用于惯性稳定系统的传感器对准卡尔曼滤波器

L.W. Stimac, T. A. Kennedy
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引用次数: 13

摘要

为了实现一组特定的运动补偿要求和转移对准要求,一种称为运动传感器子系统(MSS)的设备被开发出来。MSS是一种捷联惯性导航系统,结合了两个分散的卡尔曼滤波器,执行传递对准和估计各种捷联惯导系统仪器误差和系统角度失调。一个卡尔曼滤波器传输对准MSS到主导航器和其他滤波器估计和纠正MSS仪器轴和传感器轴之间的不对准。介绍了MSS的设计、仿真、实现和飞行试验。这些过滤器通过一个称为再生的过程耦合在一起,该过程将一个过滤器的共享状态估计传递给另一个过滤器。此外,由于滤波器工作在闭环系统中,滤波器产生的计算时间延迟会显著影响回路的稳定性。讨论了一种提供回路稳定性的方法。MSS飞行试验的结果表明,使用基于密集系统协方差分析和蒙特卡罗模拟的大型卡尔曼滤波器的开发方法可以产生满足要求的系统设计
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Sensor alignment Kalman filters for inertial stabilization systems
To achieve a particular set of motion compensation requirements and transfer alignment requirements a device called a motion sensor subsystem (MSS) was developed. The MSS is a strapdown inertial navigation system coupled with two decentralized Kalman filters that perform transfer alignment and estimation of various SINS instrument errors and system angular misalignments. One Kalman filter transfer aligns the MSS to the master navigator and the other filter estimates and corrects the misalignments between the MSS instrument axes and the sensor axes. The design, simulation, implementation, and flight testing of the MSS are described. These filters are coupled together through a process called regeneration that transfers one filter's shared state estimate to the other. In addition, because the filters operate in a closed-loop system, the computational time delay incurred by the filters significantly affects loop stability. A method of providing loop stability is discussed. The results of the MSS flight test have shown that using a methodology for developing a large Kalman filter based on intensive system covariance analysis and Monte Carlo simulation yields a system design that meets requirements.<>
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