室内定位中基于EFIM和距离协作的节点选择算法

协作定位网络中节点间(除了锚节点和锚节点间)大量的信息交换会造成计算复杂度的增加,冗余信息的交换还会造成能源浪费.为了解决这个问题,提出一种基于等效费希尔信息矩阵(EFIM)和距离协作的节点选择算法,并用位置误差界限的平方(SPEB)来描述位置精度.首先获取邻近节点到目标节点的距离作为辅助信息以提高定位精度,然后通过推导更新EFIM来计算SPEB,根据新提出的算法选择邻近节点作为辅助节点.仿真结果表明新提出的算法优于其他几种常用的算法,达到某一特定的定位精度要求时选择的邻近节点数量最少,同时还提高了能量的利用率.

Node Selection Algorithm Based on EFIM and Distance Collaboration in Indoor Localization

In cooperative localization network, the large amount of information exchange between the nodes (except anchor-to-anchor) can cause the increase of the computing complexity, and too much redundant information exchange would cause energy waste. To solve this problem, this paper presents a neighboring nodes selection algorithm based on the equivalent Fisher information matrix(EFIM) and distance collaboration, with the squared position error bound (SPEB) to characterize the location accuracy. We can obtain distances between the target node and neighboring nodes that are used as auxiliary information to improve the positioning accuracy, and calculate the SPEB after deducing and updating the EFIM. After that, the neighboring nodes are selected as the secondary nodes based on the proposed algorithm. Simulation results show that the proposed algorithm can select the minimum number of neighbors to achieve the specified positioning accuracy compared to other commonly used methods, while improve the utilization rate of energy.