Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size
Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption. This sets the framework for several important filtering problems that are not modeled by a "...
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| Published in | IEEE signal processing letters Vol. 5; no. 5; pp. 111 - 114 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.05.1998
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1070-9908 1558-2361 |
| DOI | 10.1109/97.668945 |
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| Abstract | Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption. This sets the framework for several important filtering problems that are not modeled by a "true" unknown system with bounded noise, such as adaptive equalization, to exploit the unique advantages of SMI algorithms. A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm. Interesting properties of the algorithm, such as asymptotic cessation of updates and monotonically non-increasing parameter error, are established. Simulations show significant performance improvement in varied environments with a greatly reduced number of updates. |
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| AbstractList | Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption. This sets the framework for several important filtering problems that are not modeled by a "true" unknown system with bounded noise, such as adaptive equalization, to exploit the unique advantages of SMI algorithms. A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm. Interesting properties of the algorithm, such as asymptotic cessation of updates and monotonically non-increasing parameter error, are established. Simulations show significant performance improvement in varied environments with a greatly reduced number of updates. Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption. This sets the framework for several important filtering problems that are not modeled by a "true" unknown system with bounded noise, such as adaptive equalization, to exploit the unique advantages of SMI algorithms. A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm. Interesting properties of the algorithm, such as asymptotic cessation of updates and monotonically non-increasing parameter error, are established. Simulations show significant performance improvement in varied environments with a greatly reduced number of updates |
| Author | Gollamudi, S. Kapoor, S. Yih-Fang Huang Nagaraj, S. |
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| Cites_doi | 10.1016/0378-4754(90)90002-Z 10.1016/0005-1098(82)90110-8 10.1002/acs.4480080105 10.1109/5.58323 10.1109/5.257681 10.1109/78.709523 10.1109/78.205719 10.1109/ISCAS.1996.540340 10.1109/TIT.1987.1057307 |
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| SubjectTerms | Adaptive filters Additive noise Convergence Filtering algorithms Filtering theory Least squares approximation Resonance light scattering Signal processing algorithms System identification Time sharing computer systems |
| Title | Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size |
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