Application of Cubature Kalman Filter for Bearingsonly Target Tracking

Background/Objectives: The objective of this paper is to develop a novel estimation algorithm based target tracking simulator for underwater target tracking applications. Methods/Statistical Analysis: An own ship observes corrupted sonar bearings from a radiating target and finds out Target Motion Parameters (TMP) - viz., range, course, bearing and speed of the target. The issue is inherently nonlinear as the bearing measurement is non-linearly related to the target state. CKF is a new nonlinear filter for state estimation. The modeling of target state and measurement vectors is carried out. CKF is integrated into the model to result in evolution of simulator. Extensive performance evaluation of CKF with respect to bearings-only target tracking problem in Monte-Carlo simulation is carried out and the results are presented. Findings: CKF depends on spherical-radial cubature rule that makes it potential to numerically figure variable moment integrals encountered within the nonlinear filter. The underwater passive target tracking following Cubature Kalman filter is explored in this paper. Application/Improvements: The results obtained are satisfactory and UKF can be used in futuristic submarines in Indian Navy owing to its advantages as envisaged in this paper.