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Kalman Filtering Method Based on Amplitude Information

A technology of Kalman filtering and amplitude information, applied in the field of radar, can solve problems such as the mismatch of observation noise statistical information, affecting the tracking accuracy of state estimation, and effective estimation of hard-to-observe noise statistical information, so as to reduce the amount of calculation, improve efficiency and The effect of quality and ease of engineering realization

Active Publication Date: 2022-03-04
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
  • Kalman Filtering Method Based on Amplitude Information
  • Kalman Filtering Method Based on Amplitude Information

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Embodiment Construction

[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 of insufficient estimation accuracy of the target state in the prior art when the observation noise changes. The realization scheme is: 1) Obtain radar signals in different scanning periods Process the obtained measurement information, amplitude information, and state noise covariance; 2) Obtain the initial state error covariance and observation noise covariance from the radar system parameters; 3) Set the parameters obtained in 1) and 2) as the initial state of the filter 4) Use the obtained amplitude information to calculate the covariance scale coefficient of the observation noise, and calculate the observation noise covariance; 5) Perform state estimation according to the obtained observation noise covariance; 6) Input the information obtained from the state estimation to the next step scan cycle. The invention reduces the mismatch between observation noise and statistical information of observation noise, improves the accuracy of target state estimation, and can be used for tracking and parameter estimation of radar targets.

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...

Claims

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Application Information

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Patent Type & Authority Patents(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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