基于不完全测距的移动传感器网络定位算法

Range-based localization algorithm for mobile sensor network with incomplete measurement

采用基于二次规划的无迹卡尔曼滤波及多维标度方法,研究高精度的移动传感器网络定位算法,从传感器网络整体定位角度出发,为移动传感器网络定位提供了新思路.首先对传感器网络单元建立符合实际的带约束的非线性相对运动模型;在此基础上重点考虑模型中的物理约束,采用基于二次规划的无迹卡尔曼滤波对节点间相对距离进行滤波估计;最后基于分布式的多维标度定位算法进行局部定位与拼合,给出不完全测距下的移动传感器网络定位算法.仿真结果表明,所提出的算法在相同测距误差下与其他定位算法相比定位精度更高,在不同连通度的传感器网络中,均能得到良好的定位效果.

Based on quadratic programming-unscented Kalman filter（QP-UKF） and multidimensional scaling-MAP（MDS-MAP） methods, this paper studies localization problem for the mobile sensor network with high accuracy. From the perspective of overall localization for the wireless sensor network, a new idea of mobile sensor network localization is provided. Firstly, a nonlinear dynamic relative motion model is established for sensor network units according to practical condition. By considering the constraints in the established model, the QP-UKF is introduced to estimate the actual ranges among nodes in each senor network unit. Then, based on the MDS-MAP method and estimated ranges,the whole mobile sensor network can be localized through clustering, local-localizing and merging. Finally, a complete range-based localization algorithm is proposed for the mobile sensor network with incomplete measurement. Several simulation examples illustrate that with the same range error ratio, the localization accuracy of the proposed algorithm is higher than the existing algorithms, and can perform well for the mobile sensor network with different connectivity.