A Study on Resampling Strategy of Intelligent Particle Filter Based on Genetic Algorithm

Haitao LIU; Yanming LIN; Yonghua CHEN; Ermin ZHOU; Bo PENG

doi:10.11999/JEIT200561

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3459-3466

Haitao LIU, Yanming LIN, Yonghua CHEN, Ermin ZHOU, Bo PENG. A Study on Resampling Strategy of Intelligent Particle Filter Based on Genetic Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3459-3466. doi: 10.11999/JEIT200561

Citation:

Haitao LIU, Yanming LIN, Yonghua CHEN, Ermin ZHOU, Bo PENG. A Study on Resampling Strategy of Intelligent Particle Filter Based on Genetic Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3459-3466. doi: 10.11999/JEIT200561

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A Study on Resampling Strategy of Intelligent Particle Filter Based on Genetic Algorithm

doi: 10.11999/JEIT200561

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
Suzhou Automotive Research Institue, Tsinghua University, Suzhou 215131, China

Funds: The National Natural Science Foundation of China (51765017), The Natural Science Foundation of Jiangxi Province (20202BABL204043), The Key Research and Development Projects of Jiangxi Province (20202BBEL53007)

Received Date: 2020-07-08
Rev Recd Date: 2020-12-09

Available Online: 2020-12-31

Publish Date: 2021-12-21

Abstract

Abstract

The intelligent Particle Filter (PF) based on the genetic algorithm can reduce particle degradation. An adaptive processing strategy for low weight particles is proposed for an Intelligent Particle Filter (IPF) based on the genetic algorithm. After the particles are separated and crossed, the genetic operators are optimized to deal with the low weight particles adaptively. Low weight particles determine whether they are the bottom particle according to the weight size. Then the bottom particles mutate directly, and the rest low-weight particles mutate randomly according to the mutation probability. Simulation results show that the performance of the Improved Intelligent Particle Filter (IIPF) is better than intelligent particle filter, general particle filter algorithms and extended Kalman filter. In the one-dimensional simulation experiment, the error of the improved intelligent particle filter is reduced by 10.5% and 8.5% compared with general particle filters and intelligent particle filter, and the improved intelligent particle filter has better convergence. In the multi-dimensional simulation experiment, the improved intelligent particle filter reduces the root-mean-square error and average error of the altitude by 8.5% and 7.5%, and the root-mean-square error and average error of the speed by 11.5% and 7.6%, respectively. Moreover, under the cases of multiplicative noise and non-Gaussian random noise, the improved intelligent particle filter still has more than 10% performance advantage.
- Particle Filtering (PF),
- Genetic algorithm,
- Particle degradation,
- Adaptive

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References(20)

References

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