A marine INS/GP S adaptive navigation system is presented. GPS with two antenna providing vessel'

s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system.

T he Kaiman filter is the most frequently used algorithm in the integrated navigation system, which is capable of

estimating INS errors online based on the measured errors between INS and GPS. The standard Kaiman filter

( SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not

change, the Kaiman filter will give the optimal estimation. However GPS receiver will be disturbed easily and

thus temporally changing measurement noise will join into the outputs of GPS , which will lead to performance

degradation of the Kaiman filter. Many researchers introduce fuzzy logic control method into innovation-based a

daptive estimation adaptive Kaiman filtering (IAE-AKF ) algorithm, and accordingly propose various adaptive

Kaiman filters. However how to design the fuzzy logic controller is a very complicated problem still without a

convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion

for the proper computation of the filter innovation covariance and hence of the filter gain. Th e approach is di

rect and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE

A K F algorithm theoretically in detail, the approach is tested by the simulation based on the system error mod-

el of the developed INS/GP S integrated marine navigation system. Simulation results show that the adaptive

Kaiman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra

ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the

Kaiman filter.

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