Interacting multiple model nonlinear filter design for ultra-tightly coupled integrated navigation

In this paper, applications of the interacting multiple model (IMM) nonlinear filters to ultra-tightly coupled GPS/INS integrated navigation system. An ultra-tight GPS/INS architecture involves the integration of in-phase (I) and quadrature (Q) components from the correlator of a GPS receiver with the INS data. The error caused by linearization as in the extended Kalman filter (EKF) can be avoided using the unscented Kalman filter (UKF), which employs a set of sigma points by deterministic sampling, can efficiently deal with the nonlinear problem. The interacting multiple model (IMM) has the configuration that runs in parallel several model-matched state estimation filters, which exchange information (interact) at each sampling time. The use of IMM provides the appropriate value of process noise covariance so as to maintain good estimation accuracy and tracking capability. Performance assessment for EKF/UKF and IMMEKF/IMMUKF is carried out. The results show that the proposed IMMUKF algorithm demonstrates remarkable improvement in navigation estimation accuracy as compared to the conventional approaches.