Acquisition and Tracking algorithms for Phased Array Antenna based systems

With the advent of an increasing number of high-throughput telecommunication satellite constellations in Low Earth Orbit (LEO), tracking performance requirements for User Terminals (UTs) have become more stringent. A way to efficiently perform the Acquisition (ACQ) and Tracking (TRK) of a fast-moving LEO satellite along its trajectory is needed. Phased Array Antennas (PAA) are particularly suited for this type of application since their radiation patterns can be rapidly changed and their main lobe quickly steered without any need of mechanical components. In particular, Active PAAs, possessing an individual transceiver module per each array element, can extract the information of all individual array elements. This information can later be processed by algorithms to get the Directions of Arrival (DOAs) of the satellite signals arriving at the array. Namely, subspace-based DOA estimation methods are commonly implemented in systems employing Active PAAs, since they make full use of the detailed array information available. The aim of this work is to determine which subspace-based DOA estimation algorithms for UTs based on Active PAA technology work best for ACQ and TRK scenarios involving LEO satellite constellations. An extensive bibliographic study of array data modelling, subspace-based DOA estimation methods, and subspace tracking algorithms was carried out. The most promising methods were implemented in MATLAB. Furthermore, a full ACQ plus TRK simulation system was coded and used to test application scenarios with real satellite constellation data to conclude which algorithms yielded the best real-time performance. From the results of several worst-case scenario simulations, it was concluded that satellite DOA estimations were the most accurate when using the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method for both ACQ and TRK alongside the Fast Data Projection Method (FDPM) algorithm for updating the spatial covariance matrix