传感器阵列预测空域多信号分类目标定位跟踪

目标定位跟踪技术广泛应用于军事民用领域,是当前研究的热点与难点.提出了一种空域多信号分类-自回归粒子滤波(multiple signal classification autoregressive particle filter,MUSIC-ARPF)方法,定位跟踪地面目标.该方法使用多信号分类(multiple signal classification,MUSIC)算法估计目标波达方向(direction of arrival,DOA)并计算目标信号源位置,利用自回归(autoregressive model,AR)模型和粒子滤波(particle filter,PF)算法预测信号源下一时刻位置,进而自适应选择通带与阻带扇面进行空域滤波,同时调整MUSIC算法中谱峰搜索区域,提高DOA估计的分辨率,减少目标定位的扫描域.实验结果表明,空域MUSIC-ARPF方法能够减少目标定位时间,提高目标跟踪精度.

Sensor array based predicted spatial multi-signal classification method for target localization and tracking

Target localization and tracking technology,widely used in military and civil applications,is a hot and difficult research issue.This paper proposes a spatial multiple signal classification autoregressive particle filter(MUSIC-ARPF) method to locate and track ground objects.This method firstly uses multiple signal classification(MUSIC) algorithm to estimate the direction of arrival(DOA) of the target signal source and calculate the position of the target.Secondly,an autoregressive model particle filter(ARPF) algorithm is introduced to predict the position of the signal source at the next moment.Thirdly,both the pass band and stop band of the target spatial filter are self-adaptively selected for spatial filtering,and the spectral peak searching sector of the MUSIC algorithm is adjusted according to the predicted position,which can improve the resolution of the DOA estimation and narrow the algorithm scanning band.Experiment results show that the spatial MUSIC-ARPF method can effectively enhance both target localization speed and tracking accuracy.