基于改进樽海鞘群算法的到达时间差定位

doi:10.12066/j.issn.1007-2861.2237

上海大学学报(自然科学版) ›› 2022, Vol. 28 ›› Issue (2): 238-249.doi: 10.12066/j.issn.1007-2861.2237

基于改进樽海鞘群算法的到达时间差定位

马一鸣¹, 石志东¹(), 赵康²^,³, 贡常磊¹, 单联海²

1.上海大学特种光纤与光接入网重点实验室, 上海 200444
2.上海物联网有限公司, 上海 201899
3.华东师范大学软件工程学院, 上海 200062

收稿日期:2019-12-16 出版日期:2022-04-30 发布日期:2020-09-01
通讯作者: 石志东 E-mail:zdshi@shu.edu.cn
作者简介:石志东(1964--), 男, 研究员, 博士生导师, 研究方向为电磁场与微波技术、通信与信息系统. E-mail: zdshi@shu.edu.cn
基金资助:
国家重点研发计划资助项目(2019YFB2101602)

Time difference of arrival localization based on an improved salp swarm algorithm

MA Yiming¹, SHI Zhidong¹(), ZHAO Kang²^,³, GONG Changlei¹, SHAN Lianhai²

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
2. Shanghai Internet of Things Co., Ltd., Shanghai 201899, China
3. Software Engineering Institute, East China Normal University, Shanghai 200062, China

Received:2019-12-16 Online:2022-04-30 Published:2020-09-01
Contact: SHI Zhidong E-mail:zdshi@shu.edu.cn

摘要/Abstract

摘要：

针对室内到达时间差(time difference of arrival, TDOA)位置估计中的非线性最优化问题, 提出用改进的樽海鞘群算法搜索目标位置. 通过选择最优主基站构造改进的适应度函数, 使适应度函数可以更好地反映解的优劣程度, 提高了搜索精度. 在初始樽海鞘种群中引入近似解, 使全局搜索的步骤得到简化, 加快了算法前期收敛速度. 采用自适应跟随策略更新追随者位置, 解决局部开发低效问题, 加快了算法后期收敛速度. 仿真结果表明, 基于改进樽海鞘群算法的 TDOA 定位技术相比其他元启发式算法具有更高的定位精度和更快的收敛速度.

关键词: 室内定位, 到达时间差, 樽海鞘群算法, 元启发式算法

Abstract:

To address the nonlinear optimization problem of indoor time difference of arrival (TDOA) location estimation, an improved salp swarm algorithm (SSA) is proposed to search target locations. An improved fitness function is constructed by selecting the optimal master base station so that the fitness function can better reflect the quality of the solution, thereby enhancing search accuracy. The approximate solution is introduced into an initial salp population to simplify global exploration, and the convergence speed of the algorithm is accelerated in the early stage. An adaptive following strategy is used to update follower locations to solve the problem of low efficiency in local exploitation, which accelerates the algorithm convergence speed in the later stage. Simulation results show that the TDOA localization technology based on the improved SSA has higher localization accuracy and faster convergence speed than other meta-heuristic algorithms.

Key words: indoor localization, time difference of arrival (TDOA), salp swarm algorithm (SSA), meta-heuristic algorithm

中图分类号:

TP391.9

马一鸣, 石志东, 赵康, 贡常磊, 单联海. 基于改进樽海鞘群算法的到达时间差定位[J]. 上海大学学报(自然科学版), 2022, 28(2): 238-249.

MA Yiming, SHI Zhidong, ZHAO Kang, GONG Changlei, SHAN Lianhai. Time difference of arrival localization based on an improved salp swarm algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(2): 238-249.

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参考文献 23

[1]	Saeed N, Nam H, Al-Naffouri T Y, et al. A state-of-the-art survey on multidimensional scaling-based localization techniques[J]. IEEE Communications Surveys &Tutorials, 2019, 21(4): 3565-3583.
[2]	李荣冰, 王念曾, 刘建业, 等. 面向相对导航的 UWB 测距误差估计与补偿方法[J]. 仪器仪表学报, 2019, 40(5): 28-35.
[3]	周恭谦, 杨露菁, 刘忠. 改进的非完全约束加权最小二乘 TDOA/FDOA 无源定位方法[J]. 系统工程与电子技术, 2018, 40(8): 1686-1692.
[4]	Ho K C. Bias reduction for an explicit solution of source localization using TDOA[J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2101-2114. doi: 10.1109/TSP.2012.2187283
[5]	Lee K, Oh J, You K. TDOA-/FDOA-based adaptive active target localization using iterated dual-EKF algorithm[J]. IEEE Communications Letters, 2019, 23(4): 752-755. doi: 10.1109/LCOMM.2019.2899615
[6]	Sun Y M, Zhang F R, Wan Q. Wireless sensor network-based localization method using TDOA measurements in MPR[J]. IEEE Sensors Journal, 2019, 19(10): 3741-3750. doi: 10.1109/JSEN.2019.2892652
[7]	Amiri R, Behnia F, Noroozi A. An efficient estimator for TDOA-based source localization with minimum number of sensors[J]. IEEE Communications Letters, 2018, 22(12): 2499-2502. doi: 10.1109/LCOMM.2018.2876525
[8]	王生亮, 刘根友, 高铭, 等. 改进的自适应遗传算法在 TDOA 定位中的应用[J]. 系统工程与电子技术, 2019, 41(2): 254-258.
[9]	Yue Y G, Cao L, Hu J, et al. A novel hybrid location algorithm based on chaotic particle swarm optimization for mobile position estimation[J]. IEEE Access, 2019(7): 58541-58552.
[10]	陈涛, 王梦馨, 黄湘松. 基于樽海鞘群算法的无源时差定位[J]. 电子与信息学报, 2018, 40(7): 1591-1597.
[11]	王梦馨. 基于 TDOA/AOA 的多站无源定位与跟踪算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
[12]	Wolpert D H, Macready W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82. doi: 10.1109/4235.585893
[13]	高明柯, 陈一民, 张典华, 等. 基于改进蚁群算法的血管介入手术路径规划[J]. 上海大学学报(自然科学版), 2019, 25(2): 198-205. doi: 10.12066/j.issn.1007-2861.1906
[14]	Emary E, Zawbaa H M, Grosan C. Experienced gray wolf optimization through reinforcement learning and neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(3): 681-694. doi: 10.1109/TNNLS.2016.2634548 pmid: 28092578
[15]	Darabi A, Bagheri M, Gharehpetian G B. Highly accurate directional overcurrent coordination via combination of Rosen's gradient projection: complex method with GA-PSO algorithm[J]. IEEE Systems Journal, 2020, 14(1): 1171-1182. doi: 10.1109/JSYST.2019.2904383
[16]	刘彬, 范瑞星, 刘浩然, 等. 基于混合樽海鞘-差分进化算法的贝叶斯网络结构学习算法[J]. 通信学报, 2019, 40(7): 151-161.
[17]	Mirjalili S, Gandomi A H, Mirjalili S Z, et al. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114(1): 163-191. doi: 10.1016/j.advengsoft.2017.07.002
[18]	Mirjalili S. The ant lion optimizer[J]. Advances in Engineering Software, 2015, 83(6): 80-98. doi: 10.1016/j.advengsoft.2015.01.010
[19]	Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. doi: 10.1016/j.advengsoft.2016.01.008
[20]	Heidari A A, Mirjalili S, Faris H, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Systems, 2019, 97: 849-872. doi: 10.1016/j.future.2019.02.028
[21]	熊瑾煜, 王巍, 朱中梁. 基于泰勒级数展开的蜂窝 TDOA 定位算法[J]. 通信学报, 2004, 25(4): 144-150.
[22]	孙健, 付永领, 何杰, 等. 基于非线性规划的室内 TOA 测距值优化方法[J]. 北京航空航天大学学报, 2018, 44(8): 1636-1642.
[23]	孙铭阳, 颜国正, 刘大生, 等. 基于超宽带技术的强制戒毒人员实时定位系统[J]. 上海交通大学学报, 2020, 54(1): 76-84.

基于改进樽海鞘群算法的到达时间差定位

Time difference of arrival localization based on an improved salp swarm algorithm

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