The Spectrum Analysis Solution (SAS) System: Theoretical Analysis, Hardware Design and Implementation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the Spectrum Analysis Solution (SAS) for cognitive radar, enabling adaptive spectrum sharing. It details algorithms for optimizing radar transmissions in dynamic radio frequency environments.
Area Of Science
- Electrical Engineering
- Radar Systems
- Signal Processing
Background
- Cognitive and spectrally adaptive radar systems require advanced spectrum sensing capabilities.
- Efficient spectrum sharing is crucial for next-generation radar applications.
Purpose Of The Study
- To introduce the Spectrum Analysis Solution (SAS), a multichannel super-heterodyne signal analyzer for multi-purpose spectrum sensing.
- To develop and evaluate spectrum sensing algorithms for spectrum sharing (SS) radar.
Main Methods
- The SAS features a wideband monitoring channel and eight narrowband channels operating from UHF to S-band.
- Key spectrum characterization metrics include Bandwidth (BW), average total power, percent occupancy (PO), signal-to-interference-plus-noise ratio (SINR), and power spectral entropy (PSE).
- Three optimal sub-band (OSB) algorithms (SS-MO, SS-BFE, SS-MO-BFE) were developed and evaluated for SS radar transmission.
Main Results
- The SAS facilitates dynamic tuning of narrowband channels to specific spectrum areas of interest.
- The evaluated metrics provide a comprehensive characterization of the radio frequency spectrum.
- The presented OSB algorithms demonstrate different approaches to identifying optimal transmission sub-bands.
Conclusions
- The SAS is a valuable tool for supporting spectrally adaptive and cognitive radar.
- Spectrum sensing algorithms are essential for enabling efficient spectrum sharing in radar systems.
- The developed OSB algorithms offer viable solutions for optimizing radar transmissions in shared spectrum environments.
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