利用分布式传感器网络以及数据融合方法来提高探测系统的检测与定位精度正在成为研究的热点.提出了一种应用于分布式传感器网络中的数据融合算法,通过对各个传感器节点的定位信息的加权求和来进行数据融合,用来提高探测系统目标定位的精度.算法采用两级自适应调整得到最优加权因子.首先利用线性最小方差估计(LMSE)算法得到权系数的初始值,然后利用训练节点和递归最小二乘(RLS)算法自适应地调整达到最优.对静态和运动目标的定位数据融合算法进行了仿真.仿真结果表明,相比单节点定位,融合算法的定位精度有约一到两个数量级的提高.%Distributed sensor networks and data fusion algorithms that improves the detection performance and localization accuracy of the detection system have become the research focus.A new data fusion algorithm of target positioning for the distributed sensor network is proposed which combines location data of each sensor node by weighted summation to improve the accuracy of target position.The coarse weight coefficients are be calculated by using the linear minimum square estimation (LMSE) criterion,and then are adapted according to the recursive least squares (RLS) algorithm and based on the train node to achieve the optimality.The number simulations of the data fusion algorithm for static and mobile targets positioning are given,and results show that compared with the single node localization,the positioning variance of fusion algorithm presented decreases on the order of 10-1 to 10-2.
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