The objective of the activity is to develop RF situational awareness functions for telemetry and telecommand receivers. Algorithms and a test-bed shall be developed and experimentally tested in laboratory conditions based on several representative operational cases.Targeted Improvements: Enabling interference and signal identification and classification using telecommand and telemetry signals. Description:Information obtained from RF (Radio Frequency) data can be used to enhance the situational awareness of a Telecommunications satellite, offering information onboard that cannot be obtained via other sensors. Examples of such information include unintentional communication signals (from other spacecraft), intentional jamming signals, and unexpected interference from EM (ElectroMagnetic) sources within the satellite itself (e.g., harmonics or intermodulation products from other RF emitters of the same satellite, or leakage from internal sources such as cabling, connectors, switches). This activity plans to add RF situational awareness as an additional function in TTC/TCR/PCC communications units. This can be achieved for instance by monitoring and processing an IF(Intermediate Frequency) signal within the bandwidth of the receiver chain of a transponder or transceiver based on software-defined radio (SDR) architectures. The IF raw samples at the ADC (Analogue-to-Digital Converter) can then be used to perform signal detection and classification (based on type of interference signal, type of modulation scheme, etc.) using an RF surveillance monitor that employs advanced signal processing that could be based on machine learning techniques. Machine learning techniques will be traded-off against classical techniques that use conventional algorithms for autonomous detection and classification of the main signal characteristics (modulation type, symbol rate, code rate), e.g., FFT-based parallel correlation, symbol signal-to-noise ratio estimation, parallel demodulation channels, etc.). Criteria for this trade-off will include the computational complexity, performance, cost and suitability for this user case.A breadboard will implement the selected RF situational awareness concept and, under a number of representative test cases, it will help evaluate the complexity and validate the performance of the RF surveillance monitor functions andalgorithms. In addition, an (updated) transponder/transceiver architecture will be proposed that shows how this concept can be integrated in current transponder/transceiver designs (covering mechanical, electrical, front-end, digital and interface aspects).Finally, a Test Bed that allows to monitor, control and test the RF situational awareness breadboard with a suite of representative test scenarios will also be implemented.

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