Sparse, Compressed, and Distributed Array Processing for High-Precision Sensing
Abstract
Modern radar systems rely on antenna arrays to perform target direction-of-arrival (DOA) estimation, beam steering, adaptive beamforming, and target localization, tracking, imaging, classification, and recognition. Array processing also plays an important role in electronic countermeasure (ECM) and electronic counter countermeasure (ECCM) for the emission, localization, and rejection of jamming signals. The increasing needs for multi-functional, high-resolution, and resilient radar sensing capability demand for large antenna array aperture, wide signal bandwidth, and rapid system reconfigurability under stringent size, weight, power, and cost (SWaP-C) constraints. Utilizing the latest advances in array processing, compressive sensing, information fusion, machine learning, and distributed computing, the proposed work develops novel sparse, compressed, and distributed array signal processing techniques to enable effective design and utilization of sparse array, resource-efficient waveform, low-complexity signal acquisition, and distributed network sensing. The proposed work emphasizes unique issues and characteristics that set radar operations apart from other applications.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310255
Entities
People
- Yimin D. Zhang
Organizations
- Air Force Office of Scientific Research
- Temple University
- United States Air Force