Bearings-only sensor trajectory scheduling using accumulative information

In single sensor bearings-only tracking scenarios, it is well understood that target observability is achieved by adequate maneuvers of the sensor. Sensor trajectory scheduling, which is naturally formulated as a partially observed Markov decision process, is therefore playing a crucial role in the advanced tracking process. In this paper, the accumulative information is used as a criterion for the sensor trajectory scheduling problem. A statistical model, which incorporates the uncertainty of target state into the Fisher information matrix, is derived. The latter and thus the accumulative information are then calculated recursively at each of possible sensor locations. Simulation results are presented to demonstrate the effectiveness of this method and the performance of the proposed sensor scheduling method is compared with that of the accumulative expected reward method developed previously.