Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Abstract

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios

Discriminative estimated cumulants-based automatic modulation classification over multipath fading channels

“…where p is the order of the cumulant, q is the complex conjugate order of the cumulant, and cum function is defined as [9]:…”

A Sparse Modulation Signal Bispectrum Analysis Method for Rolling Element Bearing Diagnosis

“…However, bispectrum analysis generally requires the signal to be steady-state; for unsteady or cyclostationary signals, the analysis results are not accurate enough. So many scholars had proposed some improved algorithms based on the bispectrum analysis for different research objects and specific questions, like wavelet domain bispectrum analysis [2][3][4], order bispectrum analysis [5], vector bispectrum analysis [6], cyclic bispectrum analysis [7,8], and so on [9][10][11]. In 2004, a new AM detector and its normalized form are proposed and defined [12]; Gu et al named this method as the modulation signal bispectrum (MSB) analysis and achieved fault diagnosis of downstream mechanical equipment using electrical motor current signal based on MSB in 2011 [13].…”

Machine Learning Algorithms for Identification of Low Signal-to-Noise Ratio Radar Signals

“…The trained classifier from Problem 5 was tested using the simulated single LFM pulse using the parameters given in Table 13. Classification results are shown in Figures 39,40,41,42,43,and 44. Since the parameters are not close to the realistic parameters, the classifiers are not expected to perform perfectly even at high SNR.…”

“…El-Khamy and Elsayed [40] classified multi-user chirp LFM signals using wavelets. Neural networks, support vector machines and maximum likelihood estimators were used to classify the signals.…”