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Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion
Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion
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机译：σ点卡尔曼滤波器在导航系统中的非线性估计和传感器融合
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摘要
A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.
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