Proportionate-type NLMS algorithms based on maximization of the joint conditional PDF for the weight deviation vector
In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint conditional probability that the next-step coefficient estimates reach their optimal values. We compare and sho...
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| Vydáno v: | 2010 IEEE International Conference on Acoustics, Speech and Signal Processing s. 3738 - 3741 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.03.2010
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| Témata: | |
| ISBN: | 9781424442959, 1424442958 |
| ISSN: | 1520-6149 |
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| Abstract | In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the joint maximum conditional probability density function (PDF) one-step algorithm is superior to the proportionate normalized least mean square algorithm when operating on a sparse impulse response. We also show that the new algorithm is superior to a previously introduced algorithm which assumed that the conditional PDF could be represented by the product of the marginal conditional PDFs, i.e., that the weight deviations are mutually conditionally independent. |
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| AbstractList | In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the joint maximum conditional probability density function (PDF) one-step algorithm is superior to the proportionate normalized least mean square algorithm when operating on a sparse impulse response. We also show that the new algorithm is superior to a previously introduced algorithm which assumed that the conditional PDF could be represented by the product of the marginal conditional PDFs, i.e., that the weight deviations are mutually conditionally independent. |
| Author | Doroslovački, Miloš I Wagner, Kevin T |
| Author_xml | – sequence: 1 givenname: Kevin T surname: Wagner fullname: Wagner, Kevin T organization: Radar Div., Naval Res. Lab., Washington, DC, USA – sequence: 2 givenname: Miloš I surname: Doroslovački fullname: Doroslovački, Miloš I organization: Dept. of Electr. & Comput. Eng., George Washington Univ., Washington, DC, USA |
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| Snippet | In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner... |
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| SubjectTerms | Adaptive algorithm Adaptive filtering Adaptive filters Algorithm design and analysis Convergence Filtering algorithms Least mean square algorithms Least squares approximation Noise measurement Probability density function proportionate-type normalized least mean square (PtNLMS) algorithm Radar sparse impulse response |
| Title | Proportionate-type NLMS algorithms based on maximization of the joint conditional PDF for the weight deviation vector |
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