Underwater target tracking based on strong tracking sparse grid quadrature filter

作者：Lv, Chongyang; Yu, Fei; Xiao, Shu; Dong, Qianhui

来源：Engineering Letters, 2018, 26(1): 92-99.

• Computational costs
• Gauss-Hermite quadrature
• Quadrature filters
• Sparse grid
• Strong tracking filter
• Underwater moving object
• Underwater target tracking
• Unscented Kalman Filter

Underwater moving object detection/tracking is critical in various applications such as exploration of natural undersea resources, acquiring of accurate scientific data to maintain regular surveillance of missions, navigation and tactical surveillance. In currently, underwater moving target is usually tracked using the traditional non-linear estimators such as Extended Kalman Filter (EKF) and unscented Kalman Filter (UKF). However, if an underwater target moves with delicate maneuver, the accuracy of the filter may decline, even diverge. In this paper, a (STSGQF) is proposed to deal with the problem. The STSGQF is obtained by introducing the Strong Tracking Filter (STF) to the Sparse Grid Quadrature Filter (SGQF). Compared with the Gauss-Hermite Quadrature Filter (GHQF), the sparse grid method is available to reduce the SGQF's computational cost significantly, with slight sacrifice of accuracy its accuracy declines slightly. Meanwhile, the STSGQF has stronger robustness than SGQF against the state change. The effectiveness of STSGQF is demonstrated by the simulation results more robust, better robustness.

• 出版日期2018
• 单位哈尔滨工程大学