Improved Gaussian particle filter data fusion algorithm based on KLD sampling

A Gaussian particle filter and particle number technology, applied in the field of signal processing, to achieve good real-time performance, improved filtering speed, and easy combination
CN110401430AInactive Publication Date: 2019-11-01NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Publication Date
2019-11-01
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention provides an improved Gaussian particle filter algorithm (KLD-GPF) based on KLD, belongs to the technical field of signal processing, and relates to nonlinear filtering, and the method provided by the invention is suitable for state estimation of a nonlinear dynamic system. The algorithm can adaptively adjust the number of particles, and has a remarkable effect under the conditions that noise obeys Gaussian distribution and the statistical property of the noise is suddenly changed. According to the filtering algorithm, the Kullback-Leibler (KL) distance between a discrete probability density function (PDF) of particles and a real posterior probability density function is calculated online in the sampling process, and the size of a particle set is adjusted online according to the KL distance, so that the algorithm has relatively good robustness. The KLD-GPF can maintain a good estimation effect under the condition of sudden change of noise statistical characteristics. Compared with a KLD improved particle filtering algorithm (KLD-PF), although some filtering precision is lost, the filtering speed is greatly improved.
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Description

technical field

[0001] An improved Gaussian particle filter algorithm based on KLD proposed by the invention belongs to the technical field of signal processing and relates to nonlinear filtering. The method provided by the invention is suitable for state estimation of nonlinear dynamic systems. Background technique

[0002] Nonlinear filtering problems arise in many areas, including object tracking, strapdown inertial navigation systems, and attitude estimation. The Extended Kalman Filter EKF (Extend Kalman Filter) is to linearize the nonlinear function and directly truncate the high-order items, resulting in large errors and low filtering accuracy. Unscented Kalman filter UKF is a Kalman filter using unscented transformation. Compared with EKF, its filtering accuracy has been improved, but its nonlinear transfer error always exists. Since the extended Kalman filter and the unscented Kalman filter are both based on the improvement of the Kalman filter, and the Kalman filte...

Claims

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