CAJ | 학술논문

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。
감지절점적정위시무선전감망응용적기출。현유적정태정위산법무법응용우동태전감망。침대일류목표절점이동이묘절점정지적전감망응용，제출료일충RRMCL（RSSI Rank Monte Carlo Localization）정위산법。해산법이몽특잡라산법위기출，이용RSSI（Received Signal Strength Indication）치여거리적단조체감관계화분통신역，감소채양구역대소。위료피면묘절점공선출현정위실효적정황，인입공선영향각도，제출료일충약속책략。방진결과표명，제출적RRMCL여현유적MCL화MCB정위산법상비，능유효축소채양구역，제고료정위정도화속도。
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.