Sparse decomposition framework for maximum likelihood classification under alpha-stable noise

Abstract

Recently, automatic modulation classification has gained a lot of attention in the area of cognitive radio (CR), signal detection, electronic warfare and surveillance etc. Most of the existing modulation classification algorithms are developed based on the assumption that the received signal to be identified is corrupted by only additive white Gaussian noise. The performances of these conventional algorithms degrade significantly by addition of impulse noise. In this paper, we propose a robust algorithm using sparse signal decomposition which comprises of an overcomplete dictionary for detection and classification of modulated signals. In this work, an overcomplete dictionary is constructed using the identity basis, cosine and sine elementary waveforms to capture morphological components of the impulse noise and deterministic modulated signals effectively. The proposed method of modulation classification consists of the three major steps: sparse signal decomposition (SSD) on hybrid dictionaries, modulated signal extraction, and maximum likelihood (ML) based classification. The testing and validation of both direct ML and SSD-based ML classification methods are carried out under different Gaussian and impulse noise conditions for modulation classification. Our proposed method achieves a classification accuracy of 85% at 5 dB SNR and outperforms the conventional classification methods.

Intelligent Signal Classification in Industrial Distributed Wireless Sensor Networks Based Industrial Internet of Things

“…In the same simulation environment and the same signal parameter settings, feature fusion network based on Dense-Blocks is compared with the method in [19] and DenseNet without feature fusion with α = 1.2, and the comparison results are shown in Fig. 17.…”

“…In [18], a new method based on explicit countless cost function and global optimization is designed. The authors in [19] put forward a modulation type classification method using sparse signal decomposition (SSD) of additive mixture Gaussian noise and impulse noise with an over complete mixture dictionary.…”

An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

“…Therefore, the test signal has been generated by the Eq. 16x[n] = sin(ωn) − 0.18 sin(3ωn) + 0.11 sin(5ωn), (16) where ω = 2π60 t for a power system in 60 Hz, t is the sampling time and n = 0, 1, . .…”

“…Table 2 presents the results obtained applying the four possible dictionaries to estimate the harmonic components of the signal defined by Eq. (16).…”

“…In the last decade, numerous applications in signal processing have used the Sparse Signal Decomposition (SSD) technique with Overcomplete Hybrid Dictionary (OHD) for detection and classification of modulated signals [16], analysis of ultrasound signals [17], [18], automatic target recognition in radar images [19], denoising and analysis of biomedical signals as electrocardiogram (ECG) [20], [21] and electroencephalogram (EEG) [22], among others. In the analysis of signals in power systems, [23] presents a study for compression and denoising of signals with power quality disturbances, while [24] does the detection and classification of these disturbances.…”