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In order to deal with the particle degeneracy and impoverishment problems existed in particle filters, a modified sequential importance resampling(MSIR) filter is proposed. In this filter, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information(CMI) into the resampled particles. In the evolution, new particles are first produced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Then, new and old particles are recombined for the sake of raising the diversity among the particles. Finally, those particles who have low quality are eliminated. Through the evolution, all the particles retained are regarded as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampled particle, but also the particle degeneracy and the loss of diversity among the particles are mitigated, resulting in the improved estimation accuracy. Simulation results show the superiorities of the proposed filter over the standard sequential importance resampling(SIR) filter, auxiliary particle filter and unscented Kalman particle filter.
In this deal, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information (CMI) into the resampled particles. In the evolution, new particles are first produced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Through the evolution, all the particles retained are classified as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampled particle, but also the pa rticle degeneracy and the loss of diversity among the particles are mitigated, resulting in the improved estimation accuracy.