贝叶斯推理运动轨迹相干累积的动目标检测方法

A moving target detector with coherent integration of Bayesian-inferred motion trajectories

雷达理论和实践都表明相干累积是机动目标探测行之有效的方法,但其性能会因脉冲回波处理间隔内未知距离和/或多普勒的徒动而降低。为解决这个问题,提出了一种贝叶斯推理运动轨迹相干累积的动目标检测方法。首先,通过对脉冲压缩回波信号的距离频率进行翻转,就可从未翻转与翻转后的回波信号相乘的傅里叶逆变换中,提取出慢时间维描述待探测多目标运动轨迹的时频信号。视该时频信号中各目标运动轨迹为状态变量,获得的时频信号为观测,建立起多目标运动轨迹的状态空间模型。这样,据贝叶斯滤波推理出的目标运动轨迹,构建出快-慢时间维联合的二维匹配滤波器,就能补偿目标高速/机动带来的未知距离和/或多普勒徒动。理论分析和仿真实验均证明:所提算法适用具有复杂且未知运动状态的目标,且呈现出了优于文献报道方法的性能。

It has been shown theoretically and practically that coherent integration is an effective method for moving target detection.Unfortunately,its performance is seriously damaged by the unknown range and/or Doppler frequency migrations in a coherent processing interval.To handle such a problem,a moving target detector with the coherent integration of Bayesian-inferred motion trajectories is proposed in this paper.Analyses show that by flipping the range-frequency of the pulse compressed echo signal,the time-frequency signal describing the motion trajectories of the detected targets in the slow-time domain is got by the inverse Fourier transform of the product of the original echo signal and the flipped one.When the time-frequency signal is observed and its phases describing the detected moving target trajectories are regarded as state variables,a state space model for inferring the motion trajectories of multiple targets is given.Based on the inferred target trajectories,the range-frequency-slow-time 2D matched filters are constructed to compensate the unknown range and/or Doppler frequency migrations in an echo signal.Numerical simulation results verify that the proposed method can be applied to high speed/maneuvering targets with unknown complex motion form,and has superior performance than the methods reported in the literature.