Identification of Abnormal Noise Temperature Patterns in Deep Space Network Antennas Using Fuzzy Logic

The communication between NASA space mission operations teams and their respective spacecraft in outer space is accomplished via the Deep Space Network (DSN). To ensure proper operations in returning telemetry data to mission operations, sending commands to spacecraft and providing radiometric data for navigation purposes, the DSN equipment generates a large set of self-monitor data. System noise temperature (SNT) and link margin are two of the key metrics of system performance for telemetry data return. The ability to detect the signal is affected by the SNT; the lower the noise, the better chance the system can detect the signal. Thus, there is a strong interest in monitoring and classifying the SNT. However, there are many causes of SNT behavior patterns that are still not known and difficult to classify by simple logic. There is a benefit to use a more sophisticated pattern-recognition method to detect and classify abnormal SNT behaviors.