A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy

LI Ji; ZHOU Zhanhong; HE Honglin; LIU Wenguang; LI Yiqing

doi:10.11999/JEIT220532

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2284-2292

LI Ji, ZHOU Zhanhong, HE Honglin, LIU Wenguang, LI Yiqing. A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2284-2292. doi: 10.11999/JEIT220532

Citation:

LI Ji, ZHOU Zhanhong, HE Honglin, LIU Wenguang, LI Yiqing. A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2284-2292. doi: 10.11999/JEIT220532

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A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy

doi: 10.11999/JEIT220532

School of Aeronautical Manufacturing, Nanchang Hangkong University, Nanchang 330063, China

Funds: The National Natural Science Foundation of China (51665040), The Key Projects of Natural Science Foundation of Jiangxi Province (20202ACB202003), The Natural Science Foundation of Jiangxi Province (20212BAB211015)

Received Date: 2022-04-27
Accepted Date: 2022-08-02
Rev Recd Date: 2022-07-25

Available Online: 2022-08-04

Publish Date: 2023-06-10

Abstract

Abstract

To deal with the weight degradation and sample impoverishment problems of particle filter, a Particle Filter based on Harris Hawks Optimization improved by Encircling strategy (EHHOPF) is designed. Firstly, the global search strategy in Harris Hawks Optimization is replaced by an encircling prey strategy to fit the filtering environment. Additionally, Sigmoid function is introduced to construct the nonlinear prey escaping energy to achieve the balance between exploration and exploitation. Lastly, the selection scale factor is proposed to simplify the selection mechanism of searching strategies and nonlinear dynamic prey jump strength is constructed to guarantee the convergence efficiency as well. The simulation results exhibited that the proposed particle filter can effectively improve the state estimation accuracy, filtering stability and real-time performance than the standard particle filter and particle filters optimized by krill herd algorithm, bat algorithm, cuckoo search algorithm and grey wolf optimizer.
- Particle filter,
- Harris hawks optimization,
- Weight degradation,
- Sample impoverishment

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References(20)

References

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