基于自适应Kalman滤波的合成孔径声纳运动估计

合成孔径声纳姿态、位移测量系统一般包括：惯性测量单元（IMU），差分DGPS，声多普勒计程仪DVL，深度传感器等。通常情况下，惯性测量单元（IMU）必须与DVL或DGPS进行数据融合，才能减小发散现象，提高导航精度。合成孔径声纳基阵安装在拖体上，由水面舰船利用拖缆拖曳航行，其运动受海流扰动及拖船机动的影响，未知的机动输入估计及海流的估计是必须要考虑的，这是合成孔径声纳基阵运动估计的比较特殊的地方。传统的Kalman滤波器不能直接应用于合成孔径声纳姿态、运动估计。因此，采用自适应Kalman滤波算法来处理合成孔径声纳姿态、运动估计问题。数值仿真表明，该方法较好地解决了合成孔径声纳姿态、运动估计问题。

Synthetic Aperture Sonar Movement and Attitude Estimation——The Adaptive Kalman Filter Approach

The inertial navigation systems include IMU, DVL, DGPS, and deep sensor. The movement of underwater towed fish can be considered stochastic disturb on known heading and velocity of surface ship, and be influenced by sea flow,we must consider the estimation of the unknown maneuvering input and sea flow. Fore this reason, we present a new estimation algorithm, which is adequately applied to estimate the abrupt change of input. The approach consists of a Kalman filter without an input term, and the other is the adaptive forgetting factors RISE. From the number simulation, we can conclude that as the unknown varies over the long time interval, the proposed method is robustness.