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The Application of Improved Particle Filter Algorithm in the Interactive of Multiple Model

Author: WangXiaoQing
Tutor: ShiJianFang
School: Taiyuan University of Technology
Course: Circuits and Systems
Keywords: Target tracking Particle filter algorithm Interacting Multiple Model Algorithm Kalman filter
CLC: TN713
Type: Master's thesis
Year: 2011
Downloads: 164
Quote: 0
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Abstract


Kalman filter algorithm is one of the most commonly used in target tracking filtering algorithm, it is optimal estimation of linear Gaussian noise filtering algorithm, but this algorithm in nonlinear non-Gaussian noise filtering effect is not ideal, even produce divergent phenomenon. Improvements in the Kalman filter algorithm based on the extended Kalman filter algorithm. To better nonlinear environment maneuvering target tracking, and in recent years has generated many new filtering algorithm for nonlinear environment, a particle filtering algorithm in these emerging filtering algorithm. Standard particle filter algorithm, there are a lot of shortcomings, such as divergent phenomenon, the particle number lack of particle filter algorithm improvements, improved filtering algorithm, such as the extended Kalman filter algorithm Unscented Kalman filter algorithm, since to adapt the number of particles particle filter algorithm Malta Kraft (MCMC) particle filter algorithm interacting multiple model particle filter algorithm. These filtering algorithms are often used in its application environment, used in other environments will appear larger filtering error. CA / CV model frequently used to model the target tracking algorithms, statistical models and interactive multiple model algorithm filtering algorithm used in the Kalman filter algorithm, the extended Kalman filter algorithm and particle filter algorithm. Sampling the number of particles in a particle filter algorithm, the filtering effect of the process noise and measurement noise would particle filter algorithm have a huge impact, this paper will study for the same model, the most suitable sampling particle number, and the research process noise as well as the impact of the measurement noise filtering effect of the particle filter algorithm. With the non-linear filtering algorithm, compared to the particle filter algorithm extended Kalman algorithm has a great advantage, the the algorithm complexity without integral the Fang Chengwei number of impact, the complexity of the algorithm and sampling of the number of particles only. The filtering effect of the particle filter is related to the number of particles and sampling, when sampling when the number of particles, the filtering effect of the particle filter may be still better than the extended Kalman filter. This thesis is going to take a different number of particles contrast filtering effect of the particle filter algorithm with the extended Kalman filter algorithm. Mentioned a number of resampling to set up a sampling threshold, resample only when the number of particles is less than this value, in theory, this would reduce the resampling particle filter literature. But this must be to calculate the sampling threshold, so that it will increase the complexity of the algorithm to extend the completion time of the algorithm, resampling addition, because the number of valid samples is higher than the sampling threshold, can also cause degradation of the particles to a certain extent, This will also affect the final filtering effect. Improved particle filtering algorithm compares extended Kalman particle filter, regular particle filter, linear filter optimized combination of particle filter effect, and the best improved particle filter algorithm is applied to the interacting multiple model algorithm, research This algorithm of the filtering effect.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Basic electronic circuits > Filtering techniques,the filter
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