摘要
窄带雷达受发射信号带宽限制,距离向分辨率低,通常应用于目标检测和跟踪。窄带雷达可通过转动目标的微多普勒对其进行精确成像,为目标识别提供新思路。文中在窄带雷达成像基础上,根据目标散射点稀疏性,提出了基于贝叶斯压缩感知的自旋目标成像算法。仿真结果表明,方位欠采样条件下,比复数后向投影算法得到的图像更加尖锐,因而具有更高的分辨率。且与传统压缩感知方法相比,需要更少的信号。
Narrow - band radar which emits the signal restricted by bandwidth limitation has a low resolution in range profile, so usually applies to target detection and tracking. Narrow - band radar can be imaged precisely by the rotating target＇s micro - Doppler, providing a new idea of target recognition. Due to the characteristics of narrow - band radar echoes from spinning targets, an imaging method based on Bayesian compressive sensing （BCS） is pro- posed according to the sparsity nature of narrow - band radar echoes from spinning targets. Simulation results show that the proposed approach offers a sharp and sparse image absence of side - lobes which is the common problem in conventional complex - valued back - projection method imaging methods and has fewer artifacts than the conventional compressive sensing （CS） based methods.
出处
《电子科技》
2017年第2期94-97,共4页
Electronic Science and Technology
关键词
窄带雷达
微多普勒
贝叶斯压缩感知
自旋目标
narrow- band radar
micro- doppler
Bayesian compressive sensing
spinning targets
