Estimation of The Frequency of Very Short Signals by Involving Artificial Neural Networks
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Abstract
This paper proposes a method to accurately estimate the frequencies of signals that contain less than one and a half periods of the targeted frequency. A behavioral model is developed by finding the amplitudes of three points on the main lobe of the Discrete Fourier Transform (DFT) of a zero-padded generated harmonic signal. The amplitudes are normalized, and the distance between the generated and roughly estimated frequency is found. The signal is shortened, and the process is repeated. We train an Artificial Neural Network (ANN) with the obtained results, namely with the amplitudes as Input data and the distance as the Target. With this network, we succeeded in estimating the frequencies with high accuracy, the errors being of the order of millihertz.
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