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Show all documents Reduced Rank Adaptive Filtering Using Krylov Subspace The frequent problem which arises when designing an adaptive filtering system is that large observation size, and therefore, large filter length, means inevitably high compu- tational cost, slow convergence, and poor tracking perfor- mance. However, this situation corresponds to many im- portant practical applications such as high data rate direct- sequence code division multiple access DS-CDMA sys- tems, radar or global positioning system GPS array pro- cessing. Reduced-rank adaptive filters provide a way out of this dilemma [3, 5]. The basic idea behind the rank reduction is to project the observation onto a lower-dimensional sub- space usually defined by a set of basis vectors. The adaptation is then performed within this subspace with a low-order filter resulting in substantial computational savings, better conver- gence, and tracking characteristics.
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You should be able to do this using stdfilt and conv2. You have set up the parameters of the adaptive filters and are now ready to filter the noisy signal. Or, the program could use isa- Matlab functions to check if the names are already part of the namspace. By running the example code provided you can demonstrate one process to identify an unknown FIR filter. This System object computes the filtered output and the filter error for a given input and desired signal.
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This unified survey of the theory of adaptive filtering, prediction, and control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems. In keeping with the importance of computers to practical applications, the authors emphasize discrete-time systems.
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