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Boundedness of covariance estimates of extended Kalman filtering with directional measurements and linear state dynamics

机译:方向测量和线性状态动态扩展卡尔曼滤波的协方差估计的界限

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In wildlife telemetry and several other applications, dynamical systems arise where all of the measurements consist of angles. Here we treat the problem of classical extended Kalman filtering in this situation, and analyze the equation that describe the covariance estimates of the measurement error. It is shown that under some mild hypotheses the error covariance remains bounded by bounds which can be precomputed from noise and system data, and that the upper bound tends to zero when the state and measurement covariance tend to zero.
机译:在野生动物遥测和其他几种应用中,动态系统出现在角度的所有测量中。在这里,我们在这种情况下对经典扩展卡尔曼滤波的问题进行处理,并分析描述测量误差的协方差估计的等式。结果表明,在一些温和的假设下,误差协方差仍然受到噪声和系统数据预先计算的界限的界限,并且当状态和测量协方差趋于为零时,上限趋于为零。

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