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Fault detection method for Kalman filtering sensor information fusion

A Kalman filtering and fault detection technology, applied in radio wave measurement systems, instruments, measurement devices, etc., can solve a large number of calculation problems, and achieve the effect of simplifying complex calculations, improving fault detection speed, and reducing complexity.

Inactive Publication Date: 2013-07-24
HARBIN ENG UNIV
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Problems solved by technology

Therefore, considering that the multi-channel estimation procedure requires a lot of calculations, it is not a simple problem to implement and test the Kalman filter information fusion using the previous technology

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  • Fault detection method for Kalman filtering sensor information fusion
  • Fault detection method for Kalman filtering sensor information fusion
  • Fault detection method for Kalman filtering sensor information fusion

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Embodiment Construction

[0017] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0018] Such as figure 1 As shown, in the GPS / INS / DVL integrated navigation system (that is, the satellite / inertial navigation / Doppler log integrated navigation system), it is considered that the inertial navigation system does not have other failures except its inherent drift. When a fault occurs in any subsystem, it can effectively detect and filter the fault. When the fault cannot be eliminated, the faulty sensor will be isolated, and when the fault is eliminated, the system will be restored automatically. Such as figure 2 As shown, there are m sensor channels in the Kalman filter, and the information fusion of the sensors of the m channels must be carried out at the same time. The invention proposes a fault detection method for Kalman filter sensor information fusion. Statistics of the spectral norm mathematical expectation based on the introd...

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Abstract

The invention discloses a fault detection method for Kalman filtering sensor information fusion. The method includes the steps of: 1. according to a Kalman filtering theory, establishing a state equation and an observation equation of a linear dynamic system; 2. according to the observation equation obtained in step 1, making use of a least square process to acquire state estimation, a corresponding mean square error matrix and an innovation sequence; 3. utilizing the known innovation sequence to acquire different channel-normalized new innovation sequences, and composing an innovation matrix of an m channel parallel sensor; 4. acquiring the spectral norms and spectral norm mean value of the innovation matrix according to the innovation matrix obtained in step 3; and 5. carrying out fault detection on the Kalman filtering sensor information fusion. The method provided in the invention adopts mathematical statistics and interval estimation, simplifies complex calculation, and substantially improves the fault detection speed.

Description

technical field [0001] The invention relates to a Kalman filter sensor information fusion fault detection method based on a standardized innovation matrix, and belongs to the technical field of fault detection of Kalman filter information fusion. Background technique [0002] Intelligence, high precision, and high reliability are the requirements of future aircraft for navigation systems. Information fusion technology can meet these requirements for the navigation system of future aircraft. In the field of navigation and control, effective testing of Kalman filters is inevitable. Algorithms have been developed for this purpose and corresponding algorithmic techniques for testing Kalman filter tests, which not only ensure fault location and detection, but also estimate corrections. At present, there are many Kalman filter testing algorithms, and the application of philosophical algorithms can perform fault detection on different characteristic signs of Kalman filter. Despi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C25/00G01S19/20
Inventor 沈锋宋丽杰张桂贤陈潇李平敏刘海峰李强徐定杰宋金阳
Owner HARBIN ENG UNIV
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