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Kalman filtering is widely used in target tracking. However, conventional Kalman filtering may fail to track the target when there is acceleration, deceleration, and turn. In this paper, these maneuvers are characterized by two orthogonal components of the changed velocity in l 1 -norm. The adaptive factors to adjust the Kalman gain are then generated through a mapping function based on the characterization. Sea experimental results show that the proposed adaptive Kalman filtering is better at tracking the maneuvering target

Robust weak signal detection is quite a difficult problem in the presence of interferences and noise. Generally, adaptive beamforming is an efficient way of interference suppression. However, conventional adaptive beamforming, e.g. MVDR may degrade significantly due to mismatch in the practical applications. In this paper, a robust adaptive interference suppression method is presented and then the worst-case performance optimization is implemented on the eigenanalysis-based re-constructed covariance matrix. Experimental results show the proposed method can efficiently suppress the interferences and then be for robust adaptive beamforming and weak signal detection.