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Joint Nonlinear Sparse Error Correction for Robust State Estimation
IEEE Transactions on Signal Processing ( IF 5.4 ) Pub Date : 2021-10-07 , DOI: 10.1109/tsp.2021.3118493
Sharmin Kibria , Jinsub Kim , Raviv Raich

We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. We consider a nonlinear sparse optimization formulation for joint sparse error correction and robust state estimation in a nonlinear sensing system wherein we exploit the sparsity and short-term invariance properties of gross error locations. We introduce a sequential convex approximation approach that can be used to solve the nonlinear sparse optimization problem with a convergence guarantee. We derive a necessary rank condition that identifiable gross error matrix should satisfy. Then, we present an identifiability-aware version of the proposed algorithm wherein we exploit the aforementioned identifiability condition to improve the accuracy of gross error localization. We demonstrate the efficacy of the proposed approach by applying it to power system nonlinear state estimation of IEEE 14-bus and 118-bus networks.

中文翻译:

鲁棒状态估计的联合非线性稀疏误差校正

我们研究了多周期测量向量的联合非线性状态估计,这些向量可能被稀疏的粗略错误破坏。我们在非线性传感系统中考虑用于联合稀疏误差校正和鲁棒状态估计的非线性稀疏优化公式,其中我们利用了粗误差位置的稀疏性和短期不变性。我们引入了一种顺序凸逼近方法,该方法可用于解决具有收敛保证的非线性稀疏优化问题。我们推导出可识别的总误差矩阵应满足的必要秩条件。然后,我们提出了所提出算法的可识别性版本,其中我们利用上述可识别性条件来提高粗差定位的准确性。
更新日期:2021-11-09
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