System Identification for Cascaded Filter Modeling

Larry J. Levy

Abstract:	Design of cascaded Kalman filters require adequate knowledge of the first filter for: (1) proper modeling in the second(integrating) filter or, at least, (2) correct error analysis of both filters using suboptimal covariance(and mean) analysis. System identification can provide the technology for estimating the possibly unknown first filter model from simulated or real test data. The fidelity of the estimated model may vary depending on the model structure and observability of the model parameters from the test data. Low order generic models (eg. 1st order Markov processes) of the trajectory errors provide simple average error models for easy inclusion in the integration filter. However, specific situations such as time varying PDOP are missed. System identification can then be used to fit generic models to real world or simulation test data that accounts for the proper time correlation and PDOP. The resulting model provides enough fidelity for credible covariance analysis design studies. Simulation examples are used to illustrate the approach.
Published in:	Proceedings of the 53rd Annual Meeting of The Institute of Navigation (1997) June 30 - 2, 1997 Albuquerque, NM
Pages:	287 - 291
Cite this article:	Levy, Larry J., "System Identification for Cascaded Filter Modeling," Proceedings of the 53rd Annual Meeting of The Institute of Navigation (1997), Albuquerque, NM, June 1997, pp. 287-291.
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