Sequential centralized fusion of multiple passive acoustic sensors with unknown propagation delays

Abstract

In this paper, we address the problem of target tracking using multiple acoustic sensors to observe the target state with unknown propagation delays. This problem occurs because the measurements received by multiple acoustic sensors located at different positions are from different unknown emission times of the acoustic signal even if the sensors receive measurements simultaneously; thus, they cannot be stacked up directly for centralized fusion as usual. However, the target states at different unknown emission times can be aligned to a common measurement received time by the retrodiction of state prediction. On this basis, herein we propose the centralized fusion of multiple acoustic sensors via sequential processing, namely, sequential centralized fusion (SCF). First, the measurement received time is chosen as the target state time, and the target state is predicted to this time for tracking. Second, state prediction is retrodicted to the signal emission times by solving augmented implicit nonlinear equations through Wegstein's method. Third, the state prediction is updated with acoustic measurements sequentially at measurement received time. Compared with the existing distributed fusion methods, our proposed SCF method has smaller computational complexity and better tracking performance. Illustrative examples demonstrate that SCF outperforms covariance intersection and the largest ellipsoid approximation.