Search Results for author: Arindam Bose

Found 6 papers, 1 papers with code

Waveform Design for Mutual Interference Mitigation in Automotive Radar

no code implementations8 Aug 2022 Arindam Bose, Bo Tang, Wenjie Huang, Mojtaba Soltanalian, Jian Li

The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.

Efficient Waveform Covariance Matrix Design and Antenna Selection for MIMO Radar

1 code implementation13 Feb 2020 Arindam Bose, Shahin Khobahi, Mojtaba Soltanalian

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.

Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network

no code implementations3 Feb 2020 Chirag Agarwal, Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian, Dan Schonfeld

The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications.

Deep Radar Waveform Design for Efficient Automotive Radar Sensing

no code implementations17 Dec 2019 Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian

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.

Autonomous Vehicles Radar waveform design

Deep One-bit Compressive Autoencoding

no code implementations10 Dec 2019 Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian

Parameterized mathematical models play a central role in understanding and design of complex information systems.

Comprehensive Personalized Ranking Using One-Bit Comparison Data

no code implementations6 Jun 2019 Aria Ameri, Arindam Bose, Mojtaba Soltanalian

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.

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