Timothy T. Pham

Enhancing telemetry and navigation performance with full spectrum arraying

A new arraying capability is to be introduced into the NASA Deep Space Network (DSN). It is a second-generation system employing the full spectrum combining technique first deployed to support the Galileo mission in 1996. The new arraying capability offers multimission support with real-time combining at higher data rates. In addition, for the first time in DSN history, it is also possible to array ranging signals to enhance navigation performance. With the enhanced signal-to-noise ratio (SNR) obtained from an effectively larger aperture, arraying enables support for missions whose signal level falls below the tracking threshold of a single antenna. Alternatively, it can also be used to increase the data return over that possible with a single antenna. In the extreme, the array deployment of four 34-meter antennas at Goldstone can substitute for the 70-meter antenna, the largest one in the network.

Large antenna array techniques for very low SNR channels

Various arraying techniques are studied focusing on the very low received signal SNR channel conditions commonly found in deep space communications applications. These include correlation-based blind approaches as well as a sub-space based superresolution approach. In addition to weak received signals, atmospheric turbulence and spatially correlated interference from nearby planets (and possibly quasars) creates additional channel impairment. It is demonstrated that the sub-space based MUSIC algorithm is a strong candidate for this application that can provide great angle separation accuracy and interference suppression capability. Adaptive beamforming techniques in combination with the MUSIC algorithm provide a flexible platform to combat channel impairment.

Characterizing DSN System Frequency Stability with Spacecraft Tracking Data

This paper describes a recent effort in characterizing frequency stability performance of the ground system in the National Aeronautics and Space Administration (NASA) Deep Space Network (DSN). Unlike the traditional approach where performance is obtained from special calibration sessions that are time consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This method significantly increases the amount of data available for analysis, roughly by two orders of magnitude; making itpossible to conduct trend analysis with reasonable confidence. To make it possible to process the continual stream of daily incoming data without much effort and to quickly understand the results, the processing needs to be automated and the data summarized at high level. Comparison of observed data against expected performance is also given.