SINS/DVL Integrated Navigation Method with Current Compensation Using RBF Neural Network

In strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation, the DVL is utilized to restrain the error accumulation of SINS. When operating in mid-water zone, the DVL may lose bottom track due to the limited sensor range. In this case, its measurements will be affected by sea currents. To overcome this problem, this paper proposes a novel current compensation method for SINS/DVL integrated navigation. Based on a new technical route, the proposed method removes the constraints of the existing methods. It is developed as follows. By applying the DVL measurements in the acoustic Doppler current profiler mode, a current measurement module is established to operate in parallel with the SINS/DVL integrated navigation system. On this basis, a radial basis function (RBF) neural network is designed for current compensation. Specifically, when the DVL operates normally, it can simultaneously output the earth-relative and water-relative velocities. The earth-relative velocity is applied for integrated navigation. Meanwhile, it is applied to train the RBF neural network along with the water-relative velocity. Therefore, the RBF neural network approximates the function between the earth-relative and water-relative velocities. When the DVL loses bottom track, the well-trained RBF neural network can be utilized as a velocity predictor. By inputting the water-relative velocity from DVL, it can predict the earth-relative velocity for integrated navigation. In this way, the current compensation is achieved. The effectiveness of the proposed method is verified by realistic semi-physical experiments.