Underwater Bearing Only Tracking Using Optimal Observer Maneuver Strategies

This paper considers the problem of tracking a uniform moving source using noisy bearing measurements obtained from a distant observer. Observer trajectory optimization plays a central role in this problem, with the objective to minimize the estimation error of the target state. The Bearing Only Tracking (BOT) of passive targets is mainly focused on the observer maneuver with known trajectories and rarely focused on the future prediction of observer states using adaptive optimization strategies. In this paper, observer paths using one-step ahead optimization based on a performance index are devised which are potentially useful for longer horizon observer trajectory planning in passive tracking. This performance index is the function of source parameters termed as the determinant of Error Covariance Matrix (ECM) which is numerically more efficient than the determinant of Fisher Information Matrix (FIM). The determinant of the FIM requires the calculation of future values for target states and measurements rather than the current values, which is not feasible for Kalman like filters. Therefore, in this paper, the optimization technique is implemented using the state error covariance which is readily available through Kalman filter equations and does require separate numerical calculations. Due to optimal observer maneuver, the performance of the proposed algorithm does not depend on the initial conditions as compared to the conventional tracking methods. The efficiency of the evolutionary algorithm is calculated in terms of range, position and velocity errors and simulation results show 4% fewer estimation errors for ECM based optimization than the determinant of the FIM method.

Automated summary

This paper discusses the problem of tracking a moving source using noisy bearing measurements from a distant observer, focusing on optimizing observer trajectories to minimize target state estimation errors. A new optimization method for passive tracking is proposed, which proves to be more efficient compared to traditional approaches.