A self-adaptive unscented Kalman filtering for underwater gravity aided navigation

In this paper, a self-adaptive unscented Kalman filtering for underwater gravity aided navigation is constructed. It is more accurate and far easier to implement than an extended Kalman filter. Then the novel navigation algorithm based on the self-adaptive unscented Kalman filter is explored. With this method submerged position fixes for autonomous underwater vehicle can be obtained from comparing gravity fields' measurements with gravity maps. Specifically, simulation results show that navigation errors can be reduced more effectively and efficiently by the presented algorithm.