Single-bit SAR target reconstruction method in jamming scenario

Abstract

The application belongs to the technical field of radar signal processing and imaging, and provides a single-bit SAR target reconstruction method in an interference scene, which comprises the steps of data acquisition, single-bit quantization, target reconstruction modeling, matrix form conversion, target intensity solving, target position solving and the like. In a strong interference background, the application carries out SAR target reconstruction based on single-bit quantization, can realize low-complexity and high-robustness imaging processing, and can realize target imaging under the condition of limited resources such as unmanned aerial vehicles and small satellites.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing and imaging technology, specifically relating to a single-bit SAR target reconstruction method under interference scenarios. Background Technology

[0002] Synthetic Aperture Radar (SAR), with its unique advantages of all-weather, all-day operation and high resolution, has become an irreplaceable detection method for tasks such as scene imaging, disaster assessment, and marine surveillance. However, the modern electromagnetic environment is becoming increasingly complex. Jammers can use Digital Radio Frequency Storage (DRFM) and smart modulation techniques to perform various types of interference on SAR, including noise suppression, coherent deception, and intermittent sampling and forwarding, resulting in strong noise or a large number of false targets in the images, which seriously weakens the accuracy of subsequent detection, identification, and positioning. Therefore, target reconstruction in jammed scenarios is of great significance. However, traditional high-resolution SAR systems rely on high-quantization bit (8–12 bit) analog-to-digital converters, which have high hardware costs and large data throughput. They are more prone to overload and saturation in jammed environments, making it difficult to meet the requirements of real-time countermeasures and lightweight platforms. Therefore, researching SAR target reconstruction methods based on single-bit quantization to achieve "low complexity and high robustness" imaging processing under strong jamming backgrounds has become a core technological bottleneck that urgently needs to be overcome in the field of radar countermeasures.

[0003] Current research mainly follows two approaches: "interference suppression + target reconstruction". Traditional frequency domain notch filtering and time-frequency masking methods have a certain effect on suppressing narrowband noise and transient interference by removing contaminated frequency bands or time-frequency units, but they lose useful signal energy and are powerless against coherent forwarding interference. Sparse reconstruction methods utilize the sparsity or low-rank characteristics of targets in the space-frequency-polarization domain, and employ techniques such as L1 regularization, structural sparsity, or low-rank decomposition to recover targets after projection into the interference subspace. However, they require manual setting of regularization parameters and fail to work with dense false targets or interference in the same subspace. In recent years, deep learning technology has been introduced into SAR imaging. Networks such as IRCNN and FFDNet transform imaging into a "denoising" task, performing well under known interference patterns, but with poor generalization ability to interference outside the training. Model-based DL (such as ADMM-Net and ISTA-Net) unfolds sparse iterations into network layers, taking into account physical consistency. However, the network depth is fixed, and it is prone to over-smoothing or ghosting in strong interference scenarios. Furthermore, while game theory and adversarial methods theoretically approximate Nash equilibrium through iterative optimization of waveforms or filters via a two-stage jammer-radar strategy, they suffer from high computational complexity and require a priori jamming models, making them unsuitable for real-time airborne imaging. Moreover, their reliance on high-precision echo data results in a large system data volume, hindering their application on smaller platforms such as UAVs. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a single-bit SAR target reconstruction method under interference scenarios, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows: A method for reconstructing a single-bit SAR target in an interference scenario, comprising: Step 1: SAR Echo Data Acquisition Step 2: Perform single-bit quantization on the acquired SAR radar received signal; Step 3: Establish the target reconstruction model; the target reconstruction model aims to minimize the squared L2 norm of the synthesized echo signal and the observed signal. Step 4: Transform the target reconstruction model into a matrix-form optimization model; Step 5: Solve for the target strength; Step Six: Solve for the target location; Step 7: Update the target location.

[0005] Furthermore, in step two, single-bit quantization is represented as: in, The analog signal received by the radar. It is a single-bit quantized signal.

[0006] Furthermore, in step three, the target reconstruction model is represented as: Where m and n are the number of real targets and false targets, respectively. i and j Indexes for real targets and fake targets, respectively. and These are dictionaries for real targets and fake targets, respectively. For the first i The strength of a real target, For the first j The strength of a false target, For the first i The location of the real target For the first j The location of a false target. For the first i The positional error of the actual target For the first j The positional error of a false target For grid spacing, It is a single-bit quantized signal.

[0007] Furthermore, in step four, the target reconstruction model is transformed into the following model: .

[0008] Furthermore, in step five, when solving for the target strength, the following formula is used: in, l This represents the number of iterations.

[0009] Furthermore, in step six, when determining the target location, the following formula is used: .

[0010] Furthermore, in step seven, when updating the target position, the following formula is used: in, For the updated target position vector, For the first l The initial target position vector for the next iteration. For the first l The optimal position error vector obtained in the next iteration.

[0011] The present invention has the following beneficial effects: (1) In terms of data acquisition, traditional high-precision systems require the use of high-precision analog-to-digital converters for data acquisition, which results in high hardware costs. This invention is based on single-bit data and only requires a one-bit analog-to-digital converter, which greatly reduces the system complexity.

[0012] (2) In terms of data storage, traditional high-precision systems collect a huge amount of data, requiring a large amount of storage space to store radar echo data. This invention only requires a small amount of data storage space, greatly reducing storage requirements.

[0013] (3) In terms of data transmission, a large amount of data will consume a lot of communication bandwidth during transmission. Since the present invention only requires a small amount of data, it can greatly save transmission bandwidth.

[0014] (4) In terms of data processing, traditional high-precision systems require floating-point operations for data processing. This invention can improve computational efficiency by utilizing logical operations. Attached Figure Description

[0015] Figure 1 For flowcharts; Figure 2 For energy error; Figure 3 This represents the positional error. Detailed Implementation

[0016] The following will be described in conjunction with embodiments of the present invention. Figures 1-3The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0017] This invention performs SAR target reconstruction based on single-bit quantization under strong interference backgrounds, achieving low-complexity and high-robustness imaging processing, and enabling target imaging under resource-constrained conditions such as UAVs and small satellites.

[0018] like Figure 1 Specifically, this invention proposes a single-bit SAR target reconstruction method under interference scenarios, comprising: Step 1: SAR echo data acquisition; Step 2: Perform single-bit quantization on the acquired SAR radar received signal.

[0019] in, The analog signal received by the radar. It is a single-bit quantized signal.

[0020] Step 3: Establish the target reconstruction model as follows: Where m and n are the number of real targets and false targets, respectively. i and j Indexes for real targets and fake targets, respectively. and These are dictionaries for real targets and fake targets, respectively. For the first i The strength of a real target, For the first j The strength of a false target, For the first i The location of the real target For the first j The location of a false target. For the first i The positional error of the actual target For the first j The positional error of a false target This represents the grid spacing.

[0021] Step 4: Transform the target reconstruction model into the following model: Step 5: Target Strength Calculation: in,l This represents the number of iterations.

[0022] Step Six: Target Location Determination Step 7: Update target location: Among them, among them, For the updated target position vector, For the first l The initial target position vector for the next iteration. For the first l The optimal position error vector obtained in the next iteration.

[0023] When performing iterative optimization, repeat steps five through seven.

[0024] like Figure 2 , Figure 3 The radar parameters are: bandwidth 300MHz, pulse width .01 The modulation frequency is 3×10¹⁵ Hz / s, wavelength is 0.03 m, synthetic aperture length is 1 m, beamwidth is 0.03 rad, minimum slant range is 10 km, velocity is 150 m / s, and Doppler bandwidth is 300 Hz. Imaging the target yields the energy and position errors for target reconstruction. The results show that this invention can reconstruct both real and false targets well, effectively solving the problem of lightweight SAR target reconstruction in interference scenarios.

[0025] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, alterations, or substitutions made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reconstructing a single-bit SAR target under interference scenarios, characterized in that, include: Step 1: SAR Echo Data Acquisition Step 2: Perform single-bit quantization on the acquired SAR radar received signal; Step 3: Establish the target reconstruction model; the target reconstruction model aims to minimize the squared L2 norm of the synthesized echo signal and the observed signal. Step 4: Transform the target reconstruction model into a matrix-form optimization model; Step 5: Solve for the target strength; Step Six: Solve for the target location; Step 7: Update the target location.

2. The single-bit SAR target reconstruction method under interference scenarios according to claim 2, characterized in that, In step two, single-bit quantization is represented as: in, The analog signal received by the radar. It is a single-bit quantized signal.

3. The single-bit SAR target reconstruction method under interference scenarios according to claim 1, characterized in that, In step three, the target reconstruction model is represented as: Where m and n are the number of real targets and false targets, respectively. i and j Indexes for real targets and fake targets, respectively. and These are dictionaries for real targets and fake targets, respectively. For the first i The strength of a real target, For the first j The strength of a false target, For the first i The location of the real target For the first j The location of a false target. For the first i The positional error of the actual target For the first j The positional error of a false target For grid spacing, It is a single-bit quantized signal.

4. The single-bit SAR target reconstruction method under interference scenarios according to claim 2, characterized in that, In step four, the target reconstruction model is transformed into the following model: 。 5. The single-bit SAR target reconstruction method under interference scenarios according to claim 4, characterized in that, Step 5, when solving for the target strength, the following formula is used: in, l This represents the number of iterations.

6. The single-bit SAR target reconstruction method under interference scenarios according to claim 5, characterized in that, Step six, when determining the target location, use the following formula: 。 7. The single-bit SAR target reconstruction method under interference scenarios according to claim 6, characterized in that, Step 7, when updating the target position, use the following formula: in, For the updated target position vector, For the first l The initial target position vector for the next iteration. For the first l The optimal position error vector obtained in the next iteration.

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