localization; stepped frequency modulation; joint range-DoA estimation; sparse sensing
Abstract :
[en] Multi-target localization, warranted in emerging
applications like autonomous driving, requires targets to be
perfectly detected in the distributed nodes with accurate range
measurements. This implies that high range resolution is crucial
in distributed localization in the considered scenario. This work
proposes a new framework for multi-target localization, addressing
the demand for the high range resolution in automotive applications
without increasing the required bandwidth. In particular,
it employs sparse stepped frequency waveform and infers the
target ranges by exploiting sparsity in target scene. The range
measurements are then sent to a fusion center where direction of
arrival estimation is undertaken. Numerical results illustrate the
impact of range resolution on multi-target localization and the
performance improvement arising from the proposed algorithm
in such scenarios.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Electrical & electronics engineering
Author, co-author :
Sedighi, Saeid ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Shankar, Bhavani ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Maleki, Sina ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Ottersten, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Multi-Target Localization in Asynchronous MIMO Radars Using Sparse Sensing
Publication date :
2017
Event name :
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Event organizer :
IEEE
Event place :
Curaçao, Dutch Antilles, Netherlands
Event date :
10-12-2017 to 13-12-2017
Audience :
International
Main work title :
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)