距离修正的混沌粒子群多维标度定位算法
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TP393

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国家自然科学基金 (61572435, 61472305); 陕西省自然科学基金(2015JZ002, 2015JM6311); 浙江省自然科学基金LZ16F020001); 宁波市自然科学基金(2016A610035); 空间测控通信创新探索基金(KJCK1608)


Multidimensional scaling localization algorithm based on matrix correction and chaotic particle swarm optimization
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    摘要:

    针对不规则网络以及网络空洞造成估计距离与欧氏距离相差较大,导致定位精度不足这一问题,提出一种距离修正的混沌粒子群多维标度定位算法(CMDS-CPSO).首先通过递推策略计算节点对距离,利用接收信号强度对距离加权修正,以减少距离误差,回避网络空洞问题.然后采用混沌粒子群算法对坐标转化参数问题进行优化,进一步降低坐标转换中参数所带来的影响.通过对比SPSO-MDS算法与MDS-DMC算法,仿真结果表明,距离修正的混沌粒子群算法能够明显改善节点定位精度,具有更好的鲁棒性和对不规则网络的适应性.

    Abstract:

    Against the problem of a great distance between estimated distance and actual Euclidean distance caused by irregular network and network hole that eventually results in insufficient localization accuracy, an improved multidimensional scaling localization algorithm based on matrix correction and chaotic particle swarm optimization(CMDS-CPSO) is proposed. Distance among each pair of nodes is calculated by recursive strategy and further weighted by the received signal strength, so as to reduce the distance error between estimated distance and actual Euclidean distance as well as avoid the problem of network hole. Then chaotic particle swarm optimization is adopted to solve the parameter problem during the coordinate conversion process, which could loosen the influence of parameters to a high degree. Compared with the SPSO-MDS algorithm and MDS-DMC algorithm, the simulations reveal that the proposed algorithm of CMDS-CPSO could not only significantly improve the localization accuracy of nodes but has better robustness and adaptability to irregular networks.

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引用本文格式: 齐小刚,刘兴成,刘立芳,王振宇,张权. 距离修正的混沌粒子群多维标度定位算法[J]. 四川大学学报: 自然科学版, 2018, 55: 483.

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历史
  • 收稿日期:2017-06-01
  • 最后修改日期:2017-08-13
  • 录用日期:2017-10-17
  • 在线发布日期: 2018-06-06
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