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Target tracking method based on maximum correntropy cubature particle filter

A technology of target tracking and particle filtering, which is applied in the direction of radio wave reflection/re-radiation, radio wave measurement system, and utilization of re-radiation to achieve the effect of avoiding linearization errors

Inactive Publication Date: 2018-11-13
HARBIN ENG UNIV
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Problems solved by technology

However, the maximum cross-correlation entropy filter MCUKF currently used in the field of target tracking is only suitable for Gaussian noise, and this method is no longer applicable for non-Gaussian noise

Method used

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  • Target tracking method based on maximum correntropy cubature particle filter
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  • Target tracking method based on maximum correntropy cubature particle filter

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Embodiment Construction

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0049] A kind of target tracking method based on MCCPF of the present invention, flow chart such as figure 1 shown, including the following steps:

[0050] Step 1: Establish the state equation and observation equation describing the target tracking system as follows:

[0051]

[0052] Among them, k-1 means the k-1th moment, k means the kth moment, x k is the state vector of the n-dimensional tracking target parameters at the kth moment, z k is the measurement vector of the m-dimensional tracking target parameters at the k+1th moment, f( ) and h( ) are known nonlinear functions, w k-1 is the n-dimensional system noise at the k-1th moment, v k is the m-dimensional measurement noise at the kth moment, assuming that the system noise obeys the Gaussian distribution w k-1 ~N(0,Q k-1 ), the measurement noise contains outliers and ob...

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Abstract

The invention provides a target tracking method based on maximum correntropy cubature particle filter (MCCPF). The target tracking method based on MCCPF is characterized by completing state estimationduring the target tracking process by using the MCCPF algorithm; during the target tracking process, reconstructing a state equation and a measurement equation of target tracking into a non-linear recursion model, and performing processing by means of the maximum correntropy criterion; and in a framework of standard Particle Filter (PF), using MCCKF (Maximum Correntropy Cubature Kalman Filter) togenerate an importance probability density function required by PF, and according to the algorithm process of PF, acquiring estimation of the tracking target state so as to realize real-time trackingof the target. The invention provides a target tracking method based on maximum correntropy cubature particle filter can obtain better performance than the current particle filter, improved particlefilter and robust filter in the target tracking process in which outliers appear in measurement of noise.

Description

technical field [0001] The invention relates to a target tracking method based on a maximum cross-correlation entropy volumetric particle filter, and belongs to the technical field of nonlinear robust filtering and target tracking. Background technique [0002] When the radar is used to observe the target, the measurement noise is polluted due to the error of the target tracking sensor and other reasons, and the observation outlier often occurs, so that the measurement noise no longer obeys the Gaussian distribution, which will lead to the traditional nonlinear filtering The accuracy of the algorithm decreases or even diverges, so it is necessary to use nonlinear robust filtering for measurement outliers. [0003] Aiming at the situation of outliers in the measurement noise, Gaussian Sum Filter (GSF), Huber Unscented Particle Filter (HRUPF) based on Huber technology and outlier robustness based on Student's t method have been proposed. Volumetric Kalman filter (Outlier Robu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/02G01S7/40G01S13/66
CPCG01S7/023G01S7/40G01S13/66
Inventor 张勇刚范颖王国庆汪晓雨李宁
Owner HARBIN ENG UNIV
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