Method of adaptive iterative cubature Kalman filter with forgetting factor in target tracking

An adaptive iterative and Kalman filter technology, applied in the field of target tracking, can solve the problems of large amount of calculation and inconvenient practical application

Inactive Publication Date: 2018-12-18
ZHEJIANG GONGSHANG UNIVERSITY
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AI Technical Summary

Problems solved by technology

In the 1940s, Wiener proposed the Wiener filter, which laid the foundation of the optimal filter theory. It needs to process the current and previous observations, which requires a large amount of calculation, and can only deal with stationary processes, which is not convenient for practical application.
In practice, the application of the standard Kalman filter algorithm has limitations. It is only suitable for linear Gaussian systems. When the state equation and measurement equation are linear, and the process noise and measurement noise are both Gaussian white with zero mean When there is noise, the Kalman filter algorithm is the optimal estimation algorithm

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  • Method of adaptive iterative cubature Kalman filter with forgetting factor in target tracking
  • Method of adaptive iterative cubature Kalman filter with forgetting factor in target tracking
  • Method of adaptive iterative cubature Kalman filter with forgetting factor in target tracking

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Embodiment Construction

[0061] The present invention will be further described below in conjunction with the drawings.

[0062] The present invention provides an adaptive iterative volume Kalman filter method with forgetting factor in target tracking, such as figure 1 As shown in the basic principle of target tracking, the target tracking process can be defined as the process of estimating the state of the target at any time (filtering) and in the future (prediction). The present invention provides an adaptive iterative volume Kalman filter method with forgetting factor for target tracking, which includes the following steps, combined with figure 2 Explain it:

[0063] Step 1: Establish a model for the target system, which includes two equations, the state equation and the observation equation, as shown below:

[0064]

[0065] In the formula, f(·)- the nonlinear state function of the system;

[0066] h(·)- the nonlinear measurement function of the system;

[0067] x k -System n-dimensional state vector;

[...

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Abstract

The invention discloses a method of an adaptive iterative cubature Kalman filter with a forgetting factor in target tracking. The method analyzes a basic form-filter of target state estimation in target tracking, and innovatively proposes a method of combining the forgetting factor with the adaptive iterative cubature Kalman filter, which not only enables the forgetting factor to play a role and reduces the influence of historical data on the result of the filter, but also improves the accuracy of the filter algorithm itself and the ability to deal with nonlinear problems, and finally achievesaccurate estimation of the target in target tracking.

Description

Technical field [0001] The invention belongs to the field of target tracking, and specifically relates to an adaptive iterative volume Kalman filter method with a forgetting factor in target tracking. Background technique [0002] Since the emergence of the first tracking radar station SCR-28, target tracking technology has gradually become one of the hot research fields in military and civilian use. Wax first proposed the basic concept of target tracking, and then the target tracking research began to be formally established in theory. In the 1970s, Kalman proposed the Kalman filter algorithm, and later the Kalman filter algorithm began to involve the field of target tracking, and the target tracking technology has been further developed. Target tracking is actually the problem of tracking and filtering the target state. The target state is estimated through the target measurement value obtained by the sensor. Target tracking technology has always been a basic research topic i...

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Application Information

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IPC IPC(8): G01S13/66G01S7/02
CPCG01S13/66G01S7/02
Inventor戴文战黄晓姣沈忱
OwnerZHEJIANG GONGSHANG UNIVERSITY