Abstract
The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.
Highlights
The Global Positioning System (GPS)/inertial navigation system (INS) integrated system is combined with differential GPS in order to achieve higher accuracy [1]
For the sake of which is given byverify the performance of adaptive interacting multiple model (AIMM) filter, this paper execute the simulation test which is given by xm = k1 xm −1 − k 2 + λm1 1 if m ≤ t / 2
We investigate an alternative to the extended Kalman filter (EKF), interacting multiple model (IMM) filter, and AIMM filter data fusion technology with regard to the marine integrated navigation system, and INS/GPS integrated navigation system makes the most of the complete mathematical equations which contain the pseudo-range and Doppler measure value and INS provided measure value
Summary
The GPS/INS integrated system is combined with differential GPS in order to achieve higher accuracy [1] This is basically because the inherent characteristics of high-precision differential. Amongst numerous MM estimate approaches, the interacting multiple model (IMM) filter, which is one of the highly effective state estimation algorithms, can be applied to multi-sensor data fusion [18,19,20,21]. This approach is capable of estimating the state variables of a dynamic system with numerous behavior models as a probability switching approach.
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