Communications Under Extreme RF Spectrum Conditions (CommEx)
Abstract
The Communications Under Extreme RF Spectrum Conditions (CommEx) program developed signal detection and reasoning technology that allows radios to recognize interference and jamming attacks and then adapt to maintain communications, even in the presence of cognitive jammer attacks and dynamic interference of multiple cognitive network interactions. As part of Low Probability of Detection/Anti-Jam (LPD/AJ) technologies efforts in the Project, the program developed models of adversary, commercial, and friendly cognitive radios and implemented those models to assess, in real time, the current and future dynamics of the communications network. Core technologies for operation in highly dynamic and/or high jamming to signal environments were developed to include: automated jamming waveform forensics; local environment assessment (time, space, frequency, polarization); technologies for addressing known attack strategies and interference properties; and antenna, signal processing, modulation, and network optimization technologies. Based on predictions of the level of communication success compared to mission communication requirements, the cognitive radio chooses waveform selections/configurations that best achieve mission objectives. The cognitive radio includes the capability to analyze and select optimum frequency, waveform, and network configurations during all aspects of a mission. The design effort led to new radio communication architectures, more robust radio communication networking, and better understanding of optimization amongst interference avoidance and interference suppression strategies. This program also sought to enable communication between dispersed and distributed emitters and receivers to provide a multiplier in capacity for both locating emitters and assessing effectiveness of an electronic attack. Technologies developed in this program transitioned to the Navy and Air Force.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2016
- Source ID
- 44eb1eb6f31ea2caa3701306bb6ab371