Numerous deep learning-based methodologies have been proposed to facilitate automatic sleep stage classification tasks. Nevertheless, the black-box nature of these approaches is one of the skeptical factors hindering clinical application. Towards model interpretability, this study presents a novel interpretable sleep stage classification scheme based on layer-wise relevance propagation (LRP). We first adopt the short-time Fourier transform (STFT) to convert the raw electroencephalogram (EEG) signals to the time-frequency images, which could visually demonstrate EEG patterns of each sleep stage. Moreover, we introduce an efficient convolutional neural network (CNN) based model, namely MSSENet, that assembles with the Multi-Scale CNN module and residual Squeeze-and-Excitation block for the image input. The LRP method is eventually applied to evaluate the contribution of each frequency pixel in the input time-frequency image to the model prediction. Experimental findings show that the MSSENet could outperforms or achieves comparable performance to other state-of-the-art approaches on three polysomnography (PSG) datasets. Furthermore, through utilizing the heat mapping, the LRP-based explainability results validate the high relevance of specific EEG patterns to the prediction of the corresponding sleep stage, which is consistent with the sleep scoring guidelines.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

A variety of approaches have been exploited for DOA estimation with unknown mutual coupling in recent years. However, they usually suffer performance degradation under impulsive noise. To overcome this problem, an improved method for DOA estimation in the presence of unknown mutual coupling is proposed. Firstly, a sparse representation of the received signals is created by the aid of the structure of mutual coupling matrix (MCM). Subsequently, a Cauchy-score based cost function is utilized to reconstruct the sparse model and realize DOA estimation. Numerous experiments are carried out to evaluate the algorithms and simulation results confirm that our method shows superior performance than existing methods.

This work presents an effective and robust methodology to achieve the joint estimation of direction-of-arrival (DOA) and range. One or more near-field sources are the signals of interest. Far-field sources and alpha-stable noise are additive interferences and will be removed in two steps. First, alpha-stable noise is suppressed into Gaussian or sub-Gaussian noise through a proposed mathematical limiter. Then, the near-field sources are separated from the mixed sources according to the specific technique. The two steps involve three key technologies, including noise reduction, sparse reconstruction, and subspace decomposition. In the experiments, we design an assessment standard for missing detection in data calculation, which is of utmost importance to demonstrate the novel methodology's superiority compared to the other existing competitive ones.

Direction of arrival (DOA) estimation is an important research topic in array signal processing and widely applied in practical engineering. However, when signal sources are highly correlated or coherent, conventional subspace-based DOA estimation algorithms will perform poorly due to the rank deficiency in the received data covariance matrix. Moreover, conventional DOA estimation algorithms are usually developed under Gaussian-distributed background noise, which will deteriorate significantly in impulsive noise environments. In this paper, a novel method is presented to estimate the DOA of coherent signals in impulsive noise environments. A novel correntropy-based generalized covariance (CEGC) operator is defined and proof of boundedness is given to ensure the effectiveness of the proposed method in impulsive noise environments. Furthermore, an improved Toeplitz approximation method combined CEGC operator is proposed to estimate the DOA of coherent sources. Compared to other existing algorithms, the proposed method can avoid array aperture loss and perform more effectively, even in cases of intense impulsive noise and low snapshot numbers. Finally, comprehensive Monte-Carlo simulations are performed to verify the superiority of the proposed method under various impulsive noise conditions.

In this paper, a novel method is proposed for direction of arrival (DOA) estimation in the presence of impulsive noise. By introducing the hyperbolic secant function into the framework, a novel DOA estimator is designed. Based on the proposed estimator and low-rank decomposition, a robust subspace is obtained through alternative optimization. DOA estimation is achieved by applying the multiple signal classification (MUSIC) algorithm to the derived subspace. A detailed optimization procedure is outlined, and the convergence of the proposed estimator is proven. Comprehensive simulation results demonstrate that the proposed method could achieve superior performance in impulsive noise environments, especially under strongly impulsive conditions.