Detection and Mitigation Technologies of Radio Frequency Interference (RFI) for Remote Sensing and Communications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: 30 November 2026
Special Issue Editor
Special Issue Information
Dear Colleagues,
The spectrum has become increasingly crowded for growing satellite services such as Global Navigation Satellite Systems (GNSSs) and 5G/6G communications. Intentional and unintentional radio frequency interference (RFI) from adjacent-band and out-of-band emissions poses a great challenge, threatening critical services such as aviation, telecommunications, transportation, and national security. RFI can also degrade the quality of remote sensing data from the L-band to the G-band, affecting weather forecasts, climate monitoring, and other scientific applications that rely on sensitive, clean measurements. Technological innovations in software and hardware play a critical role in detecting and mitigating complex RFI issues. In this Special Issue, recent progress and sensor development in RFI detection and mitigation technologies are welcome, including advanced signal processing with artificial intelligence (AI) and machine learning (ML), software-defined radios (SDRs), distributed and networked sensors, adaptive/tunable filtering, and dynamic spectrum sharing.
The problem of RFI will continue to grow with increased demand for wireless spectrum from new technologies. It will remain a major threat to remote sensing due to the proliferation of electronics and satellites.
This Special Issue will present the latest findings of RFI technology developments on the following topics:
- Advanced Detection Algorithms;
- Real-time Mitigation;
- Sensor and System Design;
- Dynamic Spectrum Management;
- Characterization and Modeling of RFI.
Dr. Dong L. Wu
Guest Editor
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Keywords
- advanced detection algorithms
- real-time mitigation
- sensor and system design
- dynamic spectrum management
- characterization and modeling of RFI
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