Importance

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Abstract

A method and apparatus is provided for processing a measurement process to estimate a signal process, even if the signal and/or measurement processes have large and/or expanding ranges. The method synthesizes training data comprising realizations of the signal and measurement processes into a primary filter for estimating the signal process and, if required, an ancillary filter for providing the primary filter's estimation error statistics. The primary and ancillary filters each comprise an artificial recurrent neural network (RNN) and at least one range extender or reducer. Their implementation results in the filtering apparatus. Many types of range extender and reducer are disclosed, which have different degrees of effectiveness and computational cost. For a neural filter under design, range extenders and/or reducers are selected from those types jointly with the architecture of the RNN in consideration of the filtering accuracy, the RNN size and the computational cost of each selected range extender and reducer so as to maximize the cost effectiveness of the neural filter. The aforementioned synthesis is performed through training RNNs together with range extenders and/or reducers.

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First Claim