Kalman Filtering with Unknown Sensor Measurement Losses<ce:cross-ref id='cecref0015' refid='fn1'> <ce:sup loc='post'>*</ce:sup> </ce:cross-ref> <ce:footnote id='fn1'> <ce:label>*</ce:label> <ce:note-para id='npara0001' view='all'>This work was supported by the National Natural Science Foundation of China (41576101), and Tsinghua University Initiative Scientific Research Program.</ce:note-para> </ce:footnote>

Jiaqi Zhang; Keyou You

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This work was supported by the National Natural Science Foundation of China (41576101), and Tsinghua University Initiative Scientific Research Program.

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Kalman Filtering with Unknown Sensor Measurement Losses * * This work was supported by the National Natural Science Foundation of China (41576101), and Tsinghua University Initiative Scientific Research Program.

机译：具有未知传感器测量损耗的卡尔曼滤波 * < ce：footnote id =“ fn1”> * 这项工作得到了国家自然科学基金的支持（41576101）和清华大学主动科学研究计划。

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Abstract: This work studies the state estimation problem of a networked linear system where a sensor and an estimator are connected via a lossy network. If the measurement loss is known to the estimator, the minimum variance estimate is easily computed by the intermittent Kalman filter (IKF). However, this does not hold for the case of unknown measurement losses, and we have to address the non-Gaussianityon-linearity of the networked system. By exploiting the measurement loss process and the IKF, we design three recursive suboptimal filters for state estimation, i.e., BKF-I, BKF-II and RBPF. The BKF-I is based on the MAP estimator of the loss process and the BKF-II is derived by an estimate of the conditional loss probability. The RBPF is an effective sequential importance sampling algorithm by marginalizing out the loss process. A target tracking example is included to illustrate their effectiveness and shows the tradeoff between computation complexity and estimation accuracy of the proposed filters.

机译：摘要：这项工作研究了通过损耗网络连接传感器和估计器的网络线性系统的状态估计问题。如果估算器知道测量损耗，则可以通过间歇式卡尔曼滤波器（IKF）轻松计算出最小方差估算值。但是，这不适用于未知的测量损耗，因此我们必须解决网络系统的非高斯/非线性问题。通过利用测量损失过程和IKF，我们设计了三个用于状态估计的递归次优滤波器，即BKF-I，BKF-II和RBPF。 BKF-I基于损失过程的MAP估计器，而BKF-II通过对条件损失概率的估计得出。 RBPF通过边缘化损失过程是一种有效的顺序重要性抽样算法。包括一个目标跟踪示例以说明其有效性，并显示了建议的滤波器的计算复杂度和估计精度之间的折衷。

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来源
《IFAC PapersOnLine》 |2016年第22期|共6页
作者
Jiaqi Zhang; Keyou You;
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中图分类自动化技术、计算机技术;
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Kalman Filtering with Unknown Sensor Measurement Losses * * This work was supported by the National Natural Science Foundation of China (41576101), and Tsinghua University Initiative Scientific Research Program.

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