29 citations · 70 across the 25 of their papers we have counts for
13 papers · 1 filter
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
Arian Eamaz, Farhang Yeganegi, Yunqiao Hu +2
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper in…
From Quasi-Isometric Embeddings to Finite-Volume Property: A Theoretical Framework for Quantized Matrix Completion
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We delve into the impact of memoryless scalar quantization on matrix completion. Our primary motivation for this research is to evaluate the recovery performance of nuclear norm mi…
Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems
Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz +1
Intelligent reflecting surfaces (IRS) and their optimal deployment are the new technological frontier in sensing applications. Recently, IRS have demonstrated potential in advancin…
Matrix Completion from One-Bit Dither Samples
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with time-varying thres…
HDR Imaging With One-Bit Quantization
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Modulo sampling and dithered one-bit quantization frameworks have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and…
Space-Time Adaptive Processing for radars in Connected and Automated Vehicular Platoons
Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
In this study, we develop a holistic framework for space-time adaptive processing (STAP) in connected and automated vehicle (CAV) radar systems. We investigate a CAV system consist…
Low-rank Matrix Sensing With Dithered One-Bit Quantization
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
We explore the impact of coarse quantization on low-rank matrix sensing in the extreme scenario of dithered one-bit sampling, where the high-resolution measurements are compared wi…
Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing
Arian Eamaz, Farhang Yeganegi, Deanna Needell +1
One-bit quantization with time-varying sampling thresholds (also known as random dithering) has recently found significant utilization potential in statistical signal processing ap…
Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach
Zahra Esmaeilbeig, Mojtaba Soltanalian
This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorit…
Moving Target Detection via Multi-IRS-Aided OFDM Radar
Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra +1
An intelligent reflecting surface (IRS) consists of passive reflective elements capable of altering impinging waveforms. The IRS-aided radar systems have recently been shown to imp…
One-Bit Quadratic Compressed Sensing: From Sample Abundance to Linear Feasibility
Arian Eamaz, Farhang Yeganegi, Deanna Needell +1
One-bit quantization with time-varying sampling thresholds has recently found significant utilization potential in statistical signal processing applications due to its relatively…
Near-Field Low-WISL Unimodular Waveform Design for Terahertz Automotive Radar
Arian Eamaz, Farhang Yeganegi, Kumar Vijay Mishra +1
Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation. However, recent ultra-short-range automotive radar applications at u…
Quantized Phase-Shift Design of Active IRS for Integrated Sensing and Communications
Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra +1
Integrated sensing and communications (ISAC) is a spectrum-sharing paradigm that allows different users to jointly utilize and access the crowded electromagnetic spectrum. In this…