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首页> 外文期刊>Automatic Control, IEEE Transactions on >Weight and Time Recursions in Dynamic State Estimation Problem With Mixed-Norm Cost Function
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Weight and Time Recursions in Dynamic State Estimation Problem With Mixed-Norm Cost Function

机译：具有混合范数成本函数的动态状态估计问题中的权重和时间递归

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摘要

The mixed-norm cost functions arise in many applied optimization problems. As an important example, we consider the state estimation problem for a linear dynamic system under a nonclassical assumption that some entries of state vector admit jumps in their trajectories. The estimation problem is solved by means of mixed -norm approximation. This approach combines the advantages of the well-known quadratic smoothing and the robustness of the least absolute deviations method. For the implementation of the mixed-norm approximation, a dynamic iterative estimation algorithm is proposed. This algorithm is based on weight and time recursions and demonstrates the high efficiency. It well identifies the rare jumps in the state vector and has some advantages over more customary methods in the typical case of a large amount of measurements. Nonoptimality levels for current iterations of the algorithm are constructed. Computation of these levels allows to check the accuracy of iterations.
机译：混合范数成本函数出现在许多应用的优化问题中。举一个重要的例子，我们考虑了线性动力学系统的状态估计问题，该估计是在非经典假设下，状态向量的某些项在其轨迹中跳跃。估计问题通过混合范数逼近得以解决。这种方法结合了众所周知的二次平滑的优点和最小绝对偏差方法的鲁棒性。为了实现混合范数逼近，提出了一种动态迭代估计算法。该算法基于权重和时间递归，证明了其高效率。它可以很好地识别状态向量中的罕见跃变，并且在大量测量的典型情况下，它比常规方法具有一些优势。构造了算法当前迭代的非最优水平。这些级别的计算允许检查迭代的准确性。

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