Huan Sha, Dai Gane, Luo Gaoyong, Ai Shan
School of Physics and Electronic Engineering, Guangzhou University, Guangzhou 510006, China.
Advanced Institute of Engineering Science for Intelligent Manufacturing, Guangzhou University, Guangzhou 510006, China.
Sensors (Basel). 2019 Jul 25;19(15):3279. doi: 10.3390/s19153279.
The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.
间歇采样转发干扰（ISRJ）被认为是一种用于相干雷达探测的高效欺骗干扰方法。然而，当前针对ISRJ干扰的对抗方法在检测微弱回波时可能会失效，特别是当干扰机的发射功率相对较高时。在本文中，我们提出了一种基于贝叶斯压缩感知（BCS）的针对ISRJ的新型对抗方案，在该方案中，对于单目标和多目标场景，在相对较大的信噪比（SNR）范围内都可以重建稳定的目标信号。通过推导ISRJ干扰策略，仅提取未受干扰的不连续时间段来为重建算法构建稀疏目标模型。应用一种有效的交替迭代来优化和求解稀疏目标模型的最大后验估计（MAP）。仿真结果表明了所提方案在低信噪比或大干扰比情况下的鲁棒性。此外，与传统的快速傅里叶变换（FFT）或贪婪稀疏自适应匹配追踪算法（SAMP）相比，所提算法在栅瓣电平以及目标检测/误检测概率方面都有显著提高。
