A High-Resolution Imaging Method for Scanning Radar in Low Signal-to-Noise Ratio Conditions

Abstract

The present invention discloses a high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions, comprising: Step 1: Modeling the echo signal of the scanning radar and initializing the scattering point power matrix; Step 2: Calculating the covariance matrix according to the scattering point power matrix; Step 3: Modeling the radar imaging problem as a least-squares problem by using the covariance matrix and calculating the least-squares solution of the target scattering intensity; Step 4: Calculating the scattering point power matrix according to the least-squares solution of the target scattering intensity; Step 5: Removing the smaller values in the scattering point power matrix to update it; Step 6: Repeating the operations of Step 2 to Step 5 until the maximum number of iterations is reached. The present invention applies the iterative adaptive method based on the least-squares method to scanning radar imaging, and removes the smaller values in the scattering point power matrix during the iteration process, making the target signal more prominent, improving the azimuth resolution under low signal-to-noise ratio conditions, and enhancing the imaging effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and particularly relates to a high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions. Background Art

[0002] A scanning radar is an effective tool for obtaining a forward-looking image by scanning an imaging area. Range high-resolution can be achieved through pulse compression technology. However, the azimuth resolution is limited by the Rayleigh criterion, i.e., θ∝λ / D, where λ is the wavelength and D is the antenna aperture. Due to platform limitations, it is difficult to directly increase the antenna aperture. Therefore, finding an effective method to break through the antenna aperture limit has become the focus of scanning radar imaging.

[0003] In the super-resolution method, the radar can be described as the convolution of the radar antenna pattern and the scattering value of the target in the azimuth direction. Since the radar antenna pattern is a low-pass filter, direct deconvolution will increase the noise in the high-frequency part and affect the imaging quality.

[0004] In the existing methods for improving radar imaging resolution, the literature "Angular superresolution for real beam radar with iterative adaptive approach" proposed a method using the iterative adaptive approach (IAA) to improve the azimuth resolution.

[0005] However, in radar imaging, due to the limitations of long distance and transmit power, the signal-to-noise ratio is usually low. When the signal-to-noise ratio is low, the performance of the above method will be limited, thus affecting the imaging quality. Summary of the Invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0007] A high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions, comprising:

[0008] Step 1: Model the echo signal of the scanning radar and initialize the scattering point power matrix;

[0009] Step 2: Calculate the covariance matrix of the echo according to the scattering point power matrix;

[0010] Step 3: Model the radar imaging problem as a least squares problem using the covariance matrix and calculate the least squares solution of the target scattering intensity;

[0011] Step 4: Calculate the scattering point power matrix according to the least squares solution of the target scattering intensity;

[0012] Step 5: Remove the smaller values in the scattering point power matrix to update it;

[0013] Step 6: Repeat the operations in Step 2 to Step 5 until the maximum number of iterations is reached.

[0014] In an embodiment of the present invention, in Step 1, before modeling the echo signal of the scanning radar, it further includes:

[0015] Perform range-direction matched filtering on the echo signal of the scanning radar.

[0016] In an embodiment of the present invention, Step 1 includes:

[0017] 1a) For the echo signal of the scanning radar, construct the following signal model:

[0018] y = As + e

[0019] where the vector y = [y1, y2,... y N T is the echo observation data, the vector e = [e1, e2,..., e N T is the noise generated during reception, the vector s = [s1, s2,..., s K T is the scene scattering intensity, N is the number of snapshots, K is the number of azimuths scanned by the radar, A is the steering matrix, and its expression is:

[0020]

[0021] h = [h1, h2,... h L T is the radar antenna pattern function, a k is the steering vector in the k-th direction, 1 ≤ k ≤ K;

[0022] 1b) Based on the signal model, initialize the scattering point power matrix Its expression is:

[0023]

[0024]

[0025] where the vector a k is the steering vector in the k-th azimuth, and H represents the conjugate transpose.

[0026] ​​​​In an embodiment of the present invention, in step 2, the calculation formula of the covariance matrix is as follows:

[0027]

[0028] where R represents the covariance matrix and H represents the conjugate transpose.

[0029] In an embodiment of the present invention, step 3 includes:

[0030] 3a) Modeling the radar high-resolution imaging problem as a least-squares problem, and the least-squares loss function is as follows:

[0031]

[0032] where, Q(θ k ) represents the interference and noise matrix, and its expression is is the scattering point power matrix the k-th element in;

[0033] 3b) Calculating the least-squares solution of the target scattering intensity, and the calculation formula is as follows:

[0034]

[0035] where, represents the signal of the target in the k-th azimuth.

[0036] In an embodiment of the present invention, in step 4, the element in the scattering point power matrix has the following calculation formula:

[0037]

[0038] In an embodiment of the present invention, step 5 includes:

[0039] 5a) Extracting the elements on the diagonal of the scattering point power matrix

[0040]

[0041] where, is a vector, including K elements, and diag represents extracting the diagonal elements of the matrix;

[0042] 5b) Sorting the vector

[0043]

[0044] where, represents sorting the vector​​ The sorted vector, where "sort" means sorting the vector from largest to smallest, and the vector "Index" is the sorting index;

[0045] 5c) Truncate the vector perform truncation

[0046]

[0047] where, represents the k-th element of the vector and b is the set truncation parameter;

[0048] 5d) Update the scatter point power matrix

[0049]

[0050] where, Index(k) represents the k-th element of the vector Index.

[0051] Advantages of the present invention:

[0052] The high-resolution imaging method of a scanning radar under low signal-to-noise ratio provided by the present invention applies the iterative adaptive method based on the least squares method to scanning radar imaging, and removes the smaller values of the scatter point power matrix during the iteration process, thereby suppressing noise, making the target signal more prominent, improving the azimuth resolution of the scanning radar under low signal-to-noise ratio conditions, and further enhancing the imaging effect.

[0053] The following will further elaborate on the present invention in conjunction with the accompanying drawings and embodiments. Description of the Drawings

[0054] Figure 1 is a schematic flowchart of the high-resolution imaging method of a scanning radar under low signal-to-noise ratio provided by an embodiment of the present invention;

[0055] Figure 2 is another schematic flowchart of the high-resolution imaging method of a scanning radar under low signal-to-noise ratio provided by an embodiment of the present invention;

[0056] Figure 3 is the operation result diagram using the existing IAA method;

[0057] Figure 4 is the operation result diagram using the method of the present invention. Specific Embodiments

[0058] The following further describes the present invention in detail with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0059] Embodiment 1

[0060] Please refer to Figure 1-2 , Figure 1 which is a schematic flow chart of a high-resolution imaging method for a scanning radar in a low signal-to-noise ratio scenario provided by an embodiment of the present invention, Figure 2 and is another schematic flow chart of a high-resolution imaging method for a scanning radar in a low signal-to-noise ratio scenario provided by an embodiment of the present invention; it includes the following steps:

[0061] Step 1: Model the echo signal of the scanning radar and initialize the scatterer power matrix.

[0062] First, the radar transmits a signal to the target, and the transmitted signal can be expressed as:

[0063]

[0064] wherein, is a rectangular window function, exp is an exponential operation with the natural constant e as the base, j is the imaginary unit, π is the pi, γ is the chirp rate, is the fast time.

[0065] Then, receive the echo signal, and the echo signal can be expressed at the fundamental frequency as:

[0066]

[0067] where a r (·) and a a (·) are the window function and azimuth window function of the radar linear frequency modulation signal respectively, and the distance from the radar antenna phase center to the target point is R(t m ; R B ), λ = c / f c is the wavelength corresponding to the center frequency.

[0068] It should be noted that after obtaining the echo signal, it is also necessary to perform range-direction matched filtering on the echo signal.

[0069] Specifically, set the system matching function as:

[0070]

[0071] wherein, is to reverse and then take the conjugate.

[0072] Use the above system matching function to perform pulse compression and range migration correction on the echo signal to obtain the corrected echo signal.

[0073] Then model the corrected echo signal again.

[0074] Specifically, Step 1 includes:

[0075] 1a) For the echo signal of the calibrated scanning radar, construct the following signal model:

[0076] y = As + e

[0077] where the vector y = [y1, y2, … y N T is the echo observation data, the vector e = [e1, e2, …, e N T is the noise generated during reception, and the vector s = [s1, s2, …, s K T is the scene scattering intensity, N is the number of snapshots, K is the number of azimuths scanned by the radar, A is the steering matrix, and its expression is:

[0078]

[0079] where h = [h1, h2, …, h L T is the radiation pattern function of the radar antenna, a k is the steering vector in the k-th direction, 1 ≤ k ≤ K.

[0080] 1b) Based on the above signal model, initialize the scattering point power matrix

[0081] In this embodiment, the scattering point power matrix is a K×K diagonal matrix, and the matrix the elements on the diagonal are respectively denoted as

[0082] The calculation formula for the diagonal elements is as follows:

[0083]

[0084] where the vector a k is the steering vector in the k-th azimuth, and H represents the conjugate transpose.

[0085] Step 2: Calculate the covariance matrix of the echo according to the scattering point power matrix.

[0086] The calculation formula for the covariance matrix is:

[0087]

[0088] where A is the steering matrix in step 1a), is the scattering point power matrix, and A H is the conjugate transpose of matrix A. ​​​​

[0089] Step 3: Model the radar imaging problem as a least squares problem using the covariance matrix, and calculate the least squares solution of the target scattering intensity.

[0090] 3a) Model the radar high-resolution imaging problem as a least squares problem. The least squares loss function is as follows:

[0091]

[0092] where, Q(θ k ) represents the interference and noise matrix, and its expression is

[0093] 3b) Calculate the least squares solution of the target scattering intensity. The calculation formula is as follows:

[0094]

[0095] where, represents the signal of the target at the k-th azimuth.

[0096] Step 4: Calculate the scattering point power matrix according to the least squares solution of the target scattering intensity.

[0097] Specifically, the elements in the scattering point power matrix are calculated by the formula:

[0098]

[0099] Step 5: Remove the smaller values in the scattering point power matrix to update it.

[0100] 5a) Extract the elements on the diagonal of the scattering point power matrix

[0101]

[0102] where, is a vector containing K elements, and diag represents extracting the diagonal elements of the matrix.

[0103] 5b) Sort the vector

[0104]

[0105] where, represents the vector obtained by sorting the vector , sort represents sorting the vector from largest to smallest, and the vector Index is the sorting index.

[0106] 5c) For the vector​ Truncate

[0107]

[0108] wherein represents the k-th element of the vector , and b is the set truncation parameter.

[0109] 5d) Update the scattering point power matrix

[0110]

[0111] wherein, Index(k) represents the k-th element of the vector Index.

[0112] It can be obtained from experience that during the scanning radar imaging process, larger values are generally targets and smaller values are generally noise. Therefore, in order to remove noise and make the target more prominent, in this embodiment, smaller values in the scattering point power matrix are removed and larger values are retained. Specifically, during the scanning radar imaging process, since the effective elements of the matrix are concentrated on the diagonal, the elements on the diagonal of the matrix are extracted through step 5a) for subsequent operation and processing. In step 5b), the vector is sorted from large to small to obtain the vector In step 5c), the larger elements in are retained, and the smaller elements are set to zero. In step 5d), the update of the matrix is completed.

[0113] Step 6: Repeat the operations in steps 2 to 5 until the maximum number of iterations is reached.

[0114] Specifically, the maximum number of iterations can be set to B, and the current number of iterations is b. When the maximum number of iterations B is reached, the update stops.

[0115] The high-resolution imaging method for a scanning radar in a low signal-to-noise ratio situation provided by the present invention applies an iterative adaptive method based on the least squares method to scanning radar imaging, and removes smaller values in the scattering point power matrix during the iteration process, thereby suppressing noise, making the target signal more prominent, improving the azimuth resolution of the scanning radar under low signal-to-noise ratio conditions, and further enhancing the imaging effect.

[0116] In order to verify the beneficial effects of the present invention, in this embodiment, the existing IAA algorithm and the algorithm of the present invention are respectively used for scanning radar imaging comparison under low signal-to-noise ratio conditions, and the results are respectively as shown in Figure 3 and 4 shown, wherein Figure 3It is the operation result diagram using the existing IAA method; Figure 4 It is the operation result diagram using the method of the present invention. By comparison Figure 3 and Figure 4 it can be seen that the method of the present invention has better performance.

[0117] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions, characterized in that, Including: Step 1: Model the echo signal of the scanning radar and initialize the scattering point power matrix; Step 2: Calculate the covariance matrix of the echo according to the scattering point power matrix; Step 3: Use the covariance matrix to model the radar imaging problem as a least squares problem and calculate the least squares solution of the target scattering intensity; Step 4: Calculate the scattering point power matrix according to the least squares solution of the target scattering intensity; Step 5: Remove the smaller values in the scattering point power matrix to update it; Step 6: Repeat the operations of Step 2 to Step 5 until the maximum number of iterations is reached; Among them, Step 5 includes: 5a) Extract the scattering point power matrix The elements on the diagonal Among them, is a vector, including K elements, and diag represents extracting the diagonal elements of the matrix; 5b) Sort the vector in ascending order Among them, represents the vector obtained by sorting the vector The vector Index is the sorting index, where sort represents sorting the vector from largest to smallest; 5c) Truncate the vector ​ wherein, represents the k-th element of the vector , b is a set truncation parameter, and K is the number of azimuths of radar scanning; 5d) Update the scattering point power matrix Among them, Index(k) represents the k-th element of the vector Index.

2. The high-resolution imaging method of a scanning radar in a low signal-to-noise ratio situation according to claim 1, characterized in that, In Step 1, before modeling the echo signal of the scanning radar, it also includes: Perform range direction matched filtering on the echo signal of the scanning radar.

3. The high-resolution imaging method for a scanning radar in a low signal-to-noise ratio situation according to claim 1, characterized in that Step 1 includes: 1a) For the echo signal of the scanning radar, construct the following signal model: y = As + e Among them, the vector y = [y1, y2, … y N T is the echo observation data, the vector e = [e1, e2, …, e N T is the noise generated during the reception process, the vector s = [s1, s2, …, s K T is the scene scattering intensity, N is the number of snapshots, K is the number of azimuths scanned by the radar, and A is the steering matrix, and its expression is:​​​ h = [h1, h2, …, h L T is the pattern function of the radar antenna, and a k is the steering vector in the k-th direction, where 1 ≤ k ≤ K;​ 1b) Initialize the scattering point power matrix based on the signal model The expression is as follows: Among them, H represents conjugate transpose.

4. The high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions according to claim 3, wherein, In Step 2, the calculation formula of the covariance matrix is: Among them, R represents the covariance matrix, and H represents conjugate transpose.

5. The high-resolution imaging method of a scanning radar in a low signal-to-noise ratio situation according to claim 4, wherein Step 3 includes: 3a) Model the radar high-resolution imaging problem as a least squares problem, and the least squares loss function is as follows: Among them, Q(θ k ) represents the interference and noise matrix, and its expression is is the scattering point power matrix the k-th element in 3b) Calculate the least squares solution of the target scattering intensity, and the calculation formula is as follows: Among them, represents the signal of the target in the k-th azimuth.

6. The high-resolution imaging method for a scanning radar in a low signal-to-noise ratio situation according to claim 5, wherein In step 4, the elements of the scattering point power matrix are calculated according to the following formula:

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