Decomposition method of covariance matrix for command and control system

A technology of covariance matrix and command system, applied in the field of data processing, can solve the problems of filter collapse, loss of observation information, non-positive definite covariance matrix, etc., to achieve the effect of improving robustness

Pending Publication Date: 2022-04-19
BEIJING INST OF ELECTRONICS SYST ENG
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Problems solved by technology

[0004] The premise of the Cholesky decomposition of the covariance matrix is ​​that the matrix is ​​positive definite, but in the actual measurement process of the information source, due to the constraints of the measurement environment, target state, equipment cap

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  • Decomposition method of covariance matrix for command and control system
  • Decomposition method of covariance matrix for command and control system
  • Decomposition method of covariance matrix for command and control system

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[0048] For a more explicit description of the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar parts in the drawings are represented by the same reference numerals. Those skilled in the art will appreciate that the content specifically described below is illustrative rather than restrictive, and should not limit the scope of protection of the present invention.

[0049] In the allegation system, it is necessary to accurately correlate the track information generated by multiple information source observations, and fuse to generate a comprehensive track to produce a unified air intelligence posture, of which the Kalman filter method is a common fusion method. Linear and nonlinear filtering methods such as KF (Kalman filtering) and EKF (extended Kalman filtering) and UKF (traceless Kalman filtering) have been widely used in various fields of engineering practice as a powerful tool for rea...

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Abstract

The invention discloses a covariance matrix decomposition method for a command and control system. The method comprises the following steps: S10, acquiring acquired target track information; s20, fusing the target track information to obtain a covariance matrix matrixP of the target track information; s30, after the covariance matrix matrixP is preprocessed, whether an abnormal line exists in the covariance matrix matrixP or not is judged; and S40, the covariance matrix matrixP with the abnormal row is corrected, and matrix decomposition is carried out on the corrected covariance matrix matrixP. According to the decomposition method, decomposition can still be carried out under the non-positive definite condition of the matrix, the requirement for calculating the Sigma point set by the UKF filter is met, and the purpose of improving the robustness of the track filter of the command and control system is achieved.

Description

technical field [0001] The invention relates to the technical field of data processing. More specifically, it relates to a method for decomposing the covariance matrix of a command and control system. Background technique [0002] In the command and control system, it is necessary to accurately correlate the track information generated by observations of multiple information sources, and fuse them to generate a comprehensive track to generate a unified air intelligence situation. Among them, the Kalman filter method is a commonly used fusion method. KF (Kalman filter), EKF (extended Kalman filter), UKF (unscented Kalman filter) and other linear and nonlinear filtering methods, as a powerful tool for real-time state estimation, have been widely used in various fields of engineering practice. [0003] The core idea of ​​UKF is to approach the nonlinear function distribution through sampling strategy, use the calculated Sigma point set to approximate the real probability densi...

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Application Information

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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 刘鑫张耀东王中文刘明阳
Owner BEIJING INST OF ELECTRONICS SYST ENG
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