摘要： 针对纯测距条件下移动传感器网络中的目标跟踪问题，提出一种基于非线性滤波和多维标度的目标跟踪算法。根据传感器和目标之间存在的相对运动，建立带约束的动态距离模型，利用无迹卡尔曼滤波算法提高模型对距离及距离变化率的估计精度。在此基础上，结合传感器自身的位置、速度等状态信息，使用加权多维标度方法估计目标位置和速度。仿真结果表明，在只有距离信息的情况下，该算法能够实现对目标位置的高精度定位，速度估计结果也能准确反映目标的真实运动情况，与ML-KF算法相比整体跟踪效果更好。

关键词: 移动传感器网络, 目标跟踪, 纯测距, 无迹卡尔曼滤波, 多维标度

Abstract: To address the target tracking problem for multiple mobile sensor networks in the case of range-only measurement,this paper proposes a target tracking algorithm based on non-linear filtering and Multi-Dimensional Scaling(MDS) method.According to the relative motion between sensors and the target,a dynamic distance model with constraints is established.Then the Unscented Kalman Filtering(UKF) algorithm is used to improve the estimation precision of the distance as well as the rate of distance change in the model.On this basis,the position,the velocity and other state information of the sensors are used for calculation by the weighted MDS method to estimate the position and velocity of the target.Simulation results show that,when only the information of distance is accessible,the proposed algorithm can provide highly precise positioning for the target,as well as velocity estimation that can accurately reflect the real motion state of the target.Generally,the proposed algorithm performs better than ML-KF algorithm in target tracking.

Key words: mobile sensors network, target tracking, range-only measurement, Unscented Kalman Filtering(UKF), Multi-Dimensional Scaling(MDS)

中图分类号: 

• TP18