作者：杜太行1,2, 李静秋1, 江春冬1,2
作者单位：1. 河北工业大学控制科学与工程学院, 天津 300130;
2. 河北省控制工程研究中心, 天津 300130
关键词：信号源定位;粒子退化;萤火虫算法;粒子滤波
摘要：
为提高无线电信号源的定位精度，运用粒子滤波方法对其进行定位估计。针对粒子滤波存在的粒子退化问题，提出改进的萤火虫算法优化粒子滤波。首先对萤火虫算法的吸引度公式进行改进，并利用迭代时刻粒子最优值指导个体的移动过程。然后运用改进的萤火虫算法与粒子滤波机制相结合，使粒子趋向于高似然区域，提高粒子的有效性，避免粒子退化，提高粒子滤波算法的滤波精度。最后，将改进后的算法用于无线电信号源定位算法中并进行仿真试验。实验结果表明：该文提出的算法定位结果最大定位误差为0.23%，该算法相比粒子滤波算法的定位精度有很大的提高，是一种有效的、实用性较强的定位估计算法。
Signal source localization based on optimized particle filter by improved firefly algorithm
DU Taihang1,2, LI Jingqiu1, JIANG Chundong1,2
1. School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China;
2. Hebei Control Engineering Research Center, Tianjin 300130, China
Abstract: In order to improve the positioning accuracy of the radio signal, using particle filtering method for estimation of the location. According to the particle filter has the particle degradation problem, Firefly algorithm optimized particle filter is proposed to solve that problem. Firstly, the attraction degree formula of the firefly algorithm is improved, and the global optimal value is used to guide the individual moving process. Then combining the improved firefly algorithm and particle filter mechanism, the particles tend to the high likelihood region, which improves the effectiveness of the particles, and greatly improves the filtering effect of particle filter. Finally, the improved algorithm is used in the wireless signal source localization algorithm, and simulation experiments are carried out. The maximum localization error of the localization results of the new algorithm presented in this paper is 0.23% which shows that the new algorithm compared to particle filter positioning accuracy is greatly improved. It is an effective and practical localization estimation algorithm.
Keywords: signal source localization;particle degradation;firefly algorithm;particle filter
2017, 43(11): 96-101 收稿日期: 2017-03-06;收到修改稿日期: 2017-04-20
基金项目: 河北省教育厅重点项目（ZD2017216）
作者简介: 杜太行(1963-),男,天津市人,教授,博士生导师,研究方向为电器检测、计算机应用。
参考文献
[1] JACOBSON A R, SHAO X. On-orbit direction finding of lightning radio frequency emissions recorded by the FORTE satellite[J]. Radio Science,2016,37(4):1-20.
[2] CAKIR O, KAYA I, CAKIR O. Investigation the effect of the time difference of arrival sets on the positioning accuracy for source localization[C]//Signal Processing and Communications Applications Conference. IEEE,2014.
[3] YUAN S, SONG H, PATRICE M, et al. Gauss based auxiliary particle filter[C]//IEEE International Conference on Robotics and Biomimetics. IEEE,2017.
[4] 卢茹. 基于粒子滤波的信号源测向定位[D]. 天津:河北工业大学,2014.
[5] 邢家丽,杨奕飞. 基于粒子群优化的粒子滤波算法应用研究[J]. 科学技术与工程,2012,64(4):936-939.
[6] MAROULAS V, STINIS P. Improved particle filters for multi-target tracking[J]. Journal of Computational Physics,2012,231(2):602-611.
[7] 陈志敏,薄煜明,吴盘龙,等. 基于自适应粒子群优化的新型粒子滤波在目标跟踪中的应用[J]. 控制与决策,2013, 28(2):193-200.
[8] 张民,贾海涛,沈震. 基于遗传算法改进的粒子滤波重采样模型[J]. 电子科技大学学报,2015(3):344-349.
[9] YANG X S. Firefly algorithm stochastic test functions and design optimisation[J]. International Journal of Bio-Inspired Computation,2010,2(2):78-84.
[10] 田梦楚,薄煜明,陈志敏,等. 萤火虫算法智能优化粒子滤波[J]. 自动化学报,2016,42(1):89-97.
[11] 刘长平,叶春明. 一种新颖的仿生群智能优化算法:萤火虫算法[J]. 计算机应用研究,2011,28(9):3295-3297.
[12] 朱奇光,肖亚昆,陈卫东,等. 基于萤火虫算法改进移动机器人定位方法研究[J]. 仪器仪表学报,2016,37(2):323-329.
[13] FARAHANI S M, ABSHOURI A A, NASIRI B, et al. A gaussian firefly algorithm[J]. International Journal of Machine Learning & Computing,2011,1(5):448-453.
[14] 郁书好. 萤火虫优化算法研究及应用[D]. 合肥:合肥工业大学,2015.
[15] ZAMAN M A, MATIN M A. Nonuniformly spaced linear antenna array design using firefly algorithm[J]. International Journal of Microwave Science & Technology, 2012(5):256759.
