Kalman filtering method based on amplitude information

A Kalman filter and amplitude information technology, applied in the field of radar, can solve problems such as effective estimation of difficult-to-observe noise statistical information, affecting state estimation tracking accuracy, and observation noise statistical information mismatch, etc., to improve efficiency and quality, and facilitate engineering implementation. , to achieve the effect of correct estimation

Active Publication Date: 2019-05-24
XIDIAN UNIV
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Problems solved by technology

However, the current Kalman filtering methods do not involve the impact of the target RCS fluctuation on the statistical information of the observation noise, that is, they are not effectively combined with the actual situation of signal processing, and it is difficult to effectively estimate the statistical information of the observation noise, resulting in the observation noise and its statistical information. The case of mismatch affects state estimation and tracking accuracy

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  • Kalman filtering method based on amplitude information

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[0024] The present invention is described in detail below in conjunction with accompanying drawing:

[0025] The Kalman filtering method based on amplitude information of the present invention is applicable to the scene where the target motion state is represented by a linear equation in a discrete time system, and the target motion state equation in this scene is:

[0026] X(k+1)=F(k)X(k)+G(k)u(k)+V(k), k=1, 2,...,

[0027] Among them, k is the scan period, F(k) is the state transition matrix, X(k) is the state of the discrete dynamic system in the kth scan period; G(k) is the input control matrix; u(k) is the known input Or the control signal; V(k) is the process noise, and its covariance is Q(k).

[0028] In this scenario, according to the target motion state equation X(k+1), the measurement information Z(k+1) received by the filter is expressed as:

[0029] Z(k+1)=H(k+1) X(k+1)+W(k+1)

[0030] Among them, H(k+1) is the measurement matrix, W(k+1) is the observation noise...

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Abstract

The invention discloses a Kalman filtering method based on amplitude information, which mainly solves the problem that the target state estimation precision is insufficient in the prior art when the change of noise is observed, and the implementation scheme of the Kalman filtering method comprises the following steps: 1) acquiring measurement information, amplitude information and state noise covariance obtained by processing radar signals in different scanning periods; 2) obtaining an initial state error covariance and an observation noise covariance through radar system parameters; 3) setting the parameters obtained in the step 1) and the step 2) as initial values of the filter; 4) calculating the covariance proportionality coefficient of the observation noise by using the obtained amplitude information, and calculating the covariance of the observation noise; According to the radar target tracking and parameter estimation method based on the covariance of the observation noise, themismatching condition of the observation noise and the statistical information of the observation noise is reduced, the accuracy of target state estimation is improved, and the radar target tracking and parameter estimation method can be used for tracking and parameter estimation of a radar target.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a Kalman filtering method, which can be used for tracking and parameter estimation of radar targets. Background technique [0002] The Kalman filter algorithm is a radar target tracking algorithm widely used at present. When the system characteristics are known, and the statistical characteristics of the system noise and observation noise are known a priori, the Kalman filter algorithm can realize the optimal estimation. However, in general, the state of the system is not known a priori, and the statistical characteristics of the state noise and observation noise are not fixed, especially the statistical information of the observation noise will change with the fluctuation of the target RCS. In this case, it is difficult to achieve optimality in radar target tracking using the traditional Kalman filter algorithm, and problems such as filter divergence, decreased tracki...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
Inventor 赵永波丁一何学辉刘宏伟苏洪涛
Owner XIDIAN UNIV
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