基于高分辨一维多普勒像的雷达目标机动检测算法

目标机动过程中通常伴随着剧烈的姿态变化, 有利于雷达目标的横向高分辨成像, 从而为基于高分辨一维多普勒像(One-dimensional high resolution Doppler profile, 1D-HRDP)进行机动检测提供了可行性. 文中给出了高分辨一维多普勒像的成像公式后, 首先分析了成像对目标转角、积累时间和采样率的约束条件, 并给出了成像处理流程. 随后着重分析了机动检测原理, 得到了目标机动与非机动两类运动条件下姿态变化率的差异, 为机动检测提供了基础. 由于多普勒像的非平稳特性, 本文将目标机动检测问题视作机动、非机动两类分类识别问题, 并基于反向传播神经网络设计实现了机动检测器, 提出了两项新的机动检测算法性能评估指标, 与传统平均检测延迟指标相比能更准确地反映检测器的动态性能. 仿真实验表明本文提出的机动检测算法总体性能好于其他三种基于特征的机动检测算法.

A Radar Target Maneuver Detection Algorithm Based on theOne-dimensional High Resolution Doppler Profile

Target maneuvering is always accompanied with rapid attitude variations, which are helpful to achieve high cross-range resolution for coherent pulse radar. Thus it provides the feasibility to detect target maneuver based on one-dimensional high resolution Doppler profile (1D-HRDP). In this paper, the formulation of the HRDP is first introduced, and the profiling requirements are derived subsequently, including rotation angle, coherent processing time (CPI), and digital sampling rate. The profiling procedure is thus shown. The principle of maneuver detection based on the HRDP is then fully exploited. The difference of target attitude rates between nonmaneuvering and maneuvering motion modes is analyzed, which is the basis for maneuver detection. Due to the nonstationarity of the HRDP, the maneuver detection problem is reformulated as a pattern classification problem, where nonmaneuvering and maneuvering motion modes are distinguished. A novel detector is then developed based on the back propagation (BP) neural network. Two novel indices for performance evaluation are proposed. They reflect the dynamic performance of the maneuver detector more reasonably than the classical index, which is the average detection delay. Finally, the simulation results show that the proposed detector performs better than the other three up-to-date feature-based detectors as a whole.