摘要
节点定位是无线传感器网络中最为关键的一项技术。针对无源定位的问题,提出一种到达时间差(TDOA)和到达信号增益比(GROA)联合定位算法,并且采用飞行机制的萤火虫算法(GSO)来求得最终结果。结合TDOA和GROA定位模型,引入辅助变量将方程伪线性化;然后采用修正两步加权最小二乘算法(TSWLS)来进行求解。并且在不影响收敛速度和精度的前提下,采用带有飞行机制的GSO算法来寻求目标定位的最优解,克服粒子群算法易陷入局部最优的缺点。仿真结果表明,该算法相比较TDOA算法,定位精度提高了23 d B,并且具有相对较高和较稳定的定位精度。
Node localization is one of the key technologies of wireless sensor network. To solve the problem of the passive target localization, this paper developed an adaptive time differences of arrival （TDOA） and gain ratios of arrival （GROA） localization with glowworm swarm optimization algorithm. First,it proposed an improved correction two-step weighted least-squares source localization method （TSWLS） using the TDOA and GROA localization model. This method introduced the auxiliary variable and did pseudo linear operation of nonlinear equation. Second, it used the GSO algorithm with flight mec, hanism to optimize the localization equations of the first step, which developed the principle of information sharing between individual and group. The algorithm could obviously decrease the energy consumption and quickly find the optimal solution. Specifically, the simulation experiments show that proposed algorithm has reduced the localization average error by 23 dB compared with TDOA algorithm. The results indicate that the proposed algorithm has relative higher and stable localization accuracy.
作者
邹东尧
刘碧微
杨威
Zou Dongyao Liu Biwei Yang Wei(College of Computer ＆ Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China)
出处
《计算机应用研究》
CSCD
北大核心
2017年第9期2768-2772,共5页
Application Research of Computers
基金
河南省高等学校重点科研项目(15A520109)
河南省科技厅科技攻关项目(112102210321)
河南省产学研合作项目(122107000022)
研究生科技创新基金资助项目
