Multi-Timeslot Wide-Gap Frequency-Hopping RFPA Signal and Its Sidelobe Suppression

Long, Xingwang; Wu, Wenhao; Li, Kun; Wang, Ju; Wu, Siliang

doi:10.1109/taes.2022.3188594

Cited by 4 publications

(1 citation statement)

References 62 publications

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“…Furthermore, this radar avoids fixed ambiguous range and velocity measurements [4]. Consequently, in recent years, RFPA radar has emerged in the radar field as a prominent research topic [5].…”

Section: Introductionmentioning

confidence: 99%

An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar

Wang,

Shan,

Duan

et al. 2024

Remote Sensing

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In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar.

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Section: Introductionmentioning

confidence: 99%