Affiliation:
1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Abstract
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.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference46 articles.
1. Ambiguity Function Analysis of Random Frequency and PRI Agile Signals;Long;IEEE Trans. Aerosp. Electron. Syst.,2021
2. Review on Frequency Agile Radar Seeker;Quan;Aero Weapon.,2021
3. Pace, P.E. (2008). Detecting and Classifying Low Probability of Intercept Radar, Artech. [2nd ed.].
4. Overview of radar waveform diversity;Blunt;IEEE Aerosp. Electron. Syst. Mag.,2016
5. Multi-Timeslot Wide-Gap Frequency-Hopping RFPA Signal and Its Sidelobe Suppression;Long;IEEE Trans. Aerosp. Electron. Syst.,2023