混沌粒子群鸡群融合优化的RSSI质心定位算法

针对传统接收信号强度指示（RSSI）定位算法定位精度低及粒子群优化（PSO）算法容易陷入早熟和局部最优解的问题，提出一种RSSI质心定位算法。通过RSSI测距技术计算各传感节点之间的距离，选择离未定位节点最近的3个锚节点和已定位节点，建立以未知节点坐标为参数的数学模型。在PSO算法的基础上运用混沌优化思想避免搜索过程陷入局部极小，并利用鸡群算法进行优化从而得到质量更好的解。实验结果表明，与原始质心定位算法、加权RSSI质心定位算法和PSO-RSSI质心定位算法相比，该算法具有较快的收敛速度和较高的定位精度。

RSSI Centroid Location Algorithm Optimized by Chaotic Particle Swarm and Chicken Swarm

When dealing with the traditional RSSI location algorithm, which is limited by low location accuracy, the Particle Swarm Optimization(PSO) method tends to fall into premature and local optimal solution.To address the problem, this paper proposes an RSSI centroid location algorithm.The algorithm employs the RSSI ranging technique to calculate the distance between the sensor nodes.Then it selects the located nodes and the three anchor nodes nearest the nodes that are not located, and builds a mathematical model that takes the coordinates of the unknown nodes as the parameters.On the basis of the PSO algorithm, the idea of chaotic optimization is used to avoid the search process falling into local minimum, and the Chicken Swarm algorithm is used for further optimization to get a better solution.Experimental results show that the new algorithm has higher convergence speed and location accuracy than the traditional centroid location algorithm, weighted RSSI centroid location algorithm and PSO-RSSI centroid location algorithm.