Study on Theory and Arithmetic of Stationary Alignment for PINS
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摘要: 分析了平台式惯导系统(PINS)静基座条件下的可观测性,采用系统可观测矩阵的条件数来定量计算PINS静基座可观测性能,找出该条件下的可观测矩阵条件数最小的3个不可观测变量.采用了自适应Kalman算法和常规Kalman算法对简化模型进行了仿真比较.仿真结果表明前者比后者滤波收敛快.进而提出了一种基于Elman神经网络的快速对准方法.Abstract: The observability of stationary alignment for PINS was analyzed systemically, and condition number of observable matrix was adopted to compute quantificationally the observability of stationary alignment for PINS. Three unobservable states with the least condition mumber were chosen in this condition, then both adaptive Kalman filter and general Kalman filter was employed to compare for simplified model. Simulation results showed the former was faster than later. Furthermore, a alignment method based on Elman neural network was proposed.
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Key words:
- inertial navigation /
- observability /
- neural networks /
- attitude errors
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