TY - GEN
T1 - Multi-stage adaptive signal processing algorithms
AU - Kozat, S. S.
AU - Singer, A. C.
PY - 2000
Y1 - 2000
N2 - In this paper, we explore the use of multi-stage adaptation algorithms for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. These algorithms are "multi-stage" in that they comprise multiple adaptive filtering algorithms that operate in parallel on the observation sequence, and adaptively combine the outputs of this first stage to form an overall signal estimate. Several examples of this class of algorithms are demonstrated and analyzed in both a deterministic and stochastic context with respect to their convergence and mean squared error. The first example of this class, a "universal" linear predictor, was recently introduced and shown to asymptotically achieve the performance of the best linear predictor for each sequence, (up to some maximal order). Two new algorithms have been developed that generalize this universal linear predictor, and explore the use of the LMS algorithm in each stage of adaptation. Each of these algorithms are compared through theoretical analysis of their behavior.
AB - In this paper, we explore the use of multi-stage adaptation algorithms for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. These algorithms are "multi-stage" in that they comprise multiple adaptive filtering algorithms that operate in parallel on the observation sequence, and adaptively combine the outputs of this first stage to form an overall signal estimate. Several examples of this class of algorithms are demonstrated and analyzed in both a deterministic and stochastic context with respect to their convergence and mean squared error. The first example of this class, a "universal" linear predictor, was recently introduced and shown to asymptotically achieve the performance of the best linear predictor for each sequence, (up to some maximal order). Two new algorithms have been developed that generalize this universal linear predictor, and explore the use of the LMS algorithm in each stage of adaptation. Each of these algorithms are compared through theoretical analysis of their behavior.
KW - Adaptive filters
KW - Adaptive signal processing
KW - Algorithm design and analysis
KW - Convergence
KW - Data compression
KW - Filtering algorithms
KW - Least squares approximation
KW - Machine learning algorithms
KW - Resonance light scattering
KW - Signal processing algorithms
UR - http://www.scopus.com/inward/record.url?scp=84949543122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949543122&partnerID=8YFLogxK
U2 - 10.1109/SAM.2000.878034
DO - 10.1109/SAM.2000.878034
M3 - Conference contribution
AN - SCOPUS:84949543122
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
SP - 380
EP - 384
BT - Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000
PB - IEEE Computer Society
T2 - IEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2000
Y2 - 16 March 2000 through 17 March 2000
ER -