activity
20072021
most citedTarget Localization Accuracy Gain in MIMO Radar Based Systems

658 citations · 1.6k across the 25 of their papers we have counts for

collaborators

26 papers

eess.SP2021

Fast Data-Driven Adaptation of Radar Detection via Meta-Learning

Wei Jiang, Alexander M. Haimovich, Mark Govoni +2

This paper addresses the problem of fast learning of radar detectors with a limited amount of training data. In current data-driven approaches for radar detection, re-training is g…

eess.SP2021★ 1 cited

Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints

Wei Jiang, Alexander M. Haimovich, Osvaldo Simeone

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this paper. We propose two novel learning-based approaches to wavefor…

eess.SP2019

End-to-end Learning of Waveform Generation and Detection for Radar Systems

Wei Jiang, Alexander M. Haimovich, Osvaldo Simeone

An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately:…

eess.SP2019

Blind Sparse Estimation of Intermittent Sources over Unknown Fading Channels

Annan Dong, Osvaldo Simeone, Alexander Haimovich +1

Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors,…

cs.IT2016

SUMMeR: Sub-Nyquist MIMO Radar

David Cohen, Deborah Cohen, Yonina C. Eldar +1

Multiple input multiple output (MIMO) radar exhibits several advantages with respect to traditional radar array systems in terms of flexibility and performance. However, MIMO radar…

cs.IT2016★ 391 cited

Direct Localization for Massive MIMO

Nil Garcia, Henk Wymeersch, Erik G. Larsson +2

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization thanks to their high angul…