비선형 상태공간 모델을 위한 Point-Mass Filter 연구

• 영문명: A Study on the Point-Mass Filter for Nonlinear State-Space Models
• 발행기관: 강원대학교 산업기술연구소
• 저자명: 최영권(Yeongkwon Choe)
• 간행물 정보: 『산업기술연구』vol.43, 57~62쪽, 전체 6쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

목차

1. 서 론

2. Point-Mass Filtering 알고리즘

3. PMF의 성능 향상 연구

4. 결 론

References

키워드

Point-Mass Filter Bayes Filter Estimation Nonlinear State-Space Model