摘要
针对水下航行器导航定位研究中,系统量测噪声统计特性未知时联邦卡尔曼滤波器不稳定,甚至发散的特点,文中提出了一种在线调节的自适应联邦滤波方法。该方法利用子系统理论残差和实际残差的比值,构造了自适应调整量测噪声方差因子,对子系统量测噪声进行在线调节,实现了联邦卡尔曼滤波的自适应估计。将该算法应用到惯性导航/多普勒/地磁(SINS/DVL/GNS)组合导航系统中,并与标准联邦Kalman滤波进行对比。仿真结果表明,该算法在噪声统计特性未知的情况下,收敛性比标准联邦卡尔曼滤波好,具有较高的滤波精度。
In research of underwater vehicle navigation and positioning, the federated Kalman filter becomes instable or divergent if the statistical feature of noise is unknown. Therefore, an online regulation method of adaptive federated Kalman filter is proposed in this paper. By making use of the ratio of theoretical residual to actual residual of subsystem, a measurement noise variance factor of adaptive regulation to regulate on-line measurement noise of the subsystem, thus adaptive estimation of the federated Kalman filter can be realized. The algorithm is applied to the strapdown inertial navigation system/Doppler velocity log/geomagnetic navigation system（SINS/DVL/ GNS） integrated navigation system, and compared with standard federated Kalman filter. Simulation results show that the proposed algorithm has better convergence performance than the standard Kalman filter under the condition of unknown statistical feature of noise.
作者
高沛林
GAO Pei-lin(School of Electrical and Control Engineering, Xi＇an University of Science and Technology, Xi,an 710054, China)
出处
《水下无人系统学报》
2017年第3期174-179,共6页
Journal of Unmanned Undersea Systems
关键词
水下航行器
组合导航
联邦滤波
自适应
underwater vehicle
integrated navigation
federated filter
adaptive
