• ETRI Journal
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• v.35 no.5
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• pp.943-946
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• 2013

Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF-based navigation algorithm in a low-grade or mid-grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.87-94
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• 2013

UKF (Unscented Kalman Filter) has been used in the nonlinear systems without initial accurate state estimates instead of EKF (Extended Kalman Filter) of the last decade because the UKF has robustness to the large initial estimation error. In the multirate integrated system such as INS (Inertial Navigation System)/GPS (Global Positioning System) integrated navigation system, however, it is difficult to implement the UKF based navigation algorithm in the mid-grade micro-processor due to the large computational burden. To overcome this problem, this paper proposes a MUKF (Modified UKF) that has a reduced computation burden using the basic idea that the change of the provability distribution for the state variables between measurement updates is small in the multi-rate INS/GPS integrated navigation filter. The performance of the proposed MUKF is verified by numerical simulations.