A Hybrid Integration Method for Moving Target Detection With GNSS-Based Passive Radar

Abstract

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 O(N3logN).

Keywords

• Global navigation satellite system (GNSS) based passive radar
• keystone transform (KT)
• long-time hybrid integration
• Lv's distribution