基于多普勒与微多普勒联合利用的弱小目标检测与估计方法

Weak Targets Detection and Estimation Based on Joint Use of Doppler and Micro-Doppler

近年来,无人机(UAVs)等低慢小目标对现有低空空域管理带来了巨大挑战。这类目标由于其飞行高度低、飞行速度慢及雷达散射截面(RCS)面积小,导致其回波信噪比(SNR)低,传统基于目标多普勒信息的检测估计方法检测概率低,参数估计不准确。对于无人机类低慢小目标的检测估计,除了可以利用目标径向运动产生的多普勒信息外,还可以利用目标微动部件产生的微多普勒信息,通过有效聚集因微动而分散在多个多普勒单元格内的能量,可望实现目标信噪比的提升。该文针对旋翼类低慢小目标,充分挖掘目标回波中蕴含的多普勒信息和微多普勒信息,在随机集框架下对旋翼无人机目标的多普勒和微多普勒信息进行联合建模,提出一种基于(CBMeMBer)滤波器的多普勒和微多普勒联合检测估计方法,利用贝叶斯估计实现了目标多普勒信息和微多普勒信息的有效积累和融合利用,可以提高雷达低慢小目标的检测估计性能。仿真试验表明,该方法可实现对旋翼无人机目标的稳定检测与状态估计,相比于仅利用目标多普勒信息的传统检测方法,检测灵敏度提高了2 dB。

In recent years,low-altitude slow and small targets,such as Unmanned Aerial Vehicles(UAVs),have posed a great challenge to the management of existing low-altitude airspace.These targets have low echo Signal Noise Ratio(SNR)due to their low flight altitude,slow flight speed and small Radar Cross Section(RCS),which result in low detection probability and inaccurate parameter estimation by traditional detection and estimation methods based on Doppler information of target.In addition to the Doppler information generated by the radial motion of the target,the micro-Doppler information generated by the micro-motion parts of the target can also be used for the detection and estimation of low-altitude slow and small targets like UAVs,which is expected to improve the SNR of the target by aggregating the energy dispersed in multiple Doppler cells due to the micro-motion.In this paper,a joint Doppler and micro-Doppler detection and estimation method based on the Cardinality Balanced Multi-target Multi-Bernoulli(CBMeMBer)filter is proposed,which makes full usage of the Doppler and micro-Doppler information contained in the echoes of UAV targets.By jointly modelling the Doppler and micro-Doppler information of UAV targets under the framework of Random Finite Sets(RFS),effective integration and fusion of Doppler and micro-Doppler information can be achieved.This leads to a better detection and estimation performance of low-altitude slow and small targets.Simulation experiments show that the method can achieve stable detection and state estimation of UAV targets,and the detection sensitivity is improved by 2 dB compared with the traditional detection method that only uses target Doppler information.