Adaptive Cubature Kalman filter for Bearing only Tracking with non-additive sensor noise

This paper presents a nonlinear estimation algorithm entitled adaptive Cubature Kalman filter for Bearing only Tracking problem in presence of non-additive sensor noise. The proposed nonlinear sigma point filter also incorporates adaptation algorithm with which it can take care of the critical situations where the covariance of non-additive sensor noise remains unknown. The adaptation algorithm is designed for automatic tuning of the unknown sensor noise covariance to ensure satisfactory estimation performance of the filter. From Monte Carlo simulation superiority of the proposed filter has been demonstrated over its non-adaptive counterpart for the Bearing only Tracking (BOT) problem and indicates its suitability for on board implementation.