Tracking Performance of Adaptive Array Feed Algorithms for 70-Meter DSN Antennas

This article describes computationally intelligent neural-network and leastsquares algorithms for precise pointing of NASA’s 70-meter Deep Space Network (DSN) antennas using the seven-channel Ka-band (32-GHz) array feed compensation 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 inherently 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 issue 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 network in conjunction with a very simple coarse-acquisition algorithm. In addition to complexity reduction, we also demonstrate the ability to update parameters of the radial basis function (RBF) network in near-real time in response to changes in the antenna, highlighting a useful characteristic of RBF neural networks for antennapointing control. The ability to update an RBF network in near-real time without complete restructuring or redesign of the network permits efficient operation even in the presence of frequent changes in the antenna surface.