COMPUTATIONALLY EFFICIENT PARTICLE FILTERING USING ADAPTIVE TECHNIQUES

We propose a computationally efficient particle filtering al orithm that adaptively chooses between the sequential impor tance resampling (SIR) particle filter and the unscented par ticle filter (UPF). The technique is based on the use of the Kullback-Leibler distance (KLD) sampling and the choice of either of the algorithms is governed by the error in estimati on. The SIR particle filter most opted among the variations of the particle filter because of the choice of the transitional pri or as the importance density and easy evaluation of weights. However, it can be inefficient for highly non-linear dynamic sys tems . In contrast, the UPF which uses the scaled unscented transform performs better than the SIR but is computationally more expensive. The proposed algorithm couples the easy evaluation of the weights and the faster sampling capabilities of the SIR filter with the improved accuracy of the UPF. We apply the technique to a scalar estimation problem and demonstrate through simulations that the new algorithm is more accurate than the SIR particle filter and is faster tha n the UPF for systems characterized by highly non-linear measurement models.