Efficient dual-scale generalized Radon-Fourier transform detector family for long time coherent integration
<|reference_start|>Efficient dual-scale generalized Radon-Fourier transform detector family for long time coherent integration: Long Time Coherent Integration (LTCI) aims to accumulate target energy through long time integration, which is an effective method for the detection of a weak target. However, for a moving target, defocusing can occur due to range migration (RM) and Doppler frequency migration (DFM). To address this issue, RM and DFM corrections are required in order to achieve a well-focused image for the subsequent detection. Since RM and DFM are induced by the same motion parameters, existing approaches such as the generalized Radon-Fourier transform (GRFT) or the keystone transform (KT)-matching filter process (MFP) adopt the same search space for the motion parameters in order to eliminate both effects, thus leading to large redundancy in computation. To this end, this paper first proposes a dual-scale decomposition of the target motion parameters, consisting of well designed coarse and fine motion parameters. Then, utilizing this decomposition, the joint correction of the RM and DFM effects is decoupled into a cascade procedure, first RM correction on the coarse search space and then DFM correction on the fine search spaces. As such, step size of the search space can be tailored to RM and DFM corrections, respectively, thus avoiding large redundant computation effectively. The resulting algorithms are called dual-scale GRFT (DS-GRFT) or dual-scale GRFT (DS-KTMFP) which provide comparable performance while achieving significant improvement in computational efficiency compared to standard GRFT (KT-MFP). Simulation experiments verify their effectiveness and efficiency.<|reference_end|>
@article{li2024efficient,
    title={Efficient dual-scale generalized Radon-Fourier transform detector family
  for long time coherent integration},
    author={Suqi Li, Yihan Wang, Bailu Wang, Giorgio Battistelli, Luigi Chisci,
  Guolong Cui},
    journal={arXiv preprint arXiv:2403.06788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06788},
    primaryClass={eess.SY cs.SY}
}