Search Results for author:
Found 30 papers, 3 papers with code
Efficient Waveform Covariance Matrix Design and Antenna Selection for MIMO Radar
In this paper, we investigate the joint optimization of the waveform covariance matrix and the antenna position vector for a MIMO radar system to approximate a given transmit beam-pattern, as well as to minimize the cross-correlation between the probing signals at a number of given target locations.
Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding
Parameterized mathematical models play a central role in understanding and design of complex information systems.
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.
Deep Signal Recovery with One-Bit Quantization
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.
Comprehensive Personalized Ranking Using One-Bit Comparison Data
The task of a personalization system is to recommend items or a set of items according to the users' taste, and thus predicting their future needs.
Deep One-bit Compressive Autoencoding
Parameterized mathematical models play a central role in understanding and design of complex information systems.
Deep Radar Waveform Design for Efficient Automotive Radar Sensing
In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters.
Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network
The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications.
UPR: A Model-Driven Architecture for Deep Phase Retrieval
The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications.
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA).
Unfolded Algorithms for Deep Phase Retrieval
Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications.
On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays
Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements.
IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces
We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance.
Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging
Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns.
One-Bit Phase Retrieval: More Samples Means Less Complexity?
The classical problem of phase retrieval has found a wide array of applications in optics, imaging and signal processing.
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part I: Stationary Signals
Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold.
One-Bit Compressive Sensing: Can We Go Deep and Blind?
The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements.
Waveform Design for Mutual Interference Mitigation in Automotive Radar
The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.
Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar
Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission.
Moving Target Detection via Multi-IRS-Aided OFDM Radar
In this paper, we consider a multi-IRS-aided orthogonal frequency-division multiplexing (OFDM) radar and study the theoretically achievable accuracy of target detection.
Near-Field Low-WISL Unimodular Waveform Design for Terahertz Automotive Radar
Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation.
Mutual Interference Mitigation in PMCW Automotive Radar
This paper addresses the challenge of mutual interference in phase-modulated continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems.
Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach
This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorithm unrolling.
HDR Imaging With One-Bit Quantization
Additionally, we introduce a sufficient condition specifically designed for UNO sampling to perfectly recover non-bandlimited signals within spline spaces.
Space-Time Adaptive Processing for radars in Connected and Automated Vehicular Platoons
Direct application of STAP in a network of radar systems such as in a CAV may lead to excess interference.
Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems
In this paper, we design the placement of IRS platforms for sensing by maximizing the mutual information.
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging.
Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?
In particular, we consider the joint design objective of maximizing the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and communication users by leveraging the extra degrees-of-freedom offered in the BD-RIS setting.
Ambiguity Function Shaping in FMCW Automotive Radar
In this paper, we develop a novel code optimization method that attenuates the side-lobes of the radar's ambiguity function.
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy.
