Robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

A robust direction-of-arrival (DOA) tracking method with uncertain measurement noise for underwater multiple acoustic targets is proposed based on the cardinalized probability hypothesis density (CPHD) filter and the Sage-Husa algorithm. Firstly, the varying of the DOA is modeled as Singer model. The DOA estimates provided by traditional DOA estimation methods and the estimation error are treated as measurement and measurement noise, respectively, to establish the measurement model. Based on this, the multi-target DOA tracking is performed based on the CPHD filter. In this step, the DOAs are predicted by using the tracking results at the previous time step and the varying model of the DOA, the predicted values are then modified through the measurement and measurement model to obtain the DOA tracking results. Finally, the modified Sage-Husa algorithm is used to estimate the variance of uncertain measurement noise adaptively in real time of tracking since the measurement noise variance is an important parameter to determine the tracking performance. In this manner, a robust underwater multi-target DOA tracking method is proposed. The experimental result shows that the proposed algorithm reduces the average optimal sub-patten assignment (OSPA) error of the measurements of DOAs from more than 10° to 2°, and the measurements of DOAs are significantly improved. The proposed robust underwater multi-target DOA tracking method improves the tracking performance in the scenario of uncertain measurement noise.