Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
권호 표지
DOI QR코드

DOI QR Code

Rao-Blackwellized Multiple Model Particle Filter Data Fusion algorithm

Rao-Blackwellized Multiple Model Particle Filter자료융합 알고리즘

  • Received : 2011.08.01
  • Accepted : 2011.08.30
  • Published : 2011.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

It is generally known that particle filters can produce consistent target tracking performance in comparison to the Kalman filter for non-linear and non-Gaussian systems. In this paper, I propose a Rao-Blackwellized multiple model particle filter(RBMMPF) to enhance computational efficiency of the particle filters as well as to reduce sensitivity of modeling. Despite that the Rao-Blackwellized particle filter needs less particles than general particle filter, it has a similar tracking performance with a less computational load. Comparison results for performance is listed for the using single sensor information RBMMPF and using multisensor data fusion RBMMPF.

일반적으로 비선형 시스템에서 particle filter가 Kalman Filter보다 표적추적 성능이 뛰어나다고 알려져 있다. 그러나 particle filter는 많은 연산량을 요구하는 단점이 있다. 본 논문에서는 particle filter 보다 적은 particle의 수, 즉 적은 연산량으로 동일한 성능을 가지는 Rao-Blackwellized particle filter의 모델의 민감성을 줄인 Rao-Blackwellized Multiple Model Particle Filter(RBMMPF)의 알고리즘을 소개하고 이에 다중센서 정보를 융합하는 자료융합 기법을 적용하였다. 시뮬레이션을 통해 단일센서 정보를 이용한 RBMMPF 표적추적 성능과 다중센서정보를 융합한 RBMMPF의 표적추적 성능을 비교, 분석하였다.

Keywords

References

  1. Branko Ristic, Sanjeev Arulampalam, and Neil Gordon, Beyond the Kalman Filter. Artech House Publishers, 2004.
  2. Casella, G. and Robert, C.P. (1996). Rao-Blackwellisation of sampling schemse, Biometrick 83(1):81-94 https://doi.org/10.1093/biomet/83.1.81
  3. Doucet, A., de Freitas, J. F. G., Murphy, K. and Russell, S. (2000). "Rao Blackwellised particle filtering for dynamic Bayesian networks.", Uncetainty in Artificial Intelligence.
  4. A. Doucet, S. Godsill, and C. Andrieu, "On sequential monte Carlo sampling methods for Bayesian filtering," Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000 https://doi.org/10.1023/A:1008935410038
  5. Doucet, A., de Freitas, and Gordon, N. Sequential Monte Carlo Methods in Practice. Springer-Verlang New York, 2001
  6. DAVID L. HALL and JAMES LLINAS, "An Introduction to Multisensor Data Fusion", Proceedings of the IEEE, Vol, 85, No 1, January 1997
  7. Ng Gee Wah, and Yang Rong, Comparison of Decentralized Tracking Algorithms", 2003 ISIF