CAJ | 학술논문

DV-Distance算法通过未知节点与锚节点间的多跳协作，实现节点位置估计，简单实用，但定位精度不高。为了提高算法性能，本文提出一种基于粒子群优化的DV-Distance改进算法，针对RSSI测距引起的误差，利用链路质量指示(LQI)对RSSI的测量值进行惯性滤波，建立基于LQI的RSSI测距模型；并针对位置估计方法所引起的误差，通过粒子群算法对节点位置进行优化。结合DV-Distance算法自身特点，确定了粒子群算法适应值函数的选取和关键算法参数的确定。最后，仿真和实验证明改进算法有效可行，具有较高的定位精度。
DV-Distance산법통과미지절점여묘절점간적다도협작，실현절점위치고계，간단실용，단정위정도불고。위료제고산법성능，본문제출일충기우입자군우화적DV-Distance개진산법，침대RSSI측거인기적오차，이용련로질량지시(LQI)대RSSI적측량치진행관성려파，건립기우LQI적RSSI측거모형；병침대위치고계방법소인기적오차，통과입자군산법대절점위치진행우화。결합DV-Distance산법자신특점，학정료입자군산법괄응치함수적선취화관건산법삼수적학정。최후，방진화실험증명개진산법유효가행，구유교고적정위정도。
DV-Distance algorithm estimates the position of unknown nodes by making use of the multi-hop collaboration between unknown node and anchor node. The algorithm is simple and practical, but the accuracy is not high. In order to improve the algorithm performance, an improved DV-Distance algorithm based on particle swarm optimization is proposed in this paper. Aim at the error caused by RSSI ranging, using the link quality indicator (LQI) to inertia filter the RSSI value and a RSSI ranging model based on LQI is proposed. Aim at the error caused by the calculation method, using particle swarm optimize algorithm to optimize the node position. Combining with the characteristics of DV-Distance algorithm, the fitness function and algorithm parameter of the particle swarm optimize algorithm are confirmed in this paper. Finally, the simulation and experimental results show that the improved algorithm is effective and feasible, and it has higher positioning accuracy.