Gaussian particle filter hardware implementation method based on FPGA

Abstract

The invention discloses a Gaussian particle filter hardware implementation method based on a FPGA. The method comprises the following steps: decomposing according to Cholesky by using a conditioning particle generating module CPG to obtain a covariance matrix S and a mean value mega, and drawing conditioning particles; generating a particle swarm by using a particle generating module PG; computing a weight value of the particle swarm by using a particle update module PU; updating the mean value and the covariance of the particle swarm with updated weight value by using a central processing unit module CU; acquiring the updated particle swarm by using a re-sampling module RS; computing the mean value and the covariance of the sampled particle swarm by using a covariance computing module CC; decomposing the mean value and the covariance by using the Cholesky to obtain the covariance matrix S and the mean value mega. The design flow is greatly simplified by using the module-level assembly line design. The synchronous execution of the module-level assembly line is realized through a distributed controller, and the controller is used for controlling the data generation and transmission of each processing module.

Description

technical field [0001] The invention relates to a hardware implementation method of a Gaussian particle filter algorithm based on FPGA, a block-level pipeline design method using a data flow structure, and belongs to the field of nonlinear system filtering and electronic technology. Background technique [0002] The particle filter algorithm developed in recent years is a nonlinear non-Gaussian system filtering and prediction method based on the Monte Carlo idea. It uses the random sampling point set of the posterior probability density function and the corresponding weight to represent the change of the state vector , thus completely breaking through the theoretical framework of Kalman filtering, and there is no restriction on the form of process noise and measurement noise of the system. [0003] But compared with the Kalman filter, the particle filter algorithm is more complex, and the amount of calculation is relatively large, which makes the real-time performance of the...

AI Technical Summary

Problems solved by technology

Overall, the current research on particle filter hardware implementation methods is not deep enough. Therefore, it is of great theoretical and practical significance to study the hardware implementation methods of particle filter algorithms.

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