Distributed Radar Multi-frame Detection with Least Squares Quantization

Jing Lu, Shenghua Zhou, Pramod K. Varshney, Jibin Zheng, Xiaojun Peng, Hongwei Liu, Zhiqiang Shao

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations


In a distributed multiple-input multiple-output (MIMO) radar system, multiple-frame local observations can be transmitted to a fusion center (FC) for a better detection performance, but the communication cost may be huge. In this paper, we study how to impose the least squares quantization (LSQ) method on distributed multi-frame detection (MFD). Local test statistics instead of raw signals are quantized by the LSQ algorithm and then a decision rule is formulated based on the dynamic-programming based MFD algorithm. Numerical results indicate that the LSQ algorithm causes an insignificant detection performance loss at three-bit LSQ quantization. Meanwhile, this method greatly reduces the computational complexity and the communications bandwidth costs.

Original languageEnglish (US)
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189420
StatePublished - Sep 21 2020
Externally publishedYes
Event2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
Duration: Sep 21 2020Sep 25 2020

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2020 IEEE Radar Conference, RadarConf 2020


  • distributed MIMO radar
  • dynamic programming
  • least squares quantization
  • multi-frame detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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