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Adaptive Kalman Filtering for GPS/INS Integration

Adaptive Kalman Filtering for GPS/INS Integration. Weidong Ding This research is supported by the Australian Cooperative Research Centre for Spatial Information (CRC-SI) under project 1.3 ‘Integrated positioning and geo-referencing platform’. GPS/INS integration.

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Adaptive Kalman Filtering for GPS/INS Integration

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  1. Adaptive Kalman Filtering for GPS/INS Integration Weidong Ding This research is supported by the Australian Cooperative Research Centre for Spatial Information (CRC-SI) under project 1.3 ‘Integrated positioning and geo-referencing platform’. School of Surveying & Spatial Information Systems The University of New South Wales, Australia

  2. GPS/INS integration • Surveying, navigation, location based services, etc. • Solution of position & attitude • Long term accuracy, high update rate, robustness, INS calibration presented by Weidong Ding

  3. Limitations of Kalman Filter • Wrong parameters of system models and noise properties may result in the filter being suboptimal or even cause it to diverge. presented by Weidong Ding

  4. Adaptive Kalman Filter • Covariance scaling method • By applying a scale factor to the predicted state covariance matrix to deliberately decrease the weight of the state prediction, to improve KF stableness. • Multi-model adaptive estimation • A group of KF filters; each has slightly different configurations. • The output is the optimal combination of the outputs from individual filters. • Adaptive stochastic modelling (Innovation based, Residual based) • Uncertain stochastic modelling parameters are estimated on-line using the covariance information of the KF innovation and residual series. • A new process noise scaling method is proposed. presented by Weidong Ding

  5. Results of on-line stochastic modeling presented by Weidong Ding

  6. Results using process noise scaling presented by Weidong Ding

  7. Summary • The online stochastic modelling method can estimate the individual elements of noise covariance matrix. However, it is vulnerable to the innovation and residual covariance estimation biases, and is not scalable to a large number of parameters. • The covariance scaling method is more robust and suitable for practical implementations. The proposed covariance based adaptive process noise scaling method has demonstrated significant improvements on the filtering performance in the test. • Optimal allocation of noise to each individual source is not possible using scaling factor methods, which is a topic for further investigation. presented by Weidong Ding

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