SAR moving target imaging method based on low-bit quantized data

Abstract

The invention proposes a synthetic aperture radar moving target imaging method based on low-bit quantized data, which belongs to the technical field of radar. In this method, under the set scene, low-bit sampling is performed on the radar echo signal to obtain the corresponding low-bit quantized echo data; the synthetic aperture radar imaging result of the full observation scene is obtained by using the Omega‑K algorithm; target and extract the fuzzy area containing the moving target, design the corresponding parameterized motion compensation filter, and obtain the sparse representation corresponding to the fuzzy area; use the parameterized quantization hard threshold loop algorithm to eliminate the influence of quantization error, realize motion parameter estimation and The refocusing of the image of the moving target finally obtains the imaging result of the moving target. The invention processes low-bit quantized echo data, can realize imaging effects comparable to refocusing results obtained from accurate data, greatly reduces data volume, saves storage space of radar data, and improves transmission efficiency.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a synthetic aperture radar moving target imaging method based on low-bit quantized data. Background technique [0002] Moving target imaging technology is of great significance in the field of radar target detection, recognition and classification. The traditional methods based on matched filtering technology widely used in synthetic aperture radar (SAR) imaging can achieve better imaging results for stationary targets, such as back projection algorithm (BP), range-Doppler algorithm (RD), Chirp Scaling algorithm (CS) and Omega-K algorithm, etc. However, for a moving target, the uncertainty caused by the target motion will lead to blurring and defocusing in the imaging result obtained by the above method. Specifically, the target's range velocity will cause azimuth displacement; the target's azimuth velocity and range acceleration will cause azimuth defocus. Therefore, for moving...

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Problems solved by technology

However, its limitation is that it uses a fixed sparse dictionary, and there is no consideration of target motion in its signal model, so it cannot be applied to moving target scenes and cannot compensate for motion errors.

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