Input and state estimation exploiting input sparsity
Input and state estimation exploiting input sparsity
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DOI：
10.23919/ecc.2019.8795699
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
S. Fosson;Federica Garin;S. Gracy;A. Kibangou;D. Swart
中科院分区：
文献类型：
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作者：
S. Fosson;Federica Garin;S. Gracy;A. Kibangou;D. Swart
在网络物理安全应用的激励下，我们面临着估计线性系统的状态和输入的问题，其中输入可能代表对抗性攻击的存在。我们考虑了由于量测数目太少而不能使用经典滤波器的情况，例如，它低于输入向量的大小。如果输入虽然很大，但已知是稀疏的，那么这个问题可以使用压缩感知理论的技术来解决。在本文中，我们提出了一种基于压缩感知和类卡尔曼滤波的递推估值器，它能够从有噪声的压缩测量中重建状态和输入。仿真结果表明，相对于Oracle估计器，该算法具有较高的恢复精度。
Motivated by cyber-physical security applications, we face the problem of estimating the state and the input of a linear system, where the input may represent the presence of adversarial attacks. We consider the case where classical filters cannot be used, because the number of measurements is too low, for example it is lower than the size of the input vector. If the input, although of large size, is known to be sparse, the problem can be tackled using techniques from compressed sensing theory. In this paper, we propose a recursive estimator, based on compressed sensing and Kalman-like filtering, which is able to reconstruct both the state and the input from noisy, compressed measurements. The proposed algorithm is proved to be feasible and numerically efficient, and simulations show a good recovery accuracy with respect to an oracle estimator.