  The Deep Space Network is NASA's international array of antennas that support
interplanetary spacecraft missions. A track is a block of multi-dimensional
time series from the beginning to end of DSN communication with the target
spacecraft, containing thousands of monitor data items lasting several hours at
a frequency of 0.2-1Hz. Monitor data on each track reports on the performance
of specific spacecraft operations and the DSN itself. DSN is receiving signals
from 32 spacecraft across the solar system. DSN has pressure to reduce costs
while maintaining the quality of support for DSN mission users. DSN Link
Control Operators need to simultaneously monitor multiple tracks and identify
anomalies in real time. DSN has seen that as the number of missions increases,
the data that needs to be processed increases over time. In this project, we
look at the last 8 years of data for analysis. Any anomaly in the track
indicates a problem with either the spacecraft, DSN equipment, or weather
conditions. DSN operators typically write Discrepancy Reports for further
analysis. It is recognized that it would be quite helpful to identify 10
similar historical tracks out of the huge database to quickly find and match
anomalies. This tool has three functions: (1) identification of the top 10
similar historical tracks, (2) detection of anomalies compared to the reference
normal track, and (3) comparison of statistical differences between two given
tracks. The requirements for these features were confirmed by survey responses
from 21 DSN operators and engineers. The preliminary machine learning model has
shown promising performance (AUC=0.92). We plan to increase the number of data
sets and perform additional testing to improve performance further before its
planned integration into the track visualizer interface to assist DSN field
operators and engineers.
