2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar

• Main Authors: Hong-Cheng Zeng, Jie Chen, Peng-Bo Wang, Wei Yang, Wei Liu
• Format: Article
• Language: English
• Published: MDPI AG 2018-07-01
• Series: Remote Sensing
• Subjects: Two-dimensional (2-D) coherent integration; Radon Fourier Transform; modified matched filter; aircraft detection; GNSS-based passive radar
• Online Access: http://www.mdpi.com/2072-4292/10/7/1164

Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration (RCM) and Doppler frequency migration (DFM) will seriously affect the coherent processing of azimuth signals, and the traditional range match filter will also be mismatched due to the Doppler-intolerant characteristic of GNSS signals. Accordingly, the energy loss of 2-dimensional (2-D) coherent processing is inevitable in traditional methods. In this paper, a novel 2-D coherent integration processing and algorithm for aircraft target detection is proposed. For azimuth processing, a modified Radon Fourier Transform (RFT) with range-walk removal and Doppler rate estimation is performed. In respect to range compression, a modified matched filter with a shifting Doppler is applied. As a result, the signal will be accurately focused in the range-Doppler domain, and a sufficiently high SNR can be obtained for aircraft detection with a moving target detector. Numerical simulations demonstrate that the range-Doppler parameters of an aircraft target can be obtained, and the position and velocity of the aircraft can be estimated accurately by multiple observation geometries due to abundant GNSS resources. The experimental results also illustrate that the blind Doppler sidelobe is suppressed effectively and the proposed algorithm has a good performance even in the presence of Doppler ambiguity.