Single Observer Passive Location Algorithm Based on Marginalized Iterated Cubature Kalman Filter

Because of the low observability and the high noise in single observer passive location, the performance of the positioning accuracy and convergence velocity was poor. A novel marginalized iterated cubature kalman filter was presented. The iteration strategy based on the likelihood increase was adopted. The global convergence of the algorithm was ensured, without the difficult choice of the judgment threshold. Meanwhile, the cross-covariance between the state and the measurement noise was taken into account. Then the state vector was augmented by the measurement noise. Based on the conditionally linear model, the marginalized filtering was proceeded. The positioning accuracy and the convergence velocity was improved. And the computational burden was reduced, because the less sigma points were needed in spite of the augmented state vector. Simulation results indicate that the novel algorithm improved the performance of the positioning accuracy and convergence velocity in single observer passive location.