Improved sparrow search particle filtering target tracking algorithm

• Main Authors: Cheng, Yuqing, Liu, Xiaoming, Zhang, Hui
• Format: Conference Proceeding
• Language: English
• Published: SPIE 19.02.2024
• ISBN: 1510674446; 9781510674448
• ISSN: 0277-786X
• DOI: 10.1117/12.3021407

Aiming at the particle filtering algorithm's particle degradation and impoverishment easily lead to the decrease of filtering accuracy, which makes the target tracking accuracy and robustness decrease, a particle filtering algorithm based on the improved sparrow search algorithm (ISSA-PF) is proposed. Frog Leaping Algorithm (FLA) is used in the exploration phase of SSA to update the location of the discoverer. Self-adaptive adjustment of step size based on individual fitness values. Less adaptable individuals use larger step sizes to quickly jump out of local optima. The better adapted individuals use smaller step sizes to search the surrounding solution space more finely, thus improving the algorithm's optimization performance and search capability. Introducing a Levy flight strategy to simulate sparrow flight trajectories to perturb the optimal solution for each generation of the population. Expanding the algorithm's search range and accelerating convergence. The experimental results show that the algorithm proposed in this paper has higher target tracking accuracy as well as faster operation speed than the traditional Particle Swarm Particle Filtering algorithm (PSOPF), Gray Wolf Optimization Particle Filtering algorithm (GWO-PF), and Sparrow Search Particle Filtering algorithm (SSA-PF).