基于奇异谱熵函数的间断采样转发干扰抑制新方法。

A Novel Interference Suppression Method for Interrupted Sampling Repeater Jamming Based on Singular Spectrum Entropy Function.

机构信息

Beijing Institute of Remote Sensing Equipment, Beijing 100854, China.

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.

出版信息

Sensors (Basel). 2019 Jan 2;19(1):136. doi: 10.3390/s19010136.

DOI:10.3390/s19010136

PMID:30609765

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6339108/

Abstract

As a new type of jamming, the interrupted sampling repeater jamming (ISRJ) derived from the digital radio frequency memory (DRFM) technology, can generate coherent multiple false targets after pulse compression. At present, the traditional interference suppression method and its improved methods have insufficient characteristics and poor detection performance under the condition of low signal-to-noise ratio (SNR). Aiming at addressing this defect, this paper proposes an interference suppression method for ISRJ based on singular spectrum entropy function (SSEF) from the aspects of singular value decomposition (SVD) and information entropy theories. In this method, firstly, considering the local fine characteristics and extraction efficiency, an adaptive multi-scale segmentation (AMS) method is proposed. The purpose of this processing is to extend the salient characteristics while to smooth the similar ones. In AMS, the segmentation criterion based on average energy of segments and the constraint of minimum segmentation is also proposed, then the improved delay embedded matrix is established from the improved trajectory matrix by AMS and delay embedded mapping. Secondly, the singular spectrum of the improved delay embedded matrix is extracted by SVD. Thirdly, because the recognition algorithms based on singular spectrum analysis (SSA), classical SSE and other characteristics fail at low SNR, this paper proposes a characteristic named as SSEF retrieved from the Shannon entropy model. The following proposed entropy-based threshold detection is carried out on the echo signal to realize the band-pass filtering and interference suppression. Finally, experiment results show that in comparison with other interference suppression approaches, SSEF can increase the probability of target detection and the peak-to-side-lobe ratio (PSR) after pulse compression, which validates its stability to noise and jamming especially in the condition of low SNRs.

摘要

作为一种新型干扰，基于数字射频存储（DRFM）技术的间断采样转发干扰（ISRJ）在脉冲压缩后可产生相干的多个虚假目标。目前，传统的干扰抑制方法及其改进方法在低信噪比（SNR）条件下，特征不足，检测性能较差。针对这一缺陷，本文从奇异谱熵函数（SSEF）的奇异值分解（SVD）和信息熵理论出发，提出了一种基于 ISRJ 的干扰抑制方法。在该方法中，首先，考虑到局部精细特征和提取效率，提出了一种自适应多尺度分割（AMS）方法。该处理的目的是扩展显著特征，同时平滑相似特征。在 AMS 中，还提出了基于段平均能量的分割准则和最小分割约束，然后通过 AMS 和延迟嵌入映射从改进轨迹矩阵中建立改进的延迟嵌入矩阵。其次，通过 SVD 提取改进的延迟嵌入矩阵的奇异谱。第三，由于基于奇异谱分析（SSA）的识别算法、经典 SSE 和其他特征在低 SNR 下失效，本文提出了一种从香农熵模型中提取的特征，称为 SSEF。对回波信号进行基于熵的门限检测，以实现带通滤波和干扰抑制。最后，实验结果表明，与其他干扰抑制方法相比，SSEF 可以提高目标检测概率和脉冲压缩后的峰值旁瓣比（PSR），特别是在低 SNR 条件下，验证了其对噪声和干扰的稳定性。