Deterministic resampling: Unbiased sampling to avoid sample impoverishment in particle filters

Authors:
Tiancheng Li;Tariq Pervez Sattar;Shudong Sun
Affiliations:
Center for Automated and Robotics NDT, London South Bank University, London SE1 0AA, UK and School of Mechatronics, Northwestern Polytechnical University, Xi'an 710072, China;Center for Automated and Robotics NDT, London South Bank University, London SE1 0AA, UK;School of Mechatronics, Northwestern Polytechnical University, Xi'an 710072, China
Venue:
Signal Processing
Year:
2012

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Abstract

A novel resampling algorithm (called Deterministic Resampling) is proposed, which avoids uncensored discarding of low weighted particles thereby avoiding sample impoverishment. The diversity of particles is maintained by deterministically sampling support particles to improve the residual resampling. A proof is given that our approach can be strictly unbiased and maintains the original state density distribution. Additionally, it is practically simple to implement in low dimensional state space applications. The core idea behind our approach is that it is important to (re)sample based on both the weight of particles and their state values, especially when the sample size is small. Our approach, verified by simulations, indicates that estimation accuracy is better than traditional methods with an affordable computation burden.