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A fast detection algorithm of vibration targets for Dual-Channel SAR

  • role： First author第一作者
  • Affiliation：

    College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

  • Email：zhouyang17ahf@nudt.edu.cn
  • Introduction：周阳，1991年生,男,博士研究生, 研究方向为SAR信号处理及SAR对抗理论。E-mail: zhouyang17ahf@nudt.edu.cn
ZHOU Yang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

  • Email：shenaiguo_405@sina.com
  • Introduction：沈爱国，1975年生，副教授，研究方向为雷达信号处理、雷达干扰和雷达反干扰技术。E-mail: shenaiguo_405@sina.com
SHEN Aiguo1*,  
  • Affiliation：

    College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

BI Daping1

résumé

Target vibration generates special phase modulation of Synthetic Aperture Radar (SAR) echo signals called micro-Doppler effect, which can provide favorable information for micro-motion target detection and recognition. Mining such favorable information is of great significance to improve the performance of SAR systems. At present, research on SAR vibration target detection is still insufficient. The existing detection algorithms have many problems, such as large computational complexity, weak anti-clutter and anti-noise ability, and inability to adapt to multiple vibration targets.To solve SAR real-time detection of multiple vibration targets under a strong clutter and noise background, this paper proposes a novel vibration target detection algorithm. The detection algorithm uses the Displaced Phase Center Antenna (DPCA) cancellation technique to suppress ground clutter and accumulates DPCA signal along the azimuth direction to improve the anti-noise ability. The frequency spectrum of the SAR azimuth echo of a vibrating target has a high similarity with pulse sequence. Therefore, the pulse repetition interval transform (PRI transform) method of detecting repeated pulse sequences is chosen in this paper to realize the detection of vibration targets. The detection algorithm is a two-step process. The first step is to find the range cell positions of the vibration targets (called the aim range cells). This step mainly uses DPCA technology to eliminate clutter and accumulates the DPCA signal along the azimuth direction. Then, the vibration target range cells are determined by setting an appropriate threshold. The second step is to determine the number of vibration targets in the aim range cells obtained in the first step and estimate their vibration frequencies. This step mainly performs pseudo-pulse processing on the azimuth spectrum of the target range cell and then performs PRI transformation on the pseudo-pulse train to detect the vibration targets and estimate the vibration frequencies. The algorithm can detect the vibration targets under strong clutter and noise conditions, and it has high frequency estimation precision and a small amount of calculation. Even when multiple vibration targets are present in a single range cell, the algorithm is still applicable.Simulation results prove the correctness and high efficiency of the proposed algorithm. Results show that under the condition of weak Signal-to-Noise Ratio (SNR), the first step of the detection can determine the vibration target range cells well, and when the SNR>-40 dB, the detection probability of the aim range cell is higher than 95%. In the second step, all the vibration targets in each aim range cell are detected successfully, and the vibration frequency of each vibration target is estimated accurately. Compared with the GLRT algorithm and the Hough transform algorithm, the proposed algorithm has the advantages of small computational complexity and high detection speed. The total computation time of two-step detection does not exceed 0.6 s, which shows that the proposed algorithm is suitable for real-time detection. The quasidata detection results prove that detecting vibration targets in the actual scene is feasible.The algorithm outperforms previous ones in that it involves a fairly small amount of computation and exhibits better anti-clutter and anti-noise performance. Hence, this algorithm has high practical value for remote sensing vibration targets.

mots-clés

remote sensing;Synthetic Aperture Radar (SAR);vibration target;SFM signal;DPCA;PRI transformation;CFAR

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