An Improved Multi-Frame Coherent Integration Algorithm for Heterogeneous Radar

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (16), p.4026
• Main Authors: Liu, Yiheng, Zhang, Hua, Wang, Xuemei, Dong, Qinghai, Lyu, Xiaode
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidths; Carrier frequencies; Coherence; Coherent radar; Doppler effect; Entropy; Fast Fourier transformations; Fourier transforms; heterogeneous radar; improved Keystone transform; Integration; Methods; multi-frame coherent integration; Pulse repetition frequency; Radar; Radar detection; Remote sensing; Resampling; Rescaling; Robustness (mathematics); Signal processing; Signal to noise ratio; Target detection; weak-target detection
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15164026

This paper proposes an improved multi-frame coherent integration algorithm to improve the detection performance of weak targets in heterogeneous radar. In the detection of weak targets, integration within a single frame may fail to provide sufficient signal-to-noise ratio (SNR) gain. In this case, multi-frame coherent integration is an effective solution. However, radar parameters may be different across frames (i.e., heterogeneous radar) in some practical situations, leading to a mismatch of Doppler frequencies and the fixed phases, which poses difficulties to multi-frame coherent integration. To calibrate the ranges and Doppler frequencies of heterogenous multi-frame echoes, this paper firstly employs an improved Keystone Transform (KT). Compared to conventional KT, the improved KT aligns inter-frame carrier frequencies by applying varying degrees of slow-time rescaling based on the carrier frequencies of each frame, and aligns inter-frame Pulse Repetition Frequencies (PRF) through a unified global slow-time resampling. Secondly, this paper derives the explicit expressions of the fixed-phase terms and adopts a method based on fractional range bins, thus achieving explicit compensation for mismatched phases. Finally, heterogenous multi-frame coherent integration is achieved through slow-time fast Fourier transform. The effectiveness of the proposed algorithm is validated by simulation analyses. Compared to existing entropy-based methods, the proposed algorithm demonstrates higher robustness and lower computational complexity, making it more effective in detecting weak targets under low SNR conditions.