发明名称 |
METHODS AND APPARATUS FOR IMPROVED THRESHOLD ADAPTATION FOR A EUCLIDEAN DETECTOR |
摘要 |
Improved threshold adaptation is provided for a predefined pattern in data. A detection threshold employed by a Euclidean detector to detect a pattern (such as a Servo Address Mark) in data is adapted by determining a minimum Euclidean distance metric Dp before the pattern is declared in a given portion of the data; determining a Euclidean distance metric Ds at a time when the pattern is found in the given portion, wherein at least one of the patterns is found using a Hamming detector; determining, for a plurality of the portions, a minimum Dp value, Dpmin, that is a substantial minimum Dp value for the plurality of portions and a maximum Ds value, Dsmax, that is a substantial maximum Ds value for the plurality of portions; and determining the detection threshold based on the minimum Dp value, Dpmin, and the maximum Ds value, Dsmax. The detection threshold is optionally based on a configurable margin gain value. |
申请公布号 |
US2015015986(A1) |
申请公布日期 |
2015.01.15 |
申请号 |
US201414323286 |
申请日期 |
2014.07.03 |
申请人 |
LSI Corporation |
发明人 |
Guo Linjiang;Li Zhi Bin;Zhao Yao;Qin Dahua |
分类号 |
G11B5/596 |
主分类号 |
G11B5/596 |
代理机构 |
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代理人 |
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主权项 |
1. A method for adapting a detection threshold employed by a Euclidean detector to detect a pattern in data, comprising:
determining a substantially minimum Euclidean distance metric Dp before said pattern is declared in a given portion of said data; determining a Euclidean distance metric Ds at a time when said pattern is found in said given portion, wherein at least one of said patterns is found using a Hamming detector; determining, for a plurality of said portions, a minimum Dp value, Dpmin, that is a substantial minimum Dp value for said plurality of portions and a maximum Ds value, Dsmax, that is a substantial maximum Ds value for said plurality of portions; and determining said detection threshold based on said minimum Dp value, Dpmin, and said maximum Ds value, Dsmax. |
地址 |
San Jose CA US |