An Improved Particle Filter Passive Location Method Based on Differential Squirrel Search Algorithm
Authors: 
Jie Yang, Ersen ZhangAuthors Info & Claims
Jie Yang, Ersen ZhangAuthors Info & ClaimsPages 1098 - 1104
Abstract
Particle filtering is a standard method for parameter estimation in passive location and has great application value in nonlinear and non-Gaussian systems. The standard particle filter (PF) is prone to the problem of particle weight degradation and particle dilution as the number of iterations increases, which affects the overall performance of the location algorithm. Aiming at this problem, a PF algorithm based on differential squirrel search algorithm (DSSA) optimization is proposed. The particles are divided into optimal individuals, sub-optimal individuals, and ordinary individuals according to the weight of the particles. The low-weight particles are moved closer to the position of the high-weight particles by simulating the predation behavior of squirrels, so that the location information of most particles can be retained. And seasonal monitoring condition is used to avoid the algorithm falling into local optimal. The simulation results show that the improved algorithm has a lower root mean square error (RMSE) than the standard PF algorithm and the improved PF algorithms of other intelligent optimization algorithms under non-Gaussian noise. The improved algorithm can accurately achieve the passive location of the moving target.
References
[1]
L. Liang-qun and S. Hu-Sheng, "An Improved Gaussian Particle Filtering Algorithm for Fixed Single Station Passive Location and Tracking," 2020 7th International Conference on Information Science and Control Engineering (ICISCE), 2020, pp. 265-269.
[2]
Li Lei, Gao Song, Chen Chaobo, Wang Kun, Miao Chunyu. "Review of Particle Filter," Foreign Electronic Measurement Technology, 2020, 39(06): 6-12.
[3]
W. Weiwen, R. Qunyan, L. Licheng and M. Li, "Geoacoustic Parameters Inversion by Combining Particle Filter and Genetic Algorithm," 2021 OES China Ocean Acoustics (COA), 2021, pp. 201-204.
[4]
Y. Xiaojing, J. Qingju and L. Xinke, "Research on GRNN-based Particle Filter Algorithm," 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019, pp. 2053-2057.
[5]
Y. -m. Tian and L. Chen, "Unscented Particle Filter algorithm based on artificial fish swarm algorithm," 2012 8th International Conference on Natural Computation, 2012, pp. 1123-1126.
[6]
Z. -d. Liu, Y. -m. Fang, L. Liu and X. -d. Zhao, "An improved particle swarm optimization particle filter algorithm based on harmony search," 2020 39th Chinese Control Conference (CCC), 2020, pp. 1661-1666.
[7]
Jena, B., Naik, M.K., Wunnava, A., Panda, R. (2021). A Differential Squirrel Search Algorithm. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore.
[8]
H. Zhong, H. Ruan and B. Sun, "Passive Locating Method Using Phase Difference Rate Algorithm Based on Least Squares Fitting," 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019, pp. 1504-1508.
[9]
X. Yang, Gang Liu and Jinku Guo, "A single observation passive location algorithm based on phase-difference and Doppler frequency rate of change," 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, pp. 1309-1313.
[10]
Z. Hui, W. Lifen, R. Yuan and G. Mengmeng, "An Improved Particle Filter Based on UKF and Weight Optimization," 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP), 2020, pp. 80-83.
[11]
P. Wang, Y. Kong, X. He, M. Zhang and X. Tan, "An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array," in IEEE Access, vol. 7, pp. 118343-118358, 2019.
[12]
L. J. Sankpal and S. H. Patil, "weWeb Page Re-Ranking using Squirrel Search Rank Algorithm," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020, pp. 271-278.
[13]
H. Zhong, H. Ruan and B. Sun, "An Algorithm of the DPFRC real-time Location for The Moving Emmiter by a Moving Singal Observer," 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2019, pp. 168-172.
Index Terms
- An Improved Particle Filter Passive Location Method Based on Differential Squirrel Search Algorithm
Recommendations
The Improved Particle Filter Algorithm Based on Weight Optimization
ISCC-C '13: Proceedings of the 2013 International Conference on Information Science and Cloud Computing CompanionParticle filter algorithm is to achieve recursive Bayesian filter through the simulation method of non-parameter Monte Carlo, It based on sequential importance sampling, and can not avoid particle degeneration problem, a way to overcome the particle ...
An Improved Particle Swarm Algorithm for Search Optimization
GCIS '09: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01To address the problem of space locus searching, a slowdown particle swarm optimization (SPSO) is proposed to improve the convergence performance of particle swarm from the position viewpoint. The particle swarm in SPSO is divided into many independent ...
Radar Target Tracking Algorithm Based On New Particle Swarm Optimization Particle Filter
ICNCC '21: Proceedings of the 2021 10th International Conference on Networks, Communication and ComputingThe particle filter based on particle swarm optimization algorithm has low precision and is easy to fall into local optimization, which is difficult to meet the needs of target tracking. In this paper, a new particle swarm optimization particle filter ...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 18Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)2
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format