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Related Experiment Video

Updated: Jul 7, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Adaptive acquisition and tracking for deep space array feed antennas.

R Mukai¹, V A Vilnrotter, P Arabshahi

¹Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

NASA

Area of Science:

Radio astronomy
Antenna control systems
Machine learning applications

Background:

NASA's 70-m Deep Space Network (DSN) antennas require precise pointing for effective communication.
Traditional pointing methods face challenges with antenna aging and environmental factors.

Purpose of the Study:

To evaluate the effectiveness of radial basis function (RBF) networks and least squares algorithms for precise antenna pointing.
To improve pointing accuracy beyond the benchmark for Ka-band communications.

Main Methods:

Utilized radial basis function (RBF) networks trained with the orthogonal least squares algorithm.
Implemented an array feed compensation system for enhanced accuracy.
Applied continuous adaptation strategies to the RBF network for real-time compensation.

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Main Results:

Achieved root mean square (rms) pointing errors of 0.1-0.5 millidegrees (mdeg).
Demonstrated pointing accuracy significantly better than the 0.8 mdeg Ka-band benchmark.
Showcased dramatic system performance improvements through continuous adaptation.

Conclusions:

RBF networks combined with least squares algorithms offer superior pointing accuracy for deep space antennas.
Continuous adaptation strategies effectively mitigate time-varying structural changes in large antennas.
The developed system shows significant promise for future deep space communication.