TY - JOUR
T1 - A Hybrid Integration Method for Moving Target Detection with GNSS-Based Passive Radar
AU - He, Zhenyu
AU - Yang, Yang
AU - Chen, Wu
N1 - Funding Information:
Manuscript received July 28, 2020; revised October 21, 2020; accepted November 3, 2020. Date of publication November 10, 2020; date of current version January 6, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0501803, in part by the Shenzhen Science and Technology Innovation Commission under Grant JCYJ20170818104822282, and in part by the Hong Kong Research Grants Council (RGC) Competitive Earmarked Research Grants under Project PolyU 152151/17E. (Corresponding author: Wu Chen.) The authors are with the Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University 999077, Hong Kong, and also with Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China (e-mail: zhenyu.he@connect. polyu.hk; [email protected]; [email protected]). Digital Object Identifier 10.1109/JSTARS.2020.3037200
Funding Information:
his work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0501803, in part by the Shenzhen Science and Technology Innovation Commission under Grant JCYJ20170818104822282, and in part by the HongKong Research Grants Council (RGC) Competitive Earmarked Research Grants under Project PolyU 152151/17E.
Publisher Copyright:
© 2008-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Global navigation satellite system (GNSS) based passive radar has been applied in the detection of moving targets. However, the low signal power of GNSS on the earth's surface limits the application of this technology for the long-range or low-observable target detection. Increasing the observation time can effectively improve the detection capability. But the target motion involves the range cell migration (RCM) and the Doppler frequency migration (DFM) over the long observation time, which results in the integration gain loss and lower the detection performance. This article proposes a new hybrid coherent and noncoherent integration method named the keystone transform and Lv's distribution. The proposed method not only compensate the RCM and the DFM but also provide coherent and noncoherent integration gains to increase the signal-to-noise ratio. The simulated results and the field trial results demonstrate that the detection performance of the proposed method is superior to the other two known moving target detection methods. And the analysis of the computational complexity shows that the proposed method and the other two methods are in the same order of ${\mathrm O}({{N^3}{\rm{log}}N})$.
AB - Global navigation satellite system (GNSS) based passive radar has been applied in the detection of moving targets. However, the low signal power of GNSS on the earth's surface limits the application of this technology for the long-range or low-observable target detection. Increasing the observation time can effectively improve the detection capability. But the target motion involves the range cell migration (RCM) and the Doppler frequency migration (DFM) over the long observation time, which results in the integration gain loss and lower the detection performance. This article proposes a new hybrid coherent and noncoherent integration method named the keystone transform and Lv's distribution. The proposed method not only compensate the RCM and the DFM but also provide coherent and noncoherent integration gains to increase the signal-to-noise ratio. The simulated results and the field trial results demonstrate that the detection performance of the proposed method is superior to the other two known moving target detection methods. And the analysis of the computational complexity shows that the proposed method and the other two methods are in the same order of ${\mathrm O}({{N^3}{\rm{log}}N})$.
KW - Global navigation satellite system (GNSS) based passive radar
KW - keystone transform (KT)
KW - long-time hybrid integration
KW - Lv's distribution
UR - http://www.scopus.com/inward/record.url?scp=85098771854&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2020.3037200
DO - 10.1109/JSTARS.2020.3037200
M3 - Journal article
AN - SCOPUS:85098771854
SN - 1939-1404
VL - 14
SP - 1184
EP - 1193
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
M1 - 9254092
ER -