Information and Navigation College, Air Force Engineering University, Xi'an 710077, China.
Air Force Harbin Flight Academy, Harbin 150006, China.
Sensors (Basel). 2018 Jun 17;18(6):1959. doi: 10.3390/s18061959.
Bearings-only tracking only adopts measurements from angle sensors to realize target tracking, thus, the accuracy of the state prediction has a significant influence on the final results of filtering. There exist unpredictable approximation errors in the process of filtering due to state propagation, discretization, linearization or other adverse effects. The idea of online covariance adaption is proposed in this work, where the post covariance information is proved to be effective for the covariance adaption. With theoretical deduction, the relationship between the posterior covariance and the priori covariance is investigated; the priori covariance is modified online based on the feedback rule of covariance updating. The general framework integrates the continuous-discrete cubature Kalman filtering and the feedback rule of covariance updating. Numerical results illustrated that the proposed method has advantages over decreasing unpredictable errors and improving the computational accuracy and efficiency.
纯方位跟踪仅采用角度传感器的测量值来实现目标跟踪，因此，状态预测的准确性对滤波的最终结果有显著影响。由于状态传播、离散化、线性化或其他不利影响，滤波过程中存在不可预测的近似误差。本文提出了在线协方差自适应的思想，证明了后验协方差在协方差自适应中的有效性。通过理论推导，研究了后验协方差与先验协方差之间的关系；基于协方差更新的反馈规则，在线修正先验协方差。该通用框架集成了连续-离散容积卡尔曼滤波和协方差更新的反馈规则。数值结果表明，该方法在减小不可预测误差、提高计算精度和效率方面具有优势。
