Transactions of Nanjing University of Aeronautics & Astronautics
An Improved Gaussian Particle Filter Algorithm Using KLD-Sampling
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Abstract:
To adjust the samples of filtering adaptively, an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence (KLD)-sampling (KLGPF) is proposed in this paper. During the process of sampling, the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function. KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly. Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly. Compared with the particle filter algorithm using KLD-sampling (KLPF), the speed of KLGPF increases by 28% under the same conditions.
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Clc Number:
TP274
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