分布式的有向传感器网络目标跟踪算法

Distributed Target Tracking Algorithm in Directional Sensor Networks

与传统的全方位角度感应的全向传感器不同,有向传感器拥有区域性的感知能力.文中针对有向传感器网络中测量噪声与目标距离相关的跟踪问题,引入一个具有乘性噪声的距离测量模型,提出了一种基于贝叶斯估计的分布式目标跟踪算法.该算法利用了有向传感器所感知目标的有向感知区域信息,将目标估计约束在限制区域内.仿真实验结果表明,与传统的扩展卡尔曼滤波算法以及基于最大似然和卡尔曼滤波的联合估计算法相比,文中提出的跟踪算法能够准确、有效地跟踪复杂测量环境下的移动目标.

Unlike the traditional omnidirectional sensors,directional sensors always play their roles within a special range.Aiming at the target tracking with distance-dependent measurement noises in directional sensor networks （DSNs）,the distance-dependent measurement error of sensors is modeled as a multiplicative noise,and a distributed target tracking algorithm is proposed based on Bayesian estimation.This algorithm restrains the target estimation within a restricted rectangle area by using the range information of the target detected by directional sensors.Simulation results show that,as compared with the conventional extended Kalman filtering and the estimator combining the maximum likelihood with the Kalman filtering,the proposed algorithm can accurately and effectively track moving targets in complex measurement environments.