次优扩展卡尔曼滤波的无线传感器网络定位算法

Research of Localization Technology Based on Kalman Filter and FPGA in Wireless Sensor Networks

针对无线传感器网络节点在定位过程中,由于积累的节点测距误差和定位算法引入的误差会对定位的精度产生较大影响。为此在节点测距过程中,利用卡尔曼滤波算法线性最优的特点,提出了一种适用于无线传感器网络节点定位的次优扩展卡尔曼滤波算法模型。首先在评估坐标的过程中利用卡尔曼滤波的节点自适应调度方法,其次,将上述改进的过程引入到三角定位算法中,最后将引入前后的系统性能进行比较。通过仿真实验表明,该算法提高了测距和网络节点定位的精确度及模型的自适应性,并且减少了计算量和系统的功耗。

Strong influence on the positioning accuracy induces by the errors of node location and positioning algorithm in positioning process of node of wireless sensor. Using the linear optimal characteristic of Kalman Filter（KF）,a model based on the suboptimum KF according to the node positioning in Wireless Sensors Network is proposed： firstly,the adaptive scheduling algorithm of KF used in the process of evaluation coordinate; secondly,combine the process of improvement and the trigonometry locating algorithm; finally,compare the system performance. The simulation results indicate that the precision of measurement and the positioning accuracy of network node have been improved by this algorithm. In addition,the amount of calculation and the power consumption of system could be reduced in the same time.