Chicken Swarm Kernel Particle Filter: A Structured Rejuvenation Approach with KLD-Efficient Sampling

• URL: http://arxiv.org/abs/2511.12222v1
• Date: Sat, 15 Nov 2025 13:55:29 GMT
• Title: Chicken Swarm Kernel Particle Filter: A Structured Rejuvenation Approach with KLD-Efficient Sampling
• Authors: Hangshuo Tian,
• Abstract summary: Particle filters (PFs) are often combined with swarm intelligence (SI) algorithms, such as Chicken Swarm Optimization (CSO)<n>This paper investigates the theoretical interaction between SI-based rejuvenation kernels and Kullback--Leibler divergence (KLD) sampling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Particle filters (PFs) are often combined with swarm intelligence (SI) algorithms, such as Chicken Swarm Optimization (CSO), for particle rejuvenation. Separately, Kullback--Leibler divergence (KLD) sampling is a common strategy for adaptively sizing the particle set. However, the theoretical interaction between SI-based rejuvenation kernels and KLD-based adaptive sampling is not yet fully understood. This paper investigates this specific interaction. We analyze, under a simplified modeling framework, the effect of the CSO rejuvenation step on the particle set distribution. We propose that the fitness-driven updates inherent in CSO can be approximated as a form of mean-square contraction. This contraction tends to produce a particle distribution that is more concentrated than that of a baseline PF, or in mathematical terms, a distribution that is plausibly more ``peaked'' in a majorization sense. By applying Karamata's inequality to the concave function that governs the expected bin occupancy in KLD-sampling, our analysis suggests a connection: under the stated assumptions, the CSO-enhanced PF (CPF) is expected to require a lower \emph{expected} particle count than the standard PF to satisfy the same statistical error bound. The goal of this study is not to provide a fully general proof, but rather to offer a tractable theoretical framework that helps to interpret the computational efficiency empirically observed when combining these techniques, and to provide a starting point for designing more efficient adaptive filters.

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