Particle filtering method based on spherical simplex unscented Kalman filter

Abstract

The invention provides a particle filtering method based on spherical simplex unscented Kalman filter. The particle filtering method comprises the steps of: initializing a particle and a weight value thereof; generating a particle through importance sampling; updating and normalizing the particle weight value; sampling again; outputting a result; and entering a next time step. The invention mainly improves an importance sampling step and obtains importance probability density by adopting a SSUKF (Spherical Simplex Unscented Kalman Filter) algorithm based on SSUT (Spherical Simplex Unscented Transformation). Compared with PF (Particle Filter), EKPF (Extended Kalman Particle Filter) and standard unscented particle filter, SSUPF (Spherical Simplex Unscented Particle Filter) can acquire the precision equivalent to UPF (Unscented Particle Filter). On the other hand, because the SSUT adopts sampling points, i.e. sigma points, which are distributed in a spherical way, the quantity of the sampling points is far less than the UT (Unscented Transformation), and the advantage on the aspect of computing efficiency is gradually obvious in a high dimensional system.

Description

technical field [0001] The invention belongs to the fields of signal processing, artificial intelligence, target tracking and computer vision, and specifically relates to a particle filter method. Background technique [0002] Nonlinear filtering methods are widely used in navigation guidance, positioning, signal processing, finance, artificial intelligence and many other fields. Extended Kalman filtering (EKF) is an earlier method, which has high computational efficiency, but the filtering accuracy is limited, and the applicable model is also limited. With the development of computer technology, Unscented Kalman Filter (UKF) and Particle Filter have gradually become research hotspots. Compared with EKF, UKF does not need to linearize the model, and directly uses the nonlinear model, which avoids the error introduced by local linearization and avoids divergence in strong nonlinear systems. However, both EKF and UKF are based on Gaussian assumptions, so they are not applica...

AI Technical Summary

Problems solved by technology

However, since the calculation amount of UKF depends largely on the number of sampling points in the unscented transformation (UT), for high-dimensional systems, the calculation amount of UPF will become huge with the increase of sampling points

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