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Summary: TMO Progress Report 42-143 November 15, 2000
Tracking Performance of Adaptive Array Feed
Algorithms for 70-Meter DSN Antennas
R. Mukai,1
V. Vilnrotter,1
and P. Arabshahi1
This article describes computationally intelligent neural-network and least-
squares algorithms for precise pointing of NASA's 70-meter Deep Space Network
(DSN) antennas using the seven-channel Ka-band (32-GHz) array feed compen-
sation system (AFCS). These algorithms process normalized data from the seven
horns of the array in parallel and thus are more robust and more accurate than in-
herently serial conventional processing techniques (CONSCAN) currently used by
the DSN. A previous article discussed the use of new algorithms for acquisition and
estimation of relatively large pointing errors [1] while addressing only briefly the is-
sue of fine tracking near the source. However, neural networks designed specifically
for fine-tracking operations yield better fine-pointing performance and significantly
lower complexity than those designed for coarse acquisition, and large reductions
in complexity may be achieved by using a low-complexity fine-pointing neural net-
work in conjunction with a very simple coarse-acquisition algorithm. In addition to
complexity reduction, we also demonstrate the ability to update parameters of the
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