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TMO Progress Report 42-141 May 15, 2000 Computationally Intelligent Array Feed Tracking
 

Summary: TMO Progress Report 42-141 May 15, 2000
Computationally Intelligent Array Feed Tracking
Algorithms for Large DSN Antennas
R. Mukai,1
V. Vilnrotter,1
P. Arabshahi,1
and V. Jamnejad2
This article describes computationally intelligent neural-network and least-
squares tracking algorithms for fine pointing NASA's 70-m Deep Space Network
(DSN) antennas using the seven-channel Ka-band (32-GHz) array feed compensa-
tion system (AFCS). These algorithms process normalized inputs from the seven
horns of the array in parallel and, hence, are less sensitive to variations in signal
power and require much less time per pointing estimate than do conventional serial
processing techniques (CONSCAN) currently employed by the DSN. An additional
advantage of the parallel measurement technique described here is that mechanical
scanning of the antenna is not required. It is shown that under nominal conditions a
radial basis function (RBF) neural network trained with data from the seven array
feed channels can point the antenna with less than 0.3-mdeg rms error, achieving
significantly better pointing accuracy than the 0.8-mdeg requirement often quoted
as a benchmark (corresponding to 0.1-dB signal-to-noise ratio (SNR) loss on 70-m

  

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

 

Collections: Engineering