Optimal design of the Own Ship maneuver in the bearing-only target motion analysis problem using a heuristically supervised Extended Kalman Filter

The state estimation algorithm and the Own Ship maneuvering specifications have great effects on the accuracy of the bearing-only tracking (BOT) method. In the BOT problem, the EKF algorithm is widely used as the nonlinear state estimation algorithm while it suffers from its sensitivity to the initial values of covariance matrixes. This paper aims at improving the accuracy of the BOT problem by using the metaheuristic evolutionary optimization algorithms as supervising algorithms. Three different optimization algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Cuckoo Search (CS) are used in finding the optimal initial values of the dynamic and the measurement process noise covariance matrixes of the Extended Kalman Filter (EKF). Moreover, the optimal path (leg) planning of the Own Ship maneuver is also done by minimizing the Fisher Information Matrix (FIM). The Monte Carlo analysis of the simulation results demonstrates the effectiveness of the evolutionary algorithms in improving the performance of the EKF in a BOT problem.