基于改进粒子群算法的WSN节点定位技术研究

（郑州大学 电气工程学院，河南 郑州 450001）

摘 要：针对传感器节点定位技术存在定位不准确和使用局限性等问题，采用一种改进的粒子群算法来对基于TOA的极大似然估计法进行优化处理，以提高定位精度。自适应变异的粒子群算法是依照群体适应度变化率自适应调节惯性权重值，根据当前种群的平均粒距对种群中部分粒子进行变异处理。通过仿真分析测距误差、未知节点个数和节点通信半径对定位精度的影响，结果表明，相同测试条件下，改进的算法定位更加精确，并且能够提升粒子群算法跳出局部最优的能力，有效的避免了标准粒子群优化算法易早熟、陷入局部最优的缺点，最大概率的寻找到全局最优解。

关键字：节点定位；改进粒子群算法；极大似然估计；全局最优 中图分类号：TP391.9 文献标志码：A

WSN Node Localization Technology Research Based On Improved

PSO

FENG Dong-qing，JIANG Xiao-xiao

(School of Engineering,Zhengzhou University,Zhengzhou Henan 450001,China)

ABSTRACT:In view of the sensor node positioning technology problems such as inaccurate positioning and use limitations,in order to improve positioning accuracy,an improved particle swarm optimization algorithm is used to optimize the maximum likelihood estimation method based on TOA.Adaptive Particle Swarm Optimization with Mutation(APSOwM)can adaptively adjust inertia weight value in accordance with the fitness rate of the group,then,it uses the current population average seed spacing to the particles which is part of the population for variation process.Through the simulation analysis of ranging error,unknown nodes and node communication radius of influence on positioning accuracy.The experimental results show that under same test conditions the improved algorithm can achieve more accurate positioning,enhance the ability of PSO to jump out of local optimum and maximum probability search for the global optimal solution,at the same time effectively avoid the shortcomings of the standard particle swarm optimization algorithm which is easy to premature and fall into local optimum.

KEYWORDS:Node localization;Improved PSO;Maximum likelihood estimation;global optimal

常，定位算法分为基于测距和无需测距两

1.引言

类。基于测距定位算法是通过给定位节点

随着信息技术的日新月异，无线传感配备额外的检测器件（例GPS）来测量节点网络取得了迅猛的发展。在无线传感器网间点到点的角度或距离信息，然后利用三边络中，传感器网络节点定位是通过获取各测量法、多边测量法、三角测量法或个传感器节点在平面或空间中的绝对或相Min-Max法计算出未知节点的坐标信息，常对位置信息，从而获得监测区域的某传感用的测距方法有：RSSI、TOA、TDOA和

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器节点的其他信息，因此，节点的定位成AOA等，此种定为了无线传感器网络的关键技术之一。通