Improved Sage-Husa adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment

Abstract

The invention relates to an improved Sage-Husa adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment. The method comprises the following steps of 1, acquiring sensor measurement information and GPS latitude and longitude information; 2, establishing a GPS / INS integrated navigation system model, and establishing a state equation and a measurement equation; 3, an information anomaly detection process, constructing test statistics according to a prediction residual vector, judging whether abnormal observation exists or not, and when abnormal measurement detection exists in a system, adopting improved Sage-Husa adaptive filtering, carrying out Kalman filtering gain zero setting, and introducing an exponential attenuation adaptive factor to adjust observation measurement noise; and 4, filtering the integrated navigation system by using an improved Sage-Husa adaptive filtering method, and predicting and correcting the Q array and the R array in real time on the basis of standard Kalman filtering. The multi-source information fusion equipment comprises a sensor, a processor, an information acquisition unit and a data transmission and receiving unit. The method is advantaged in that an information anomaly detection process is added, and navigation precision and fault tolerance of the system are improved.

Description

technical field [0001] The invention belongs to the technical field of navigation and positioning, and relates to an improved Sage-Husa adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment. Background technique [0002] Multi-source information fusion is a technology that summarizes and integrates the collected data output by each sensor, and uses certain rules to combine the best results. In the field of navigation and positioning of intelligent transportation systems, the most classic and efficient method for multi-source information fusion is Kalman Filtering (KF). However, this algorithm also has some limitations. During the calculation process of the Kalman filter, systematic errors will continue to accumulate, which will affect the positive definiteness of the error covariance matrix, resulting in inaccurate filtering estimation results, so it needs to be improved. Contents of the invention [0003] Th...

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Problems solved by technology

However, this algorithm also has some limitations. During the calculation of the Kalman filter, systematic errors will continue to accumulate, which will affect the positive definiteness of the error covariance matrix, resulting in inaccurate filtering estimation results, so it needs to be improved.

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