Sample importance resample particle filter hardware implementation method based on FPGA

A particle filter and hardware implementation technology, which is applied in nonlinear system filtering and electronic fields, can solve the problem of less hardware implementation, and achieve the effect of speeding up the execution time and reducing the complexity of the algorithm
CN104009733AActive Publication Date: 2014-08-27HUAWEI TEHCHNOLOGIES CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUAWEI TEHCHNOLOGIES CO LTD
Publication Date
2014-08-27

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Abstract

The invention discloses a sample importance resample particle filter hardware implementation method based on an FPGA. The method includes the following steps that firstly, a particle generation module is used for receiving input vectors to generate particles and then outputting the particles to a particle renewing module and a resample module; secondly, the particle renewing module is used for conducting renewing, namely, weight calculation and weight normalization, on the particles generated in the first step and then outputting the particles to the resample module; thirdly, the resample module conducts resample and state renewing on the particles renewed in the second step or the particles generated in the first step and then feeding the particles back to the particle generation module in the first step; fourthly, an output generation module conducts data generation and output on the particles renewed in the second step or the particles generated in the first step.
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Description

technical field

[0001] The invention relates to a hardware implementation method of a particle filter algorithm based on FPGA, a module-level assembly line design method using a data flow structure, and belongs to the field of nonlinear system filtering and electronic technology. Background technique

[0002] Particle filter is a statistical filtering method based on Monte Carlo method and recursive Bayesian estimation, which is suitable for any nonlinear stochastic system with non-Gaussian background that can be represented by state space model and traditional Kalman filter.

[0003] However, particle filtering has problems such as particle degradation, loss of particle diversity, year-on-year increase in the number of particles and computational complexity. On the other hand, the particle filter algorithm is more complex and the amount of calculation is relatively large, which makes the real-time performance of the particle filter very poor and hinders its practical applic...

Claims

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