Interpolation Based Maximum-Likelihood (ML) Detection of Asynchronous Servo Repeatable Run Out (RRO) Data

This paper demonstrates the use of asynchronous maximum-likelihood (ML) detection to improve the detection performance of coded servo repeatable run out data. A suboptimal ML algorithm based on an absolute value metric is presented. This paper compares the performance of the ML algorithms with an as...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2006-10, Vol.42 (10), p.2585-2587
Main Authors: Aziz, P.M., Annampedu, V.
Format: Article
Language:English
Subjects:
Algorithms
Asynchronous
coding
Computer simulation
Cross-disciplinary physics: materials science
rheology
detection
Detection algorithms
Digital filters
Effectiveness
Error detection
Exact sciences and technology
Finite impulse response filter
Gain
Interpolation
Magnetism
Materials science
Maximum likelihood detection
Other topics in materials science
Performance enhancement
Performance gain
Performance loss
Permissible error
Physics
Rail to rail outputs
repeatable run out (RRO)
servo
Servomechanisms
Timing
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Description
Summary:This paper demonstrates the use of asynchronous maximum-likelihood (ML) detection to improve the detection performance of coded servo repeatable run out data. A suboptimal ML algorithm based on an absolute value metric is presented. This paper compares the performance of the ML algorithms with an asynchronous bit by bit (BBB) detection algorithm. Simulation results quantify the performance improvement over the BBB algorithm. A gain correction algorithm is also proposed to allow the asynchronous ML detection performance to be less sensitive to gain errors. The efficacy of the gain correction algorithm is quantified via simulations
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2006.878640