TY - GEN
T1 - Distributed Radar Multi-frame Detection with Least Squares Quantization
AU - Lu, Jing
AU - Zhou, Shenghua
AU - Varshney, Pramod K.
AU - Zheng, Jibin
AU - Peng, Xiaojun
AU - Liu, Hongwei
AU - Shao, Zhiqiang
N1 - Funding Information:
This work is partially supported by the national Science Fund for Distinguished Young Scholars (61525105), the fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project No. B18039), Xidian University-Syracuse University Joint Center for Information Fusion, the program for Cheung Kong Scholars and Innovative Research Team in university, and the National Natural Science Foundation of China (61601340).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/21
Y1 - 2020/9/21
N2 - 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.
AB - 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.
KW - distributed MIMO radar
KW - dynamic programming
KW - least squares quantization
KW - multi-frame detection
UR - http://www.scopus.com/inward/record.url?scp=85098585996&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098585996&partnerID=8YFLogxK
U2 - 10.1109/RadarConf2043947.2020.9266325
DO - 10.1109/RadarConf2043947.2020.9266325
M3 - Conference contribution
AN - SCOPUS:85098585996
T3 - IEEE National Radar Conference - Proceedings
BT - 2020 IEEE Radar Conference, RadarConf 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Radar Conference, RadarConf 2020
Y2 - 21 September 2020 through 25 September 2020
ER -