| Title | Particle Filtering Based on Biome Intelligence Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Yinuo, Liu, Shujuan, Zhou, Jingyuan, Sun, Tengxuan |
| Conference Name | 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC) |
| Date Published | June 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-4322-7 |
| Keywords | adaptive filtering, Biota intelligence, Cuckoo algorithm, Filtering, firefly algorithm, Initialize, Metrics, particle filter, Particle filters, Prediction algorithms, pubcrawl, resilience, Resiliency, Scalability, security, Sociology, Statistics, swarm intelligence, Technological innovation |
| Abstract | Particle filtering is an indispensable method for non-Gaussian state estimation, but it has some problems, such as particle degradation and requiring a large number of particles to ensure accuracy. Biota intelligence algorithms led by Cuckoo (CS) and Firefly (FA) have achieved certain results after introducing particle filtering, respectively. This paper respectively in the two kinds of bionic algorithm convergence factor and adaptive step length and random mobile innovation, seized the cuckoo algorithm (CS) in the construction of the initial value and the firefly algorithm (FA) in the iteration convergence advantages, using the improved after the update mechanism of cuckoo algorithm optimizing the initial population, and will be updated after optimization way of firefly algorithm combined with particle filter. Experimental results show that this method can ensure the diversity of particles and greatly reduce the number of particles needed for prediction while improving the filtering accuracy. |
| URL | https://ieeexplore.ieee.org/document/9539970 |
| DOI | 10.1109/SPAC53836.2021.9539970 |
| Citation Key | wang_particle_2021 |
Particle Filtering Based on Biome Intelligence Algorithm