Continuous-Time Tracking Algorithms Involving Two-Time-Scale Markov Chains
This work is concerned with least-mean-squares (LMS) algorithms in continuous time for tracking a time-varying parameter process. A distinctive feature is that the true parameter process is changing at a fast pace driven by a finite-state Markov chain. The states of the Markov chain are divisible into a number of groups. Within each group, the transitions take place rapidly; among different groups, the transitions are infrequent. Introducing a small parameter into the generator of the Markov...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Signal Processing|
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