Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 5, SEPTEMBER 2002 1149 Adaptive Acquisition and Tracking for Deep Space
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 5, SEPTEMBER 2002 1149
Adaptive Acquisition and Tracking for Deep Space
Array Feed Antennas
Ryan Mukai, Victor A. Vilnrotter, Senior Member, IEEE, Payman Arabshahi, Member, IEEE, and
Vahraz Jamnejad, Senior Member, IEEE
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 0.10.5 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

  

Source: Arabshahi, Payman - Applied Physics Laboratory & Department of Electrical Engineering, University of Washington at Seattle

 

Collections: Engineering