Bayesian Cramer-Rao Bound for multiple targets tracking in MIMO radar

Phuoc Vu, Alexander M. Haimovich, Braham Himed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Theoretical performance bounds are developed for tracking multiple targets in multiple-input multiple-output (MIMO) radar systems with widely distributed antennas. In previous work, we proposed a direct tracker, which eliminates the need for explicit association of observations to tracks, thus improving tracking performance compared to conventional trackers that estimate time delays and Doppler. In this paper, we develop the Bayesian Cramer Rao Bound (BCRB) for the performance of direct tracking of multiple targets in a distributed MIMO radar. A first-order approximation linearizes the observations with respect to the targets state vector. The BCRB is developed for Swerling Type 1 targets. The theoretical performance bounds are applied to demonstrate the performance of direct trackers versus conventional trackers.

Original languageEnglish (US)
Title of host publication2017 IEEE Radar Conference, RadarConf 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages938-942
Number of pages5
ISBN (Electronic)9781467388238
DOIs
StatePublished - Jun 7 2017
Event2017 IEEE Radar Conference, RadarConf 2017 - Seattle, United States
Duration: May 8 2017May 12 2017

Publication series

Name2017 IEEE Radar Conference, RadarConf 2017

Other

Other2017 IEEE Radar Conference, RadarConf 2017
Country/TerritoryUnited States
CitySeattle
Period5/8/175/12/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

Keywords

  • BCRB
  • MIMO radar
  • Multiple targets tracking

Fingerprint

Dive into the research topics of 'Bayesian Cramer-Rao Bound for multiple targets tracking in MIMO radar'. Together they form a unique fingerprint.

Cite this