Small Autonomous Underwater Vehicle Navigation System Based onAdaptive UKF Algorithm
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摘要: 针对海流扰动及姿态、航向误差角引起的无法确知的导航系统模型误差, 设计了一种带模型误差的自适应无迹卡尔曼滤波器(Adaptive unscented Kalman filter, AUKF)用于小型水下机器人(Small autonomous underwater vehicle, SAUV)推位导航系统. 首先提出了小型水下机器人三维运动连续时间模型; 然后针对该模型特点, 基于极大后验估值原理推导了AUKF算法. 仿真结果说明该算法能够克服海流扰动及姿态和航向误差引起的模型误差. 对比经典无迹卡尔曼滤波器算法, 采用该算法的小型水下机器人推位导航系统在复杂海况下的滤波精度显著提高.
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关键词:
- 自适应无迹卡尔曼滤波器 /
- 小型水下机器人 /
- 推位导航系统 /
- 非线性 /
- 极大后验估计
Abstract: Ocean current disturbance and attitude, heading errors can cause uncertain navigation system model error. To solve the above problem, an adaptive unscented Kalman filter (AUKF) with model error is designed for a small autonomous underwater vehicle's (SAUV) dead reckoning (DR) navigation system. Firstly, three-dimensional motion of SAUV continuous time model is designed. Then, the proposed AUKF algorithm is deduced according to maximum a posterior (MAP) theory. Finally, simulation results show that the algorithm can overcome the model error caused by disturbance currents and attitude, and heading errors. Compared with the conventional UKF algorithm, the filter precision of the SAUV's DR navigation system in complex sea state is improved a lot by adopting the proposed algorithm. -
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