Adaptive acquisition and tracking for deep space array feed antennas

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.