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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements
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State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements

机译：缺少测量的异步多速率多传感器非线性动力学系统的状态估计

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摘要

This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.
机译：本文涉及一种非线性多速率多传感器异步采样动态系统的状态估计。 N个传感器以多个采样率独立地观察单个目标，动态系统以最高采样率制定。观察是异步获得的，每个传感器可能会以一定概率随机丢失数据。融合状态估计是使用多尺度系统理论和改进的sigma点卡尔曼滤波器生成的。结果表明，我们的主要结果改进和扩展了现有的sigma点Kalman滤波器，对于该样本，采样率不均匀。本文还允许采用伯努利分布随机丢失的测量。最后，通过数值仿真的例子说明了所提算法的可行性和有效性。

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