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A Passive Synthetic Aperture Localization Method Based on Sparse Sampling Reconstruction
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AI
DOI：10.1109/LGRS.2025.3596123.png)
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Abstract
En 中文
In passive localization, the synthetic aperture positioning (SAP) method can achieve high precision and high-resolution positioning. However, existing research neglects the issue of target adaptability. For radar emitter targets, receivers can only periodically capture signals when the emitter’s beam scans toward the receiving antenna, resulting in spectral aliasing of the received signals. This leads to multiple false targets in localization images and reduced accuracy. This study employs the fractional Fourier transform (FrFT) integrated with compressed sensing for continuous signal reconstruction, aiming to eliminate spurious targets and enhance positioning accuracy. Initially, spectral aliasing is suppressed through FrFT, capitalizing on the approximately linear frequency-modulated (LFM) characteristics inherent in Doppler signals. Subsequently, a continuous signal is reconstructed using compressed sensing with FrFT basis vectors forming the sensing matrix. Finally, the SAP method is implemented to achieve precise positioning. The effectiveness of the proposed method has been validated through simulations and uncrewed aerial vehicle (UAV) experiments, demonstrating that it significantly enhances the adaptability of SAP methods to radar emitter targets.
Keywords:
Compressed sensing
emitter localization
fractional Fourier transform (FrFT)
synthetic aperture positioning (SAP)
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K