Target estimation by exploiting low rank structure in widely separated MIMO radar

Shunqiao Sun, Kumar Vijay Mishra, Athina P. Petropulu

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

5 Scopus citations


We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates. We process the low-rate samples of all transmit-receive chains at each receiver as data matrices. We demonstrate that each of these matrices is low rank as long as the target moves slowly within a coherent processing interval. We apply recent advances in matrix completion to recover the missing samples of each receiver signal matrix at the common fusion center. The maximum likelihood method is then employed to estimate the targets' positions and Doppler velocities. Unlike other WS-MIMO formulations based on compressed sensing, our approach recovers target parameters at reduced rates, and is gridless. Further, there is no loss of signal-to-noise ratio (SNR) due to subsampling. Numerical experiments demonstrate reasonably accurate target localization at SNR of 20 dB and sampling rate reduction to 20%.

Original languageEnglish (US)
Title of host publication2019 IEEE Radar Conference, RadarConf 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116792
StatePublished - Apr 2019
Event2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States
Duration: Apr 22 2019Apr 26 2019

Publication series

Name2019 IEEE Radar Conference, RadarConf 2019


Conference2019 IEEE Radar Conference, RadarConf 2019
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation


  • Compressed sensing
  • Low rank
  • Matrix completion
  • Target localization
  • Widely separated MIMO radar


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