基于RSSI等级的蒙特卡罗定位算法应用研究

感知节点的定位是无线传感网应用的基础。现有的静态定位算法无法应用于动态传感网。针对一类目标节点移动而锚节点静止的传感网应用，提出了一种RRMCL（RSSI Rank Monte Carlo Localization）定位算法。该算法以蒙特卡罗算法为基础，利用RSSI（Received Signal Strength Indication）值与距离的单调递减关系划分通信域，减少采样区域大小。为了避免锚节点共线出现定位失效的情况，引入共线影响角度，提出了一种约束策略。仿真结果表明，提出的RRMCL与现有的MCL和MCB定位算法相比，能有效缩小采样区域，提高了定位精度和速度。

Application and research on Monte Carlo localization algorithm based on RSSI rank

The localization of perceptive nodes is the foundation for WSN（Wireless Sensor Network）applications. The existing static localization algorithms can not be used in dynamic sensor networks. In this paper, the so-called RRMCL （RSSI Rank Monte Carlo Localization）localization algorithm is proposed, which is about WSN applications where target nodes are moving while anchor nodes are static. The algorithm based on MCL divides communication region to reduce the size of sampling area by using the monotonic decreasing relation between RSSI value and the distance. In order to avoid localization failure caused by anchor node collinearity, the algorithm puts forward a constraint strategy which brings collinearity impact angle. The simulation results show that the proposed RRMCL can effectively reduce sampling area and improve the localization accuracy and speed, comparing with existing MCL and MCB algorithms.