A kind of fpga hardware implementation method, device and target tracking method of particle filter based on Bayesian resampling

Abstract

The invention discloses an FPGA hardware implementation method, device and target tracking method of particle filtering based on Bayesian resampling. The FPGA implementation method is as follows: the particle sampling unit reads old particles from the particle cache block, Receive random numbers, and sample and update the read old particles in parallel; the weight update unit reads the observed values, performs weight calculations on the updated particles in parallel, and stores the generated weights in the weight cache block; the Bayesian resampling unit uses Bayesian resampling method, resampling is performed in parallel according to all weight values ​​in the weight cache block, and the index output value is stored back to the corresponding index cache block; the pseudo-random permutation generator reads the address of new particles from the index cache block , randomly assign new particles to each particle cache block to realize the exchange of particles in parallel computing; execute the above steps in a loop until all time steps are iteratively completed to complete the state estimation of the system. The invention can improve the calculation speed of the particle filter system.

Description

technical field [0001] The invention belongs to the field of nonlinear filtering of electronic technology, in particular to an FPGA hardware implementation method, device and target tracking method of particle filtering based on Bayesian resampling. Background technique [0002] Particle Filter (PF: Particle Filter), also known as Sequential Monte Carlo method (SMC: Sequential MonteCarlo), is a non-linear filtering method based on Bayesian sampling estimation of sequence importance sampling, which uses the randomness of the posterior probability density function The set of sampling points and the corresponding weight values ​​represent the changes of the state vector. Thus breaking through the theoretical framework of the Kalman filter and the limitations of the Gaussian system, it can be applied to any form of state-space model (State-SpaceModel). Particle filter algorithm can solve almost any nonlinear filtering problem, and is widely used in economic statistics, modern s...

AI Technical Summary

Problems solved by technology

Multi-core CPUs have low parallelism and are not suitable for highly intensive computing tasks such as particle filtering; existing GPU-based particle filter accelerators spend most of their time on resampling steps, and because particle filtering needs to generate a large number of random numbers, and There are a large number of branch structures, which are not suitable for GPU computing, making the speedup ratio very limited; FPGA can have pipeline parallelism and data parallelism at the same time, and can design highly customized hardware architecture, so it is very suitable for particle filter algorithm acceleration

However, the existing FPGA-based particle filter accelerators mostly focus on the traditional resampling algorithm and simplification of the algorithm, which will affect the particle filter effect and are not suitable for application scenarios with a large number of particles.

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