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Found 9 papers, 0 papers with code
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).
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.
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.
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.
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.
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.
