Radar detection and hrrp imaging integration method and system based on distance super-resolution

Abstract

The application discloses a radar detection and HRRP imaging integrated method based on distance super-resolution, and belongs to the technical field of radar signal processing, and comprises the following steps: S1, a narrow-band LFM signal is transmitted, echo is received, pulse compression and coherent accumulation are carried out, and a distance-Doppler domain two-dimensional signal is generated; S2, in a detection branch, a traditional two-dimensional cross false alarm rate CFAR is used to extract target distance and radial velocity; S3, in an imaging branch, a one-dimensional distance profile of a Doppler gate where the target is located is extracted, and super-resolution reconstruction is carried out through a regularization deconvolution algorithm; and S4, detection and imaging results are fused, and target parameters and a high-resolution range image are output; the application adopts a unified LFM waveform, and avoids the time resource fragmentation problem caused by narrow-band / wide-band signal switching.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, specifically to a method for simultaneously achieving target detection and high-resolution one-dimensional range profile (HRRP) under a narrowband linear frequency modulation (LFM) signal system, which is particularly suitable for airborne radar to detect and image moving targets. Background Technology

[0002] High-resolution range profiling (HRRP) imaging technology generates high-resolution scattering intensity distributions in the range dimension of a target using broadband linear frequency modulated signals and pulse compression techniques. Its core advantage lies in its intuitive principle and the fact that it requires no complex imaging conditions. Furthermore, HRRP technology can be combined with deep learning frameworks to achieve efficient matching and analysis by associating the target's scattering characteristics with its motion attitude information, thus providing crucial support for high-precision target identification and classification.

[0003] Traditional HRRP imaging methods rely on prior target information: first, the radar system needs to acquire preliminary azimuth and range information of the target through conventional detection tasks. Then, the dispatch center allocates beam positions for the target location in the next task cycle to perform HRRP imaging. This process separates target detection and HRRP imaging into different independent tasks, such as... Figure 1 As shown. Specifically, radar detection missions use narrowband linear frequency modulation (LFM) signals to match conventional target sizes to obtain the maximum signal-to-noise ratio, and combine this with Moving Target Detection (MTD) and Constant False Alarm Rate (CFAR) algorithms to achieve target detection and coarse measurement of position and velocity; HRRP imaging, on the other hand, needs to switch to wideband LFM signals to improve range resolution and obtain fine target scattering characteristics. This asynchronous mission has three main problems:

[0004] 1) Reduced real-time performance and accuracy. Target HRRP imaging results are highly sensitive to target attitude. Specifically, target recognition requires real-time matching of the target's motion trajectory attitude (output by the detection task) with the high-resolution range image (generated by the HRRP task). However, the time-division strategy leads to processing delays in the target detection-imaging-recognition sequential process. Moreover, for highly maneuverable targets, the time delay significantly reduces the matching accuracy of attitude and scattering features, directly affecting the accuracy of the recognition results.

[0005] 2) Signal-to-noise ratio loss. To maintain the same search data rate, the time-sharing task mechanism needs to compress the pulse accumulation time during the detection and imaging phases, resulting in a decrease in signal processing gain, especially in long-range or low-scattering-intensity scenarios where the target signal-to-noise ratio is severely degraded.

[0006] 3) High hardware complexity. Broadband imaging requires high sampling rates and large storage resources, increasing the system load and making it unsuitable for cost-constrained miniaturized, unmanned airborne radars. Summary of the Invention

[0007] To address the aforementioned problems, the purpose of this invention is to provide an integrated method for target detection and HRRP imaging without signal bandwidth switching, thereby solving the problems of wasted pulse resources, insufficient real-time performance, and limited resolution caused by the separation of target detection and HRRP imaging processes in traditional radar systems.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: an integrated method for radar detection and HRRP imaging based on range super-resolution, characterized by comprising the following steps:

[0009] Step S1: Transmit a narrowband LFM signal, receive the echo and perform pulse compression and coherent accumulation to produce a two-dimensional signal in the range-Doppler domain;

[0010] Step S2: In the detection branch, the target distance and radial velocity are extracted using the traditional two-dimensional lateral false alarm rate (CFAR).

[0011] Step S3: In the imaging branch, extract the one-dimensional distance profile of the Doppler gate where the target is located, and perform super-resolution reconstruction using a regularized deconvolution algorithm;

[0012] Step S4: Fuse the detection and imaging results, and output the target parameters and high-resolution distance image.

[0013] Furthermore, step S3 specifically includes:

[0014] Step S31: Extract the one-dimensional range profile of the Doppler gate where the target is located, construct a range-dimensional super-resolution model, and characterize the coupling relationship between the target scattering distribution and the system response. Its expression is:

[0015]

[0016] in, This is the range dimension observation data of the Doppler gate at the target location, obtained after pulse compression and coherent accumulation processing of the original radar multipulse echo. The distance convolution kernel function is... For the target scattering coefficient distribution, This refers to the random noise of the system receiver.

[0017] Step S32: Design a sparse-smooth regularization objective function to simultaneously constrain the localization of discrete strong scattering points and the recovery of continuous scattering structures;

[0018] Step S33: Solve the objective function based on iterative multiplication optimization and output the super-resolution HRRP image.

[0019] Furthermore, the expression for the sparse-smooth regularization objective function is:

[0020]

[0021] in, This is the range dimension observation data of the Doppler gate at the target location, obtained after pulse compression and coherent accumulation processing of the original radar multipulse echo. The distance convolution kernel function is... For the target scattering coefficient distribution, and This is the regularization parameter, used to adjust the weight ratio between the regularization term and the fidelity term, determined by the L-curve method. This is the optimal estimate of the target scattering coefficient distribution.

[0022] The iterative multiplication optimization adopts the Picard multiplication update rule, and the iterative formula is as follows:

[0023]

[0024] in, and For adjacent iterations, It is a solution The problem of norm nondifferentiability introduces a very small constant. It is a diagonal matrix. For vectors The Each element.

[0025] The present invention also discloses an integrated radar detection and HRRP imaging system based on range super-resolution, including a narrowband LFM signal transceiver module, a range-Doppler domain processing module, a CFAR detection module and a regularized super-resolution imaging module.

[0026] Compared with the prior art, the technical solution adopted in this invention has the following beneficial effects:

[0027] (1) A unified LFM waveform is adopted to avoid the problem of time resource fragmentation caused by narrow / wideband signal switching;

[0028] (2) By synchronously outputting target detection information and HRRP images in a single processing flow, the real-time performance of the system is significantly improved, and the signal-to-noise ratio and accuracy are avoided due to time segmentation.

[0029] (3) It retains the low complexity advantage of narrowband systems and reduces hardware implementation costs. Attached Figure Description

[0030] Figure 1This is a schematic diagram illustrating the separate processes of traditional target detection and HRRP imaging.

[0031] Figure 2 This is a schematic diagram of the integrated target detection and HRRP imaging method of the present invention.

[0032] Figure 3 This is a schematic diagram illustrating the coupling relationship between the target scattering distribution and the system response.

[0033] Figure 4 This is a schematic diagram of the target scattering coefficient distribution in an example.

[0034] Figure 5 This is a schematic diagram of the echo (three-dimensional space) result after pulse compression in an example.

[0035] Figure 6 This is a schematic diagram of the echo (planar projection) result after pulse compression in an example.

[0036] Figure 7 This is a schematic diagram of the echo (three-dimensional space) and target detection results after coherent accumulation in an embodiment.

[0037] Figure 8 This is a schematic diagram of the echo (planar projection) and target detection results after coherent accumulation in an embodiment.

[0038] Figure 9 for Figure 8 A schematic diagram of the distance image of the Doppler gate where the target is located.

[0039] Figure 10 for Figure 9 A schematic diagram of the range image after super-resolution using this embodiment.

[0040] Figure 11 This is a schematic diagram of the integrated target detection and HRRP imaging system of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

[0042] like Figure 2 As shown, this embodiment provides an integrated method for radar detection and HRRP imaging based on range super-resolution, which includes the following steps:

[0043] Step S1: Transmit a narrowband LFM signal, receive the echo and perform pulse compression and coherent accumulation to produce a two-dimensional signal in the range-Doppler domain;

[0044] Step S2: In the detection branch, the target distance and radial velocity are extracted using the traditional two-dimensional lateral false alarm rate (CFAR).

[0045] Step S3: In the imaging branch, extract the one-dimensional distance profile of the Doppler gate where the target is located, and perform super-resolution reconstruction using a regularized deconvolution algorithm;

[0046] Step S31: Construct a range-Doppler domain echo convolution model to characterize the coupling relationship between target scattering distribution and system response, such as... Figure 3 As shown;

[0047] Step S32: Design a sparse-smooth regularization objective function to simultaneously constrain the localization of discrete strong scattering points and the recovery of continuous scattering structures;

[0048] Step S33: Solve the objective function based on iterative multiplication optimization and output a super-resolution HRRP image;

[0049] Step S4: Fuse the detection and imaging results, and output the target parameters and high-resolution distance image.

[0050] The expression for the sparse-smooth regularization objective function is:

[0051]

[0052] in, This is the range dimension observation data of the Doppler gate at the target location, obtained after pulse compression and coherent accumulation processing of the original radar multipulse echo. The distance convolution kernel function is... For the target scattering coefficient distribution, and For weight parameters, This is the optimal estimate of the target scattering coefficient distribution.

[0053] The iterative multiplication optimization adopts the Picard multiplication update rule, and the iterative formula is as follows:

[0054]

[0055] in, and For adjacent iterations.

[0056] Example: Airborne radar detection of surface ships and HRRP imaging.

[0057] 1. System parameters:

[0058] a. Center frequency: 10GHz, LFM signal bandwidth: 20MHz; pulse width: 20us, pulse repetition frequency: 2000Hz;

[0059] b. Target range: 3000m, radial velocity: 6m / s, size: 65m, target signal-to-noise ratio: 30dB, target range-dimensional scattering coefficient distribution as follows: Figure 4 As shown, it includes two sets of discrete impact scattering points and two sets of continuously distributed trapezoidal and rectangular scattering structures, respectively simulating the strong scattering points and continuous scattering points in the actual target.

[0060] 2. Processing flow:

[0061] a. The radar transmits a narrowband LFM signal and receives 128 pulse echoes;

[0062] b. Perform pulse compression processing on the original echo, such as... Figure 5 and Figure 6 As shown;

[0063] c. After pulse compression, target energy is accumulated through coherent accumulation processing to complete target detection. The target distance is 3000m and the radial velocity is 6m / s. Figure 7 and Figure 8 As shown.

[0064] d. Extract and extract the one-dimensional range profile of the target Doppler gate (corresponding to a velocity of 6 m / s), such as... Figure 9 As shown, although the signal-to-noise ratio reaches 30 dB, the insufficient distance resolution due to the limited 20 MHz signal bandwidth leads to aliasing of scattering points, making it impossible to obtain a clear HRRP image.

[0065] e. Input the regularization deconvolution objective function and use the L-curve method to determine the regularization weight parameters;

[0066] f. Using Picard multiplication for updating, the optimal solution is obtained, and the super-resolution HRRP image is output, such as... Figure 10 As shown. It can be seen that, compared to Figure 9 The method of this invention successfully separated four sets of scattering points, which are closely similar to the original scattering point distribution, with a structural similarity (SSIM) of 0.9433.

[0067] like Figure 11 As shown, this embodiment also discloses an integrated radar detection and HRRP imaging system based on range super-resolution, including a narrowband LFM signal transceiver module, a range-Doppler domain processing module, a CFAR detection module, and a regularized super-resolution imaging module.

[0068] Although the present invention has been disclosed above with reference to preferred embodiments, the embodiments and accompanying drawings are not intended to limit the invention. Any person skilled in the art can make various changes or modifications without departing from the spirit and scope of the invention, and these changes will also be within the protection scope of the invention. Therefore, the protection scope of the present invention should be defined by the scope of the claims of this application.

Claims

1. A method for integrating radar detection and HRRP imaging based on range super-resolution, characterized in that, Includes the following steps: Step S1: Transmit a narrowband LFM signal, receive the echo and perform pulse compression and coherent accumulation to produce a two-dimensional signal in the range-Doppler domain; Step S2: In the detection branch, the target distance and radial velocity are extracted using two-dimensional lateral false alarm rate (CFAR). Step S3: In the imaging branch, extract the one-dimensional distance profile of the Doppler gate where the target is located, and perform super-resolution reconstruction using a regularized deconvolution algorithm; Step S4: Fuse the detection and imaging results, and output the target parameters and high-resolution distance image; Step S3 specifically includes: Step S31: Extract the one-dimensional range profile of the Doppler gate where the target is located, construct a range-dimensional super-resolution model, and characterize the coupling relationship between the target scattering distribution and the system response. Its expression is: in, This is the range dimension observation data of the Doppler gate at the target location, obtained after pulse compression and coherent accumulation processing of the original radar multipulse echo. The distance convolution kernel function is... For the target scattering coefficient distribution, This refers to random noise in the system receiver. Step S32: Design a sparse-smooth regularization objective function to simultaneously constrain the localization of discrete strong scattering points and the recovery of continuous scattering structures; Step S33: Solve the objective function based on iterative multiplication optimization and output a super-resolution HRRP image; The expression for the sparse-smooth regularization objective function is: in, This is the range dimension observation data of the Doppler gate at the target location, obtained after pulse compression and coherent accumulation processing of the original radar multipulse echo. The distance convolution kernel function is... For the target scattering coefficient distribution, and This is the regularization parameter, used to adjust the weight ratio between the regularization term and the fidelity term, determined by the L-curve method. This is the optimal estimate of the target scattering coefficient distribution; The iterative multiplication optimization adopts the Picard multiplication update rule, and the iterative formula is as follows: in, and The number of adjacent iterations. It is a solution The problem of norm nondifferentiability introduces a very small constant. It is a diagonal matrix. For vectors The Each element.

2. A system for implementing the method of claim 1, characterized in that, include: Narrowband LFM signal transceiver module, pulse compression module, range-Doppler domain processing module, CFAR detection module, and regularized super-resolution imaging module; The narrowband LFM signal transceiver module is responsible for generating narrowband LFM pulse train signals, downsampling them, and then transmitting the signals to the pulse compression module. The pulse compression module performs pulse compression processing on the received narrowband LFM pulse signals one by one, and sends the processing results to the range-Doppler processing module. The range-Doppler processing module coherently accumulates the pulse train data after pulse compression along the slow time dimension to extract Doppler information, and simultaneously transmits the processing results to the CFAR detection module and the regularized super-resolution imaging module. The CFAR detection module performs constant false alarm rate (CFAR) target detection on the data from the range-Doppler processing module and outputs target detection information. The regularized super-resolution imaging module sequentially extracts the one-dimensional distance data of each Doppler unit and generates high-resolution range image (HRRP) information through regularized super-resolution imaging processing.

Images