相关论文: Advancing Single-Snapshot DOA Estimation with Siam…

Intelligent reflecting surface (IRS) is expected to play a pivotal role in future wireless sensing networks owing to its potential for high-resolution and high-accuracy sensing. In this work, we investigate a multi-target…

The lack of a large amount of training data has always been the constraining factor in solving a lot of problems in machine learning, making One Shot Learning one of the most intriguing ideas in machine learning. It aims to learn…

The near-field effect of short-range multiple-input multiple-output (MIMO) systems imposes many challenges on direction-of-arrival (DoA) estimation. Most conventional scenarios assume that the far-field planar wavefronts hold. In this…

The self-attention mechanism has emerged as a critical component for improving the performance of various backbone neural networks. However, current mainstream approaches individually incorporate newly designed self-attention modules (SAMs)…

The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a novel framework for the band selection problem:…

In this paper, a new signal processing framework is proposed, in which the array time samples are represented in DOA-frequency domain through a single stage problem. It is shown that concatenated array data is well represented in a…

Deep neural networks have emerged as powerful tools for learning operators defined over infinite-dimensional function spaces. However, existing theories frequently encounter difficulties related to dimensionality and limited…

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand…

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and…

We consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a moving receiver, and present a Deep Learning (DL) framework for passive synthetic aperture radar (SAR) imaging. Existing…

Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed impressive progress propelled by deep learning methods.…

Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original…

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial…

Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited…

We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of non-uniform linear arrays, referred to as co-prime arrays, in this paper. By exploiting the second order statistical information of the…

Spiking Neural Networks (SNNs) are one of the most promising bio-inspired neural networks models and have drawn increasing attention in recent years. The event-driven communication mechanism of SNNs allows for sparse and theoretically…

This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyper-spectral imagery of supervised (shallow or deep) convolutional networks is very…

Apart from the conventional parameters (such as signal-to-noise ratio, array geometry and size, sample size), several other factors (e.g. alignment of the antenna elements, polarization parameters) influence the performance of direction of…

In practical scenarios, processes such as sensor design, manufacturing, and installation will introduce certain errors. Furthermore, mutual interference occurs when the sensors receive signals. These defects in array systems are referred to…

In this paper, we tackle for the first time the problem of maximum likelihood (ML) estimation of the signal-to-noise ratio (SNR) parameter over time-varying single-input multiple-output (SIMO) channels. Both the data-aided (DA) and the…