Application of Square-Root Unscented Kalman Filter Smoothing Algorithm in Tracking Underwater Target

Abstract

Abstract. In passive tracking, the nonlinearity may cause computational complication and precision degradation. To solve this problem, a novel filtering-smoothing algorithm based on Square-Root Unscented Kalman Filter (SR-UKFS) is proposed to track underwater target. In the SR-UKFS algorithm, the Square-Root Unscented Kalman Filter (SR-UKF) is used as forward-filtering algorithm to provide current location results, and the Rauch-Tung-Striebel (RTS) algorithm smoothes the previous state vector and covariance matrix using the current location results. Comparative analysis and validation are made on the tracking performances of SR-UKFS algorithm and SR-UKF algorithm, and the simulation results show that, under the same conditions, the SR-UKFS can more effectively improve the tracking precision than the SR-UKF algorithm. The SR-UKFS algorithm can reduce nearly 59% of the position error and nearly 54% of the velocity error.