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Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution.

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  • Moving horizon estimation (en)
  • 滾動時域估計 (zh)
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  • 滾動時域估計（Moving horizon estimation、MHE）是一種利用一連串量測的信號進行最优化的作法，量測的信號中包括雜訊（隨機變異）以及其他的不準確性，根據這些信號產生未知參數或是變數的估計值。滾動時域估計和確定性的作法不同，滾動時域估計需要遞迴式的求解法，利用线性规划或非线性规划來找到對應的解。 若在一些可以簡化的條件下，滾動時域估計可以簡化成卡尔曼滤波。在針對及滾動時域估計的評估中，發現滾動時域估計的性能有所提昇，唯一需要付出的代價是其計算成本。因為滾動時域估計在計算上的成本較高，因此一般會應用在運算資源較充裕的系統，而且是反應較慢的系統。不過在文獻中已有不少加速的方法。 (zh)
  • Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution. (en)
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  • Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution. MHE reduces to the Kalman filter under certain simplifying conditions. A critical evaluation of the extended Kalman filter and the MHE found that the MHE improved performance at the cost of increased computational expense. Because of the computational expense, MHE has generally been applied to systems where there are greater computational resources and moderate to slow system dynamics. However, in the literature there are some methods to accelerate this method. (en)
  • 滾動時域估計（Moving horizon estimation、MHE）是一種利用一連串量測的信號進行最优化的作法，量測的信號中包括雜訊（隨機變異）以及其他的不準確性，根據這些信號產生未知參數或是變數的估計值。滾動時域估計和確定性的作法不同，滾動時域估計需要遞迴式的求解法，利用线性规划或非线性规划來找到對應的解。 若在一些可以簡化的條件下，滾動時域估計可以簡化成卡尔曼滤波。在針對及滾動時域估計的評估中，發現滾動時域估計的性能有所提昇，唯一需要付出的代價是其計算成本。因為滾動時域估計在計算上的成本較高，因此一般會應用在運算資源較充裕的系統，而且是反應較慢的系統。不過在文獻中已有不少加速的方法。 (zh)
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