一种权系数两级自调整的融合定位精度提高方法

在分布式传感器网络节点定位技术中，使用数据融合方法以提高探测系统的检测与定位精度正成为研究的热点。提出了一种应用于分布式传感器网络中的数据融合定位算法，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。该算法采用两级自适应调整得到最优加权因子，首先利用线性最小均方差（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，提出的融合算法的定位精度有约1—2个数量级的提高。

An approach for improving precision of fusion positioning based on weights coefficient twice stage self-adjusting

In node localization technology of distributed sensor networks, using data fusion method to improve detection performance and localization precision of detection system have become the research focus. Propose a new data fusion localization algorithm for distributed sensor networks, by weighted summation of location information of each sensor node to carry out data fusion and improve precision of target position. The initial value of weight coefficients are calculated by using linear least mean square error （ LMSE ） algorithm, and then are adaptively adjusted to achieve optimal using training node and recursive least square（RLS） algorithm. Simulations on data fusion algorithm for static and mobile targets positioning are given, and resu lts show that compared with the single node localization, positioning precision of the presented fusion algorithm increases 1 -2 order.