Unscented particle filtering method based on particle swarm optimization algorithm

Abstract

The invention provides an unscented particle filtering method based on a particle swarm optimization algorithm, which comprises the steps of: 1, initial time: obtaining a group of initial particles from an initial distribution p(x0), setting an initial average and variance of the group of initial particles; 2, sampling sequential importance; 3, updating weight; 4, obtaining an normalized weight; 5, sampling; 6, updating state; and 7, solving a globally optimum solution G(t) at the current time. The invention ensures that a particle swarm more trends to a high likelihood region before the weight is updated through a particle swarm optimization process, thereby solving the problem of sample depletion to a certain degree. The optimization process ensures particles far away from real state trends to an area with high occurrence probability of the real state, thereby improving the action effect of each particle. Compared with other intelligent optimization algorithms, the particle swarm optimization algorithm has the advantages of easy implementation and no adjustment on various parameters, lowers the particle number required by accurate estimation, and improves the computing efficiency of filtering.

Description

technical field [0001] The invention provides a filtering method, in particular to an unscented particle filtering method (PSO-UPF) using a particle swarm optimization algorithm. Background technique [0002] Nonlinear system state estimation problems widely exist in signal processing, navigation guidance, target tracking, financial analysis, artificial intelligence and other related fields. Unscented Kalman filter (UKF) and particle filter are a kind of nonlinear filtering methods that have been studied and applied widely in recent years. Compared with the traditional Extended Kalman Filter (EKF), UKF does not need to linearize the model, and uses the nonlinear model directly, avoiding the error introduced by local linearization and avoiding divergence in strong nonlinear systems. However, both EKF and UKF are based on Gaussian assumptions, so they are not applicable to many non-Gaussian models in engineering applications. An effective method to make up for the above shor...

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Problems solved by technology

At this stage, although the weight of the particles is not zero after each resampling, the number of effective particles decreases after resampling because the resampling copies too many particles with high weights. In this way, after several recursive calculations Finally, the effective particles are exhausted by the re-sampling step until the last sample with a weight of 1. At this time, the distribution of the sample actually evolves into a single-point distribution, which cannot reflect the real distribution

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