Name
Abstract | ||
|---|---|---|
The use of radial basis function (RBF) networks and least squares algorithms for acquisition and fine tracking of NASA's 70-m-deep space network antennas is described and evaluated. We demonstrate that such a network, trained using the computationally efficient orthogonal least squares algorithm and working in conjunction with an array feed compensation system, can point a 70-m-deep space antenna with root mean square (rms) errors of 0.1-0.5 millidegrees (mdeg) under a wide range of signal-to-noise ratios and antenna elevations. This pointing accuracy is significantly better than the 0.8 mdeg benchmark for communications at Ka-band frequencies (32 GHz). Continuous adaptation strategies for the RBF network were also implemented to compensate for antenna aging, thermal gradients, and other factors leading to time-varying changes in the antenna structure, resulting in dramatic improvements in system performance. The systems described here are currently in testing phases at NASA's Goldstone Deep Space Network (DSN) and were evaluated using Ka-band telemetry from the Cassini spacecraft. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TNN.2002.1031946 | Neural Networks, IEEE Transactions |
Keywords | Field | DocType |
adaptive acquisition,70-m-deep space antenna,array feed compensation system,neural networks,radial basis function rbf networks.,antenna aging,ka-band telemetry,array feed,deep space array feed,rbf network,antenna elevation,70-m-deep space network antenna,antennas,ka-band frequency,deep space network,antenna structure,nasa,orthogonal least-squares,squares algorithm,index terms—adaptive,thermal gradient,neural network,computer networks,dsn,system performance,radial basis function,benchmark testing,frequency,root mean square,least square,signal to noise ratio,indexing terms | Radial basis function,Control theory,Computer science,Telemetry,Electronic engineering,Artificial intelligence,Space Network,NASA Deep Space Network,Benchmark (computing),Spacecraft,Pattern recognition,Signal-to-noise ratio,Root mean square | Journal |
Volume | Issue | ISSN |
13 | 5 | 1045-9227 |
Citations | PageRank | References |
5 | 0.46 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mukai, R. | 1 | 5 | 0.46 |
| V. A. Vilnrotter | 2 | 32 | 2.75 |
| Arabshahi, P. | 3 | 72 | 5.02 |
| Jamnejad, V. | 4 | 5 | 0.46 |