# Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches

arXiv: https://arxiv.org/abs/1308.2443 · HTML: https://mlsanity.com/paper/1308.2443/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2013.

## Built on by (most cited first, 7 of 7)

- [Improving the evaluation of samplers on multi-modal targets](https://mlsanity.com/paper/2504.08916/) (2025)
- [Multiparticle Kalman filter for object localization in symmetric environments](https://mlsanity.com/paper/2303.07897/) (2023)
- [Massively Parallel Reweighted Wake-Sleep](https://mlsanity.com/paper/2305.11022/) (2023)
- [Simulating counterfactuals](https://mlsanity.com/paper/2306.15328/) (2023)
- [ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs](https://mlsanity.com/paper/2510.21107/) (2025)
- [Ancestry Tree Clustering for Particle Filter Diversity Maintenance](https://mlsanity.com/paper/2509.24124/) (2025)
- [On the Interaction Between Chicken Swarm Rejuvenation and KLD-Adaptive Sampling in Particle Filters](https://mlsanity.com/paper/2511.12222/) (2025)

## Abstract (arXiv)

During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, Approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.