology, and expand the application scope of DOA estimation technology in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a DOA estimation method for a distributed millimeter-wave radar network; Figure 2 is a schematic diagram of a one-dimensional distributed millimeter-wave radar network; Figure 3 is a position diagram of physical transmitting array elements and virtual transmitting array elements in a one-dimensional array distributed millimeter-wave radar network; Figure 4 is a comparison diagram between the method proposed by the present invention and the traditional digital beam forming (DBF) method; Figure 5 is a position diagram of physical transmitting array elements in a two-dimensional array distributed millimeter-wave radar network; Figure 6 is a position diagram of virtual transmitting array elements in a two-dimensional array distributed millimeter-wave radar network; Figure 7 is a result diagram of the traditional DBF method; Figure 8 is a result diagram of the method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.

[0018] In one embodiment, the present invention proposes a DOA estimation method for a distributed millimeter-wave radar network, as Figure 1 shown, the method includes the following steps: S1: After forming a large-aperture MIMO radar system based on multiple small millimeter-wave radars, obtain the steering vector of the two-dimensional MIMO equivalent virtual plane array, and construct a two-dimensional single snapshot sparse array signal model.

[0019] In this embodiment, after the integrated radar units form a large-aperture MIMO radar system, according to the far-field hypothesis conditions, the steering vectors of the transmitting array and the receiving array are respectively derived from the echo signals. and , and then the azimuth angle and the elevation angle are obtained to the steering vector of the two-dimensional MIMO equivalent virtual planar array : ; wherein, represents the Kronecker product.

[0020] Therefore, assuming a single snapshot array contains targets, the direction angle and elevation angle of the th target are , the backscattering coefficient is , , in the case of no noise, the th element of the two-dimensional single snapshot array response can be expressed as: ; wherein, represents the steering vector from the direction angle and the elevation angle to the two-dimensional MIMO equivalent virtual planar array, represents the position of the th element in the two-dimensional MIMO equivalent virtual planar array, represents the echo wavelength, is the exponential function.

[0021] S2: Introduce a uniform two-dimensional array with the same aperture size as the two-dimensional single snapshot sparse array signal model, and construct a block Hankel matrix.

[0022] In this embodiment, a uniform two-dimensional array with the same aperture size as is introduced , and a block Hankel matrix is constructed: ; ; wherein, , , is a Hankel matrix, is the th element in.

[0023] When and at this time, the rank of , there exists the following Vandermonde factorization: ; wherein, is the subarray popularity matrix, is a sparse diagonal matrix representing the target scattering characteristics, and the superscript T represents the transpose.

[0024] S3: Construct a matrix completion method with sparse and low-rank constraints to perform data interpolation and extrapolation processing on the aperture missing of the sparse array.

[0025] In this embodiment, since the matrix has many missing elements (unobserved items are set to zero), therefore can be regarded as an undersampled version. In the presence of noise, combining the sparsity of the target diagonal matrix and the low rank of the block Hankel matrix , the matrix completion problem is formulated as: ; wherein, represents the L1 norm, represents the nuclear norm, represents the F norm, is a sampling operator, represents extracting the known entries in the matrix according to the observation set , the regularization parameters and play a balancing role, is also a regularization parameter for controlling the noise level, represents the constraint condition.

[0026] S4: For the completed full-array echo signal, adopt a fast two-dimensional angle estimation algorithm based on phase coherence factor weighting to achieve efficient joint estimation of the target azimuth angle and elevation angle.

[0027] In this embodiment, after completing the matrix completion of the signal , the estimated full-array echo signal can be obtained, and the two-dimensional DBF output response is constructed as: ; ; wherein, Represents the azimuth-elevation two-dimensional spatial spectrum amplitude distribution, , , discretize the observed airspace into uniform azimuth-elevation grids, and the grid spacing is set according to the angular resolution; Represents the four-dimensional manifold matrix of a two-dimensional matrix with respect to the azimuth angle and the elevation angle , where the superscript H represents the conjugate transpose, Represents in the th azimuth angle and the th elevation angle of the two-dimensional matrix th element, Represents in the th element; Define the weight coefficient at direction as , and generate the phase coherence factor weighting coefficient in the azimuth-elevation direction based on the standard deviation of the phase as: ; ; Among them, is the complex signal phase extraction operator, is the standard deviation operation; Use to correct the DBF output: ; Among them, is the control factor, which is between 0 and 1.

[0028] By performing a two-dimensional peak search on the weighted spatial spectrum , accurately obtain the azimuth angle and elevation angle of the target.

[0029] Next, combine specific experiments to illustrate the effectiveness of the method proposed by the present invention.

[0030] Figure 2 Shows a one-dimensional distributed millimeter-wave radar network structure, which includes two independent radar units, and completes coherent cascading through local oscillator and trigger signal synchronization to form a coherent radar system, and each radar is configured with two transmitters and four receivers. Figure 3 Shows Figure 2 the positions of the physical transmit array elements and virtual transmit array elements of the radar network structure. It can be seen that 32 virtual receive array elements can form a virtual array with an aperture of 74d (d is equal to half the wavelength). Figure 4Shows the comparison diagram between the method proposed by the present invention and the traditional DBF method. The beam response resolution of the method proposed by the present invention is the highest, reaching the theoretical limit of 1.1°, and at the same time the sidelobe is as low as below -30dB, judged according to Figure 4 the maximum sidelobe amplitude except the main lobe in

[0031] Figure 5 Shows the physical transmit array element positions of a two-dimensional array distributed millimeter-wave radar network structure, and the positions of the corresponding virtual transmit array elements are Figure 6 as shown. Each sensor is equipped with four transmit array elements and four receive array elements, and a total of up to 64 physical channels can be formed to synthesize a MIMO two-dimensional virtual array in the area of [0,107](d)×[0,13](d). Figure 7 and Figure 8 Show the spectra of two targets when the azimuth and elevation angles are (10°, -10°) and (-20°, 15°). The sparse arrays used adopt the traditional DBF method and the method proposed by the present invention respectively, Figure 7 and Figure 8 The color bar on the right represents the normalized amplitude (dB). Both methods can generate peaks at the corresponding positions. However, the sidelobes of the results obtained by the method proposed by the present invention are significantly lower.

[0032] Under the same experimental conditions as above, for two different network structures, different SNRs are set respectively, and the comparison test results with the traditional DBF method are shown in Table 1. It can be seen that the method proposed by the present invention can not only improve the resolution, but also reduce the maximum sidelobe level, indicating the effectiveness and correctness of the method proposed by the present invention for DOA estimation of distributed millimeter-wave radar networks.

[0033] Table 1 Comparison test results of the method proposed by the present invention and the traditional DBF method

[0034] In summary, the present invention constructs a block Hankel matrix structure and uses matrix completion technology to interpolate and extrapolate sparse array data, making up for the missing virtual aperture. At the same time, it combines phase coherence factor weighting to suppress grating lobe and sidelobe interference, and improves the signal-to-noise ratio level of the array response. The method proposed by the present invention has low computational complexity while ensuring high-precision angle estimation, adapts to the real-time processing requirements of distributed millimeter-wave radar networks, and provides a reliable solution for efficient and robust DOA estimation of multiple targets in complex environments.

[0035] In another embodiment, the present invention proposes a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute the DOA estimation method for a distributed millimeter-wave radar network in the foregoing embodiment.

[0036] In another embodiment, the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the DOA estimation method for a distributed millimeter-wave radar network in the foregoing embodiment is implemented.

[0037] In the embodiments disclosed in the present application, the computer storage medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CDROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0038] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed in the present application can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0039] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art of this technology, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A DOA estimation method for a distributed millimeter wave radar network, characterized in that: The steps include: S1: Based on the MIMO radar system formed by multiple millimeter-wave radars, the steering vector of the two-dimensional MIMO equivalent virtual plane array is obtained, and then a two-dimensional single-snapshot sparse array signal model is constructed; S2: Introduce a uniform two-dimensional array with the same aperture size as the two-dimensional single-snapshot sparse array signal model and construct a block Hankel matrix; S3: Based on the block Hankel matrix, a matrix completion method is constructed, and the matrix completion method is used to complete the aperture missing in the two-dimensional single-snapshot sparse array signal model, and the full array echo signal is obtained after completion; S4: For the full array echo signal, a two-dimensional angle estimation algorithm based on phase coherence factor weighting is used to jointly estimate the target azimuth and elevation angles.

2. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 1, characterized in that: In step S1, the process of obtaining the steering vector of the two-dimensional MIMO equivalent virtual plane array is: Based on the MIMO radar system, according to the far-field assumption, the steering vectors of the transmitting array and the receiving array are derived according to the echo signal. and , and then get the azimuth and pitch angle Steering vectors to the 2D MIMO equivalent virtual plane array : ； in, represents the Kronecker product.

3. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 1, characterized in that: In step S1, the process of constructing a two-dimensional single snapshot sparse array signal model is: Set up a single snapshot array containing Target, The azimuth and elevation angles of the target are , the backscatter coefficient is , , in the absence of noise, the following two-dimensional single-snapshot sparse array signal model is constructed: ； in, is a two-dimensional single snapshot array response No. elements, Indicates the direction angle and pitch angle To the steering vector of the 2D MIMO equivalent virtual plane array, represents the first The position of the element, represents the echo wavelength, is an exponential function.

4. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 3, characterized in that: The process of step S2 is: Introduction and Uniform 2D array of the same aperture size , construct a The block Hankel matrix of : ； ； in, , , is a The Hankel matrix of yes Middle elements.

5. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 4, characterized in that: when and hour, The rank of , There exists a Vandermonde factorization as follows: ； in, is the subarray manifold matrix, is a sparse diagonal matrix that represents the scattering characteristics of the target. The superscript T represents the transpose.

6. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 5, characterized in that: In step S3, the matrix completion method is expressed as the following matrix completion problem: ； in, and is the regularization parameter used for balancing, represents the L1 norm, represents the nuclear norm, represents the F-norm, is the sampling operator, According to the observation set Extract Matrix Known entries in is the regularization parameter used to control the noise level, Represents a constraint.

7. The DOA estimation method for a distributed millimeter wave radar network as claimed in claim 4, characterized in that: The process of step S4 is: For full array echo signal , construct a two-dimensional DBF output response for: ； ； in, represents the azimuth-elevation two-dimensional spatial spectrum amplitude distribution, , , the observation space is discretized into a uniform azimuth-elevation grid, Indicates the azimuth and pitch angle The four-dimensional popular matrix of the two-dimensional matrix, the superscript H represents the conjugate transpose, express About Azimuth and Pitch angle The two-dimensional matrix elements, express Middle elements, is the calculated variable; Based on the standard deviation of the phase, the direction is generated The phase coherence factor weighting coefficient for: ； ； in, is the complex signal phase extraction operator, is the standard deviation operation; use right Make the following corrections: ； in, represents the weighted spatial spectrum, is the control factor; By weighting the spatial spectrum Perform a two-dimensional peak search to obtain the azimuth and elevation angles of the target.

8. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the DOA estimation method for a distributed millimeter wave radar network as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the DOA estimation method for a distributed millimeter wave radar network as described in any one of claims 1 to 7 is implemented.

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