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Review of Multisensor Processing and Parallel Mapping
Fast Subspace Tracking Algorithms
4 other sections not shown
algorithm graph algorithm HHT app comm array processing automated mapping automating the mapping beamforming bearing estimation cells CG-FST coarse grain mapping column communication complexity compute condition estimation convergence correlation matrix cost functions deflation dependence graph double precision DSP algorithms edges eigenvalues Fast Subspace Tracking Furthermore global sum Grain FST grain partitioning Householder transformation hyperbolic Householder hypercube Idle cost function IEEE Transactions Intel iPSC/860 Intel Paragon iWarp linear speedup Minimax multiprocessors node processors noise subspace Number of Processors operations optimization orthonormal parameters performance plane rotations preserve the signal processor q QR algorithm QR decomposition QR factorization rank increase rank revealing real-time recursive least squares refinement step RO-FST sensor array processing shown in Figure signal eigenstructure signal flow graph signal processing signal subspace simulated annealing singular value decomposition singular values snapshot stage 4A subgraph subspace tracking algorithms target machine tasks techniques tmult TQR-SVD upper triangular wavefronts