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Performance comparison among some nonlinear filters for a low cost SINS/GPS integrated solution

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Abstract

The Kalman filter is a familiar minimum mean square estimator for linear systems. In practice, the filter is frequently employed for nonlinear problems. This paper investigates into the application of the Kalman filter’s nonlinear variants, namely the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the second order central difference filter (CDF2). A low cost strapdown inertial navigation system (SINS) integrated with the global position system (GPS) is the performance evaluation platform for the three nonlinear data synthesis techniques. Here, the discrete-time nonlinear error equations for the SINS are implemented. Test results of a field experiment are presented and performance comparison is made for the aforesaid nonlinear estimation techniques.

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Authors and Affiliations

  1. Center for Control and Instrumentation, National Engineering and Scientific Commission, Islamabad, Pakistan

    Jamshaid Ali & M. Rasheeq Ullah Baig Mirza

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  1. Jamshaid Ali
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  2. M. Rasheeq Ullah Baig Mirza
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Correspondence to Jamshaid Ali.

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Ali, J., Ullah Baig Mirza, M.R. Performance comparison among some nonlinear filters for a low cost SINS/GPS integrated solution. Nonlinear Dyn 61, 491–502 (2010). https://doi.org/10.1007/s11071-010-9665-y

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  • DOI: https://doi.org/10.1007/s11071-010-9665-y

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