A method and device for achieving super-resolution of a target azimuth

Abstract

The present invention provides a method, device, storage medium and electronic device for realizing super-resolution of a target azimuth, which relates to the technical field of radar signal processing. The method includes: acquiring target echo data on a range cell where the target is located; reconstructing the target echo data to obtain reconstructed echo data, wherein the reconstructed echo data is a sequence of data uniformly sampled at a preset sampling frequency; performing denoising processing on the reconstructed echo data to obtain denoised echo data; and inputting the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the range cell. The technical solution provided by the present invention can obtain more accurate target azimuth angle information.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and particularly to a method and device for achieving target azimuth super-resolution. Background Art

[0002] Radar azimuth super-resolution has been a major problem existing in azimuth mechanically scanned radars for a long time. The larger the size of the radar antenna aperture, the narrower its beam and the higher the azimuth resolution. However, usually restricted by many factors such as detection range, antenna installation conditions, and price cost, it is impossible to use a large-aperture radar antenna. Therefore, it is of great significance to achieve radar azimuth super-resolution through digital signal processing.

[0003] Radar azimuth super-resolution refers to the ability to resolve multiple targets on the same range cell by the radar within the same beam without changing the hardware conditions. The deconvolution principle can be used to effectively improve the radar azimuth resolution. Deconvolution azimuth super-resolution is mainly based on the beam scanning principle. The target echo signal received by the radar is obtained by convolving the target azimuth signal with the antenna pattern information, and theoretically, the reconstruction of the target azimuth signal can be achieved by using the convolution inversion method. Its core idea is to construct a suitable filter to represent the inverse function of the antenna pattern function, and use the echo signal as the input of the system to obtain the original azimuth information of the target.

[0004] However, the existing azimuth super-resolution algorithms with the transmitted signal in the form of combined pulses have the following problems:

[0005] 1. The parameters in the deconvolution filter cannot be accurately determined;

[0006] 2. The sampling of the obtained target echo data in the time domain shows non-uniform characteristics;

[0007] 3. The noise in the target echo data will be amplified equally when passing through the existing deconvolution filter.

[0008] The above problems make the target azimuth angle information obtained by the direct deconvolution azimuth super-resolution algorithm inaccurate. Summary of the Invention

[0009] In view of the above problems in the prior art, the present invention proposes a method and device for achieving target azimuth super-resolution, which can obtain more accurate target azimuth angle information.

[0010] To achieve the above object, the technical solution of the present invention is implemented as follows:

[0011] In a first aspect, an embodiment of the present invention provides a method for achieving target azimuth super-resolution, the method includes:

[0012] Obtain the target echo data on the range cell where the target is located;

[0013] Reconstruct the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency;

[0014] Denoise the reconstructed echo data to obtain the denoised echo data;

[0015] Input the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the range cell.

[0016] Preferably, the obtaining the target echo data on the range cell where the target is located includes:

[0017] Extract the target echo data on the range cell where the target is located from a pre-constructed range-Doppler matrix; wherein, the range-Doppler matrix stores a plurality of range cells and the target echo data corresponding to each range cell in the plurality of range cells.

[0018] Preferably, the range-Doppler matrix is pre-constructed in the following manner:

[0019] Determine the number of combined pulse groups within a beamwidth;

[0020] Arrange the target echo data received by the radar in chronological order based on the number of combined pulse groups within the beamwidth to obtain the range-Doppler matrix.

[0021] Preferably, each combined pulse group includes a long pulse and a short pulse; the following expression is used to determine the number of combined pulse groups within a beamwidth:

[0022]

[0023] wherein, N is the number of combined pulse groups within the beamwidth; ceil(x) is the smallest integer greater than x; θ 0.5 is the antenna half-power beamwidth; T l is the period of the long pulse; T s is the period of the short pulse; N lc is the accumulation times of the long pulse; N sc is the accumulation times of the short pulse; ω is the antenna rotation speed.

[0024] Preferably, the target echo data includes a long pulse echo sequence and a short pulse echo sequence; the reconstructing the target echo data to obtain the reconstructed echo data includes:

[0025] Extract the long pulse echo sequence or the short pulse echo sequence from the target echo data;

[0026] Interpolate the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data.

[0027] Preferably, the interpolating the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data includes:

[0028] Interpolate the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency by using a trained BP neural network to obtain the reconstructed echo data.

[0029] Preferably, the denoising the reconstructed echo data to obtain the denoised echo data includes:

[0030] Denoise the reconstructed echo data by using the wavelet threshold denoising method to obtain the denoised echo data; wherein, the wavelet basis used in the wavelet threshold denoising method is the db10 orthogonal wavelet, and the number of layers of wavelet decomposition for the reconstructed echo data is 4 layers.

[0031] Preferably, in the process of performing the denoising process by using the wavelet threshold denoising method, the threshold used is:

[0032]

[0033] where λ j is the threshold; μ is a preset adjustment factor; SNR1 is the signal-to-noise ratio of the reconstructed echo data; M is the number of wavelet coefficients decomposed in each layer; j is the decomposition scale; e is the natural constant.

[0034] Preferably, in the process of performing the denoising process by using the wavelet threshold denoising method, the following expression is used to obtain the wavelet estimation coefficient:

[0035]

[0036] where is the wavelet estimation coefficient; d j，k is the wavelet coefficient decomposed at scale j; λ j is the threshold used in the process of performing the denoising process by using the wavelet threshold denoising method; c is a quantization factor used to adjust the speed of threshold change between -λ j and λ j

[0037] ​Preferably, the distance unit where the target is located is determined in the following manner:

[0038] Receive the echo signal of the target;

[0039] Perform pulse compression processing on the echo signal to obtain the echo signal after pulse compression;

[0040] Perform MTI, MTD, and CFAR processing on the echo signal after pulse compression in sequence to obtain the distance unit where the target is located.

[0041] Preferably, the parameters of the deconvolution filter include: the ratio of the noise power spectrum to the signal power spectrum; the ratio of the noise power spectrum to the signal power spectrum is determined in advance using the following expression:

[0042] P = 1 / SNR2

[0043] where P is the ratio of the noise power spectrum to the signal power spectrum; SNR2 is the signal-to-noise ratio obtained during the CFAR processing.

[0044] In a second aspect, an embodiment of the present invention provides a device for realizing target azimuth super-resolution. The device includes:

[0045] An echo data acquisition unit for acquiring target echo data on the distance unit where the target is located;

[0046] A reconstruction unit for reconstructing the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is a sequence of uniformly sampled data at a preset sampling frequency;

[0047] A denoising unit for denoising the reconstructed echo data to obtain the denoised echo data;

[0048] An azimuth angle information acquisition unit for inputting the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the distance unit.

[0049] In a third aspect, an embodiment of the present invention provides a storage medium on which program code is stored. When the program code is executed by a processor, it realizes the method for realizing target azimuth super-resolution as described in any one of the above embodiments.

[0050] In a fourth aspect, an embodiment of the present invention provides an electronic device. The electronic device includes a memory and a processor. Program code that can run on the processor is stored on the memory. When the program code is executed by the processor, it realizes the method for realizing target azimuth super-resolution as described in any one of the above embodiments.

[0051] A method, device, storage medium, and electronic device for achieving super-resolution of a target azimuth provided by an embodiment of the present invention first obtain target echo data on a distance unit where a target is located, and then reconstruct the target echo data to obtain reconstructed echo data, where the reconstructed echo data is a sequence of data uniformly sampled at a preset sampling frequency; then, perform denoising processing on the reconstructed echo data to obtain denoised echo data, and input the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the distance unit. It can be seen that in the technical solution provided by the embodiment of the present invention, since the target echo data is reconstructed into a sequence of data uniformly sampled at a preset sampling frequency, the obtained result has higher accuracy compared with directly processing non-uniform echo data in the prior art. That is, the technical solution provided by the embodiment of the present invention can obtain more accurate target azimuth angle information.

[0052] In addition, the embodiment of the present invention uses the signal-to-noise ratio obtained when performing CFAR processing on the echo signal after pulse compression to determine the ratio of the noise power spectrum to the signal power spectrum of the deconvolution filter, and uses the wavelet threshold denoising method to perform denoising processing on the reconstructed echo data, effectively solving the technical problems in the prior art that the parameters in the deconvolution filter cannot be accurately determined and the noise in the echo data will be equally amplified when passing through the existing deconvolution filter, so as to further obtain more accurate target azimuth angle information. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The scope of the present disclosure can be better understood by reading the detailed description of the exemplary embodiments below in conjunction with the accompanying drawings. The accompanying drawings included are:

[0054] Figure 1 It is a flowchart of the method according to the embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of reconstructing the target echo data according to the embodiment of the present invention;

[0056] Figure 3 It is a block diagram of the deconvolution azimuth resolution process according to the embodiment of the present invention;

[0057] Figure 4 It is a schematic diagram of the resolution result when the echo data within one beam width is incomplete according to the embodiment of the present invention;

[0058] Figure 5 It is a schematic diagram of the target azimuth resolution result after reconstructing the echo data according to the embodiment of the present invention;

[0059] Figure 6 It is a schematic diagram of the target azimuth resolution result before using wavelet threshold denoising according to the embodiment of the present invention;

[0060] Figure 7 It is a schematic diagram of the target azimuth resolution result after wavelet threshold denoising in the embodiment of the present invention;

[0061] Figure 8 It is a structural diagram of the device in the embodiment of the present invention. Specific embodiments

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following will describe in detail the implementation method of the present invention in conjunction with the drawings and embodiments, so as to fully understand how the present invention uses technical means to solve technical problems and the implementation process of achieving technical effects and implement accordingly.

[0063] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0064] Example 1

[0065] According to an embodiment of the present invention, a method for achieving target azimuth super-resolution is provided. As Figure 1 shown, the method described in the embodiment of the present invention includes:

[0066] Step S101, obtaining target echo data on the range cell where the target is located;

[0067] In this embodiment, first, the following method is used to determine the range cell where the target is located: receiving the echo signal of the target; performing pulse compression processing on the echo signal to obtain the echo signal after pulse compression; and sequentially performing MTI (Moving Target Indication), MTD (Moving Target Detection), and CFAR (Constant False Alarm Rate) processing on the echo signal after pulse compression to obtain the range cell where the target is located.

[0068] Specifically, the echo signal of the target is the radar echo signal of the target. The radar echo signal of the target is pre-modeled. For a stationary radar platform, after pulse compression and range correction, the target echo on the same range cell can be expressed as:

[0069]

[0070] where σ0 represents the target scattering intensity, a is the antenna pattern function, θ0 is the target azimuth angle, θ is the beam scanning angle, B is the bandwidth of the transmitted frequency-modulated signal, R is the radar operating range, r0 is the target slant range, c is the speed of light, λ0 is the carrier wavelength, and i is the imaginary unit.

[0071] After obtaining the echo signal after the above pulse compression, a series of signal processing such as MTI (Moving Target Indication), MTD (Moving Target Detection), and CFAR (Constant False Alarm Rate) are successively performed on the echo signal after the pulse compression, and the initial target detection result can be obtained, and the distance cell d where the target is located is given. Among them, the distance cell d corresponds to the target slant range r0 in the above expression.

[0072] In this embodiment, the obtaining of the target echo data on the distance cell where the target is located includes: extracting the target echo data on the distance cell where the target is located from a pre-constructed range-Doppler matrix; wherein, the range-Doppler matrix stores a plurality of distance cells and the target echo data corresponding to each distance cell among the plurality of distance cells.

[0073] In this embodiment, the range-Doppler matrix is pre-constructed in the following manner: First, determine the number of combined pulse groups within a beam width; then, arrange the target echo data received by the radar in chronological order based on the number of combined pulse groups within the beam width to obtain the range-Doppler matrix. Among them, the number of combined pulse groups within a beam width represents the number of data at such a plurality of moments, that is, the number of rows of the range-Doppler matrix. And each column of the range-Doppler matrix is the target echo data corresponding to each distance cell.

[0074] In this embodiment, each of the combined pulse groups includes a long pulse and a short pulse; the following expression is used to determine the number of combined pulse groups within a beam width:

[0075]

[0076] where, N is the number of combined pulse groups within the beam width; ceil(x) is the smallest integer greater than x; θ 0.5 is the antenna half-power beam width; T l is the period of the long pulse; T s is the period of the short pulse; N lc is the accumulation times of the long pulse; N sc is the accumulation times of the short pulse; ω is the antenna rotation speed. Among them, the accumulation times of the long pulse and the accumulation times of the short pulse are determined according to the performance of the radar itself.

[0077] Specifically, in this embodiment, the target echo data is recorded according to range cells. First, according to the radar performance, the number of target echoes within a beamwidth is determined. For a combined pulse radar with a relatively high transmit frequency, usually within a beamwidth, there are multiple combined pulse groups, and the value of the number N thereof is determined by using the above expression. Then, the N combined pulse groups are arranged in chronological order to form a range-Doppler matrix, and the target echo data is recorded according to the range cell where the target is located.

[0078] Step S102: Reconstruct the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is sequence data uniformly sampled at a preset sampling frequency.

[0079] In this embodiment, the target echo data includes a long pulse echo sequence and a short pulse echo sequence; the reconstructing the target echo data to obtain the reconstructed echo data includes: extracting the long pulse echo sequence or the short pulse echo sequence from the target echo data; performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data.

[0080] In this embodiment, the performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data includes: using a trained BP neural network to perform interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data.

[0081] Currently, some radars increase their maximum detection range and reduce the radar detection blind area by transmitting combined pulses with different pulse widths. Therefore, when using the Wiener filtering method, the echo information of the target within a beamwidth may include multiple combined pulse groups. Therefore, affected by the transmit pulse width, detection environment, receiver noise, and filter parameters, etc., there will be an order-of-magnitude difference in the amplitude values of echo signals with different pulse widths; at the same time, due to the difference in the pulse repetition period of pulses with different widths, the sampling of the echo signal in the time domain shows non-uniform characteristics. At this time, it is no longer appropriate to directly use all echo information, and it is necessary to reconstruct and organize the echo data within a beamwidth by an appropriate method.

[0082] Specifically, due to the differences in the repetition periods of the long and short pulse echo sequences, the target echo signal in step S101 is actually a non-uniform sampling sequence. At the same time, affected by factors such as the pulse width of the transmitted pulse, the detection environment, the receiver noise, and the filter parameters, there will be an order-of-magnitude difference in the amplitude values of the echo signals with different pulse widths. Therefore, when considering the deconvolution filtering method, it is advisable to use the long and short pulse echo sequences separately. The resulting data incompleteness and sampling non-uniformity need to be processed through interpolation.

[0083] Taking the short pulse echo sequence as an example below, the method for reconstructing the target echo data is described:

[0084] As Figure 2 shown, assume that the number of combined pulse groups within a beam width is 3, the period of the long pulse echo sequence is 400 μs, accumulated 2 times, the period of the short pulse echo sequence is 150 μs, accumulated 3 times, and the sampling period T of the reconstructed echo data is 50 μs. Then, the pre-trained BP neural network is used to perform interpolation processing on the short pulse echo sequence at the preset sampling frequency (determined by the above 50 μs sampling period) to obtain the reconstructed echo data.

[0085] As Figure 2 shown, there are many blank areas at the sampling points of the short pulse echo sequence in the period T. In this embodiment, a three-layer BP neural network is selected to perform interpolation processing on this part of the time-domain information. The training set of the BP neural network is the known measurement information in the time domain, and its prediction sequence is the unknown sampling of the new short code sequence in the period T. Finally, a new short code time-domain sequence with complete and uniform sampling information is formed, that is, the above-mentioned reconstructed echo data.

[0086] In this embodiment, the method for reconstructing the long pulse echo sequence is the same as that of the short pulse echo sequence, that is, in this embodiment, only the long pulse echo sequence or the short pulse echo sequence needs to be selected for interpolation processing. In addition, this embodiment can also perform interpolation processing on the long pulse echo sequence and the short pulse echo sequence separately, and the obtained results can be mutually verified.

[0087] For the situation where the target azimuth sampling signal period is non-uniform or the sampling signal is incomplete due to the pulse emission system (such as combined pulses), the embodiment of the present invention proposes a method of using a three-layer BP neural network to perform interpolation reconstruction on the sampling sequence, which can ensure the completeness of the signal on the one hand and the uniformity of the echo signal sampling on the other hand.

[0088] Step S103, perform denoising processing on the reconstructed echo data to obtain the denoised echo data;

[0089] Radar echoes usually contain a large amount of noise information. When this noise passes through existing deconvolution filters, it will also be amplified equally, thus affecting the performance of the deconvolution filter and resulting in an inability to obtain ideal results in target azimuth resolution. Therefore, in this embodiment, denoising processing is also performed on the reconstructed echo data.

[0090] In this embodiment, performing denoising processing on the reconstructed echo data to obtain denoised echo data includes: using the wavelet threshold denoising method to perform denoising processing on the reconstructed echo data to obtain denoised echo data; wherein, the wavelet basis used in the wavelet threshold denoising method is the db10 orthogonal wavelet, and the number of layers of wavelet decomposition performed on the reconstructed echo data is 4 layers.

[0091] Specifically, the reconstructed echo data is a one-dimensional time-domain signal sequence, that is, wavelet threshold denoising processing is performed on this one-dimensional time-domain signal sequence. Here, considering selecting the db10 orthogonal wavelet with a higher vanishing moment order, a longer support length, and good regularity as the wavelet basis, and performing 4-layer wavelet decomposition on this one-dimensional time-domain signal sequence.

[0092] In the process of wavelet threshold denoising, the threshold serves as the boundary between noise and effective signals in splitting wavelet detail coefficients and plays an important role in the entire process. Selecting a suitable threshold helps improve the denoising effect. This embodiment gives a threshold value selection method that automatically adjusts with the signal-to-noise ratio and decomposition scale. Its central idea is: for signals with a higher signal-to-noise ratio, a smaller threshold is taken; as the decomposition scale is later, the threshold value decreases, and as the decomposition scale increases, the trend of the threshold decreasing slows down.

[0093] In this embodiment, in the process of performing the denoising processing using the wavelet threshold denoising method, the threshold used is:

[0094]

[0095] wherein, λ j is the threshold; μ is a preset adjustment factor; SNR1 is the signal-to-noise ratio of the reconstructed echo data; M is the number of wavelet coefficients in each layer of decomposition; j is the decomposition scale; e is the natural constant.

[0096] To obtain higher signal reconstruction accuracy, when estimating wavelet coefficients, the oscillation that may occur at ±λ (i.e., the above threshold) of the hard threshold function should be avoided, and the constant deviation between the estimated value of the wavelet coefficient in the soft threshold function and the wavelet coefficient after decomposition should also be weakened. This embodiment gives an improved threshold function for estimating wavelet coefficients.

[0097] That is, in this embodiment, in the process of performing the denoising processing using the wavelet threshold denoising method, the following expression is used to obtain the wavelet estimated coefficient:

[0098]

[0099] Among them, is the wavelet estimation coefficient; d j，k is the wavelet coefficient after decomposition at scale j; λ j is the threshold used in the denoising process using the wavelet threshold denoising method; c is the quantization factor, which is used to adjust the speed of the threshold change between -λ j and λ j The speed of the threshold change between them.

[0100] Estimate the wavelet coefficient through the above expression, and perform wavelet reconstruction of the one-dimensional signal according to the low-frequency coefficient of the nth layer of wavelet decomposition and the high-frequency coefficients of the first layer to the nth layer after quantization processing. The reconstructed signal is used as the signal after denoising the radar received echo, that is, the above-mentioned denoised echo data.

[0101] In this embodiment, by reasonably selecting the wavelet basis and decomposition scale in the wavelet transform and appropriately designing the wavelet threshold and threshold function, the wavelet threshold noise reduction processing of the radar echo is realized, achieving the purpose of improving the signal-to-noise ratio of the echo signal, so that the deconvolution method also has a good resolution effect when the signal-to-noise ratio is lower than 30 dB.

[0102] Step S104, input the denoised echo data into a pre-designed deconvolution filter, so that the deconvolution filter outputs the target azimuth angle information on the range cell.

[0103] The deconvolution method is essentially to design a suitable deconvolution filter to extract the target azimuth angle information from the received echo. According to the Wiener filtering criterion, the optimal deconvolution filter H(ω) is:

[0104]

[0105] Among them, G(ω) is the Fourier transform of the antenna pattern function, G * (ω) is the complex conjugate of G(ω), P n (ω) is the noise power spectrum, P s (ω) is the signal power spectrum. Since P n (ω), P s (ω) are unknown, and the magnitude of their ratio directly affects the characteristics of the deconvolution filter. Therefore, reasonable and effective values determine the performance of the filter in the azimuth resolution process.

[0106] That is, the parameters of the deconvolution filter include: the ratio of the noise power spectrum to the signal power spectrum; in this embodiment, the ratio of the noise power spectrum to the signal power spectrum is determined in advance by the following expression:

[0107] P = 1 / SNR2

[0108] Wherein, P is the ratio of the noise power spectrum to the signal power spectrum; SNR2 is the signal-to-noise ratio obtained when performing the CFAR processing in step S101.

[0109] In the prior art, in the design of the deconvolution filter, it is difficult to calculate the ratio of the noise power spectrum to the signal power spectrum. In order to obtain more accurate target azimuth angle information, in this embodiment, the signal-to-noise ratio obtained when performing CFAR processing is used to determine the ratio of the noise power spectrum to the signal power spectrum, solving the problem of the value of the ratio of the noise power spectrum to the signal power spectrum in the deconvolution filter.

[0110] As Figure 3 shown, let the deconvolution filter be H(ω), and the denoised echo data be Y(ω), then the target azimuth angle information X(ω) is:

[0111] X(ω) = Y(ω)·H(ω)

[0112] In Figure 3 , y(θ) is the time-domain signal of Y(ω), and x(θ) is the time-domain signal of X(ω).

[0113] The following proves the technical effect of this embodiment:

[0114] Suppose the radar beam width is 9°, the radar scanning angle range is -15° to 15°, the radar pulse repetition frequency is 1000 Hz, the antenna rotation speed is 100° / s, the target azimuth angles are 0° and 3° respectively, and the target effective scattering coefficients are all 1. Using the above method for simulation experiments, Figure 5 and Figure 7 can be obtained, and they are respectively compared with Figure 4 and Figure 6 .

[0115] From Figure 4 and Figure 5 , it can be seen that the target azimuth resolution result after reconstructing the echo data using the method described in this embodiment is significantly better than the resolution result when the echo data is incomplete.

[0116] From Figure 6 and Figure 7 , it can be seen that the target azimuth resolution result after wavelet threshold denoising using the method described in this embodiment is significantly better than the target azimuth resolution result before wavelet threshold denoising.

[0117] That is, the method provided in this embodiment can obtain more accurate target azimuth angle information compared with the prior art.

[0118] A method for achieving super-resolution of the target azimuth provided by an embodiment of the present invention first obtains target echo data on the range cell where the target is located, and then reconstructs the target echo data to obtain the reconstructed echo data, where the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency; then, performs denoising processing on the reconstructed echo data to obtain the denoised echo data, and inputs the denoised echo data into a pre-designed deconvolution filter, so that the deconvolution filter outputs the target azimuth angle information on the range cell. It can be seen that in the technical solution provided by the embodiment of the present invention, since the target echo data is reconstructed into sequence data uniformly sampled at a preset sampling frequency, compared with the prior art that directly processes non-uniform echo data, the obtained result has higher accuracy. That is, the technical solution provided by the embodiment of the present invention can obtain more accurate target azimuth angle information.

[0119] In addition, the embodiment of the present invention uses the signal-to-noise ratio obtained when performing CFAR processing on the echo signal after pulse compression to determine the ratio of the noise power spectrum to the signal power spectrum of the deconvolution filter, and uses the wavelet threshold denoising method to perform denoising processing on the reconstructed echo data, effectively solving the technical problems in the prior art that the parameters in the deconvolution filter cannot be accurately determined and the noise in the echo data will be equally amplified when passing through the existing deconvolution filter, so as to further obtain more accurate target azimuth angle information.

[0120] Example 2

[0121] Corresponding to the above method embodiment, the present invention also provides a device for achieving super-resolution of the target azimuth, as Figure 8 shown, the device includes:

[0122] An echo data acquisition unit 201, configured to acquire target echo data on the range cell where the target is located;

[0123] A reconstruction unit 202, configured to reconstruct the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency;

[0124] A denoising unit 203, configured to perform denoising processing on the reconstructed echo data to obtain the denoised echo data;

[0125] An azimuth angle information acquisition unit 204, configured to input the denoised echo data into a pre-designed deconvolution filter, so that the deconvolution filter outputs the target azimuth angle information on the range cell.

[0126] In this embodiment, the echo data acquisition unit 201 acquires the target echo data on the range cell where the target is located in the following manner:

[0127] Extract the target echo data on the range cell where the target is located from the pre-constructed range-Doppler matrix; wherein, the range-Doppler matrix stores a plurality of range cells and the target echo data corresponding to each range cell in the plurality of range cells.

[0128] In this embodiment, the range-Doppler matrix is pre-constructed in the following manner:

[0129] Determine the number of combined pulse groups within one beamwidth;

[0130] Arrange the target echo data received by the radar in chronological order based on the number of combined pulse groups within one beamwidth to obtain the range-Doppler matrix.

[0131] In this embodiment, each combined pulse group includes a long pulse and a short pulse; the following expression is used to determine the number of combined pulse groups within one beamwidth:

[0132]

[0133] where N is the number of combined pulse groups within one beamwidth; ceil(x) is the smallest integer greater than x; θ 0.5 is the antenna half-power beamwidth; T l is the period of the long pulse; T s is the period of the short pulse; N lc is the accumulation times of the long pulse; N sc is the accumulation times of the short pulse; ω is the antenna rotation speed.

[0134] In this embodiment, the reconstruction unit 202 includes:

[0135] An extraction unit for extracting the long pulse echo sequence or the short pulse echo sequence from the target echo data;

[0136] An interpolation processing unit for performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data.

[0137] In this embodiment, the interpolation processing unit obtains the reconstructed echo data in the following manner:

[0138] Perform interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency using a trained BP neural network to obtain the reconstructed echo data.

[0139] In this embodiment, the denoising unit 203 obtains the denoised echo data in the following manner:

[0140] The wavelet threshold denoising method is used to denoise the reconstructed echo data to obtain the denoised echo data; wherein, the wavelet basis used in the wavelet threshold denoising method is the db10 orthogonal wavelet, and the number of layers of wavelet decomposition performed on the reconstructed echo data is 4 layers.

[0141] In this embodiment, during the denoising process where the denoising unit 203 uses the wavelet threshold denoising method, the threshold used is:

[0142]

[0143] where λ j is the threshold; μ is a preset adjustment factor; SNR1 is the signal-to-noise ratio of the reconstructed echo data; M is the number of wavelet coefficients decomposed in each layer; j is the decomposition scale; and e is the natural constant.

[0144] In this embodiment, during the denoising process where the denoising unit 203 uses the wavelet threshold denoising method, the following expression is used to obtain the wavelet estimated coefficients:

[0145]

[0146] where is the wavelet estimated coefficient; d j,k is the wavelet coefficient decomposed at scale j; λ j is the threshold used during the denoising process using the wavelet threshold denoising method; c is a quantization factor used to adjust the rate of change of the threshold between -λ j and λ j .

[0147] Furthermore, the device described in this embodiment further includes:

[0148] a receiving unit, configured to receive the echo signal of the target;

[0149] a pulse compression processing unit, configured to perform pulse compression processing on the echo signal to obtain the echo signal after pulse compression;

[0150] a signal processing unit, configured to perform MTI, MTD, and CFAR processing on the echo signal after pulse compression in sequence to obtain the range cell where the target is located.

[0151] In this embodiment, the parameters of the deconvolution filter include the ratio of the noise power spectrum to the signal power spectrum; the ratio of the noise power spectrum to the signal power spectrum is determined in advance using the following expression:

[0152] P = 1 / SNR2

[0153] where P is the ratio of the noise power spectrum to the signal power spectrum; SNR2 is the signal-to-noise ratio obtained during the CFAR processing.

[0154] For the working principle, working process, and other content related to the specific implementation of the above device, reference can be made to the specific implementation of the method for achieving target azimuth super-resolution provided by the present invention, and the same technical content will not be described in detail here.

[0155] An apparatus for achieving target azimuth super-resolution provided by an embodiment of the present invention first obtains target echo data on the range cell where the target is located, then reconstructs the target echo data to obtain the reconstructed echo data, where the reconstructed echo data is sequence data uniformly sampled at a preset sampling frequency; then, denoises the reconstructed echo data to obtain the denoised echo data, and inputs the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the range cell. It can be seen that in the technical solution provided by the embodiment of the present invention, since the target echo data is reconstructed into sequence data uniformly sampled at a preset sampling frequency, compared with directly processing non-uniform echo data in the prior art, the obtained result has higher accuracy. That is, the technical solution provided by the embodiment of the present invention can obtain more accurate target azimuth angle information.

[0156] In addition, the embodiment of the present invention uses the signal-to-noise ratio obtained during the CFAR processing of the pulse-compressed echo signal to determine the ratio of the noise power spectrum to the signal power spectrum of the deconvolution filter, and uses the wavelet threshold denoising method to denoise the reconstructed echo data, effectively solving the technical problems in the prior art that the parameters in the deconvolution filter cannot be accurately determined and the noise in the echo data will be equally amplified when passing through the existing deconvolution filter, thereby being able to further obtain more accurate target azimuth angle information.

[0157] Example 3

[0158] According to an embodiment of the present invention, there is also provided a storage medium, on which program code is stored, and when the program code is executed by a processor, it implements the method for achieving target azimuth super-resolution as described in any one of the above embodiments.

[0159] Example 4

[0160] According to an embodiment of the present invention, an electronic device is further provided. The electronic device includes a memory and a processor. A program code that can run on the processor is stored on the memory. When the program code is executed by the processor, the method for achieving target azimuth super-resolution described in any one of the above embodiments is implemented.

[0161] A method, device, storage medium, and electronic device for achieving target azimuth super-resolution provided by an embodiment of the present invention first obtain target echo data on a distance unit where the target is located, and then reconstruct the target echo data to obtain the reconstructed echo data, where the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency; then, perform denoising processing on the reconstructed echo data to obtain the denoised echo data, and input the denoised echo data into a pre-designed deconvolution filter, so that the deconvolution filter outputs the target azimuth angle information on the distance unit. It can be seen that in the technical solution provided by the embodiment of the present invention, since the target echo data is reconstructed into sequence data uniformly sampled at a preset sampling frequency, compared with the prior art that directly processes non-uniform echo data, the obtained result has higher accuracy. That is, the technical solution provided by the embodiment of the present invention can obtain more accurate target azimuth angle information.

[0162] In addition, the embodiment of the present invention uses the signal-to-noise ratio obtained when performing CFAR processing on the pulse-compressed echo signal to determine the ratio of the noise power spectrum to the signal power spectrum of the deconvolution filter, and uses the wavelet threshold denoising method to perform denoising processing on the reconstructed echo data, effectively solving the technical problems in the prior art that the parameters in the deconvolution filter cannot be accurately determined and the noise in the echo data will be equally amplified when passing through the existing deconvolution filter, so as to further obtain more accurate target azimuth angle information.

[0163] The present invention solves the following technical problems:

[0164] 1. Give a method for obtaining the value of the ratio of the noise power spectrum to the signal power spectrum of the deconvolution filter in the radar azimuth super-resolution process.

[0165] 2. Give a method for reconstructing the radar echo information within a beam width to achieve the completeness of the radar echo and the uniformity of the sampling period.

[0166] 3. Give a method for reducing the noise of the radar echo to improve the signal-to-noise ratio of the echo, so that the deconvolution azimuth filtering method can also have a good resolution effect when the signal-to-noise ratio is lower than 30 dB.

[0167] The present invention has the following beneficial effects:

[0168] 1. The process of achieving target azimuth super-resolution by deconvolving the radar azimuth time-domain sampling signal is described in detail, which is beneficial to solving the problem of difficult target azimuth resolution within one beamwidth in the same range cell and has reference significance for improving the azimuth resolution of the radar.

[0169] 2. Artificial intelligence is introduced into the process of processing radar echo signals, realizing the reconstruction of target echo signals within one beamwidth, which has reference value for data reconstruction of incomplete and non-periodic sampling signals.

[0170] 3. Wavelet threshold denoising processing is implemented on the radar echo signal, enabling the deconvolution azimuth super-resolution method to also have good effects when the echo signal-to-noise ratio is lower than 30 dB.

[0171] 4. It demonstrates the feasibility of the wavelet threshold denoising method in radar echo signal processing. During the wavelet threshold denoising process, the wavelet basis and decomposition scale in wavelet transform are reasonably selected, and a new design method of wavelet threshold and threshold function based on prior information of signal-to-noise ratio is given.

[0172] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0173] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0174] In addition, the functional units in various embodiments of the present invention can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0175] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0176] Although the embodiments disclosed in the present invention are as above, the content described is only an embodiment adopted for the convenience of understanding the present invention and is not used to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains, without departing from the spirit and scope disclosed by the present invention, can make any modifications and changes in the form of implementation and details, but the protection scope of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for achieving super-resolution of the target azimuth, characterized in that, The method includes: Obtaining target echo data on the range cell where the target is located; Reconstructing the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency; Performing denoising processing on the reconstructed echo data to obtain the denoised echo data; Inputting the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the range cell; Wherein, the target echo data includes a long pulse echo sequence and a short pulse echo sequence; the reconstructing the target echo data to obtain the reconstructed echo data includes: Extracting the long pulse echo sequence or the short pulse echo sequence from the target echo data; Performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data; The performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency to obtain the reconstructed echo data includes: Performing interpolation processing on the long pulse echo sequence or the short pulse echo sequence at the preset sampling frequency by using a trained BP neural network to obtain the reconstructed echo data.

2. The method for achieving super-resolution of the target orientation according to claim 1, characterized in that The obtaining target echo data on the range cell where the target is located includes: Extracting target echo data on the range cell where the target is located from a pre-constructed range-Doppler matrix; wherein, the range-Doppler matrix stores a plurality of range cells and target echo data corresponding to each range cell in the plurality of range cells.

3. The method for achieving super-resolution of the target orientation according to claim 2, characterized in that The range-Doppler matrix is pre-constructed in the following manner: Determining the number of combined pulse groups within a beam width; Arranging the target echo data received by the radar in chronological order based on the number of combined pulse groups within the beam width to obtain the range-Doppler matrix.

4. The method for achieving super-resolution of the target orientation according to claim 3, characterized in that, Each of the combined pulse groups includes a long pulse and a short pulse; Using the following expression to determine the number of combined pulse groups within a beam width: Wherein, N is the number of combined pulse groups within the one beam width; ceil(x) is the smallest integer greater than x; θ 0.5 is the half-power beam width of the antenna; T l is the period of the long pulse; T s is the period of the short pulse; N lc is the accumulation times of the long pulse; N sc is the accumulation times of the short pulse; ω is the antenna rotation speed.

5. The method for achieving target azimuth super-resolution according to claim 1, characterized in that The performing denoising processing on the reconstructed echo data to obtain the denoised echo data includes: Performing denoising processing on the reconstructed echo data by using the wavelet threshold denoising method to obtain the denoised echo data; wherein, the wavelet basis used in the wavelet threshold denoising method is the db10 orthogonal wavelet, and the number of layers for wavelet decomposition of the reconstructed echo data is 4 layers.

6. The method for achieving super-resolution of the target orientation according to claim 5, wherein, During the denoising processing using the wavelet threshold denoising method, the threshold used is: Among them, λ j is the threshold; μ is a preset adjustment factor; SNR1 is the signal-to-noise ratio of the reconstructed echo data; M is the number of wavelet coefficients decomposed in each layer; j is the decomposition scale; e is the natural constant.

7. The method for achieving super-resolution of the target orientation according to claim 6, characterized in that During the denoising processing using the wavelet threshold denoising method, the following expression is used to obtain the wavelet estimation coefficient: Among them, is the wavelet estimation coefficient; d j,k is the wavelet coefficient after decomposition at scale j; λ j is the threshold adopted in the denoising process using the wavelet threshold denoising method; c is a quantization factor used to adjust the speed of threshold change between -λ j and λ j .

8. The method for achieving target azimuth super-resolution according to claim 1, characterized in that The range cell where the target is located is determined in the following manner: Receiving the echo signal of the target; Performing pulse compression processing on the echo signal to obtain the pulse-compressed echo signal; Successively performing MTI, MTD, and CFAR processing on the pulse-compressed echo signal to obtain the range cell where the target is located.

9. The method for achieving target azimuth super-resolution according to claim 8, characterized in that The parameters of the deconvolution filter include: the ratio of the noise power spectrum to the signal power spectrum; the ratio of the noise power spectrum to the signal power spectrum is determined in advance by the following expression: P = 1 / SNR2 where P is the ratio of the noise power spectrum to the signal power spectrum; SNR2 is the signal-to-noise ratio obtained during the CFAR processing.

10. An apparatus for achieving target azimuth super-resolution for implementing the method for achieving target azimuth super-resolution according to any one of claims 1-9, characterized in that, The device includes: An echo data acquisition unit for acquiring target echo data on the range cell where the target is located; A reconstruction unit for reconstructing the target echo data to obtain the reconstructed echo data; wherein, the reconstructed echo data is a sequence data uniformly sampled at a preset sampling frequency; A denoising unit for performing denoising processing on the reconstructed echo data to obtain the denoised echo data; An azimuth angle information acquisition unit for inputting the denoised echo data into a pre-designed deconvolution filter so that the deconvolution filter outputs the target azimuth angle information on the range cell.

11. A storage medium, on which program code is stored, characterized in that, When the program code is executed by a processor, it implements the method for achieving target azimuth super-resolution as described in any one of claims 1 to 9.

12. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program code that can run on the processor. When the program code is executed by the processor, it implements the method for achieving target azimuth super-resolution as described in any one of claims 1 to 9.

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