An extended method for Statistical Signal Characterization using moments and cumulants, as a fast and accurate pre-processing stage of simple ANNs applied to the recognition of pattern alterations in pulse-like waveforms

TL;DR

This paper introduces an extended statistical feature extraction method, ESSC, for pulse waveform recognition that enhances accuracy and reduces computational complexity, making it suitable for low-resource systems.

Contribution

The paper presents an extension of the Statistical Signal Characterization method, ESSC, which improves pattern recognition accuracy and efficiency in neural networks.

Findings

ESSC achieves around 90% accuracy in pulse recognition tasks.

The approach reduces execution time by approximately 4 times compared to deep learning.

It maintains high accuracy at SNR above 20dB, suitable for practical scenarios.

Abstract

We propose a feature-extraction procedure based on the statistical characterization of waveforms, applied as a fast pre-processing stage in a pattern recognition task using simple artificial neural network models. This procedure involves measuring a set of 30 parameters, including moments and cumulants obtained from the waveform, its derivative, and its integral. The technique is presented as an extension of the Statistical Signal Characterization method, which is already established in the literature, and we referred to it as ESSC. As a testing methodology, we employed a procedure to distinguish a pulse-like signal from different versions of itself with altered or deformed frequency spectra, under various signal-to-noise ratio (SNR) conditions of Gaussian white noise. The recognition task was performed by machine learning networks using the proposed ESSC feature extraction method. Additionally, we compared the results with those obtained using raw data inputs in deep learning networks. The algorithms were trained and tested on cases involving Sinc-, Gaussian-, and Chirp-pulse waveforms. We measure accuracy and execution time for the different algorithms solving these pattern-recognition cases, and evaluate the architectural complexity of building such networks. We conclude that a simple multi-layer perceptron network using ESSC can achieve an accuracy of around 90%, comparable to that of deep learning algorithms, when solving pattern recognition tasks in practical scenarios with SNR above 20dB. Additionally, this approach offers an execution time approximately 4 times shorter and significantly lower network construction complexity, enabling its use in low-resource computational systems.