Fast subspace tracking using coarse grain and fine grain parallelism

Subspace tracking is an integral part of many high resolution adaptive array methods. Unfortunately, the high computational complexity and non-parallel nature of traditional subspace tracking algorithms have deterred their use in real-time systems. We discuss parallel mappings of the fast subspace tracking algorithm. The serial complexity of this algorithm is already among the lowest {O(Nr) for N channels and an r dimensional subspace}. We show that even greater reductions in effective complexity can be achieved by mapping our algorithm onto multiple processors. Near linear speedup is obtained on machines spanning the range from fine grain systolic arrays to coarse grain commercially available MPPs