Dynamic magnetic information storage or retrieval – Automatic control of a recorder mechanism – Controlling the head
Reexamination Certificate
2002-10-23
2004-12-21
Tran, Sinh (Department: 2651)
Dynamic magnetic information storage or retrieval
Automatic control of a recorder mechanism
Controlling the head
C360S075000
Reexamination Certificate
active
06833972
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method for reproducing phase information signals which reconstruct original phase information data rows, based on the convoluted phase information data rows which are read by the head from information recording media in an information recording device.
2. Description of the Related Art
The current trend of magnetic recording and reproducing devices is moving toward greater recording densities in order to achieve miniaturization and high capacity. In the field of hard disk drives which are a typical magnetic recording and reproducing device, those with a surface recording density over 15 Gbits/in
2
(23.3 Mbits/mm
2
) have been already on the market, and those with a surface recording density of 40 Gbits/in
2
(62.0 Mbits/mm
2
) have been in rapid technical progress toward the actual practice. It is expected that track pitches will reach the level of submicron in the near future.
For such a narrow track, tracking servo technique of the head is important. In the current magnetic recording and reproducing devices, preformatted information signals such as tracking servo signals, address information signals, and reproduction clock signals are recorded on disk-shaped information recording media at fixed angle intervals. The head reproduces these signals at fixed intervals and accurately scans the target track, while pinpointing and modifying its location.
One of such tracking servo techniques is a phase servo control which employs the time information of regenerative signal pulses, that is, phase information as the location information of the head (refer to Japanese Laid-open Patent Application No. 10-83640 (Page 5, FIG. 6), Japanese Laid-open Patent Application No. 11-144218 (Pages 8-10, FIG. 7), for example).
When an information recording medium thus preformatted is installed in an information recording device, the phase patterns written in the information recording medium might be decentered with respect to the rotation center of the information recording medium. In this case, even when the head is at a standstill, the phase signals as the location information of the head picked up by the head are not constant, but fluctuate in the form of sine wave. This makes it impossible to perform proper tracking control.
SUMMARY OF THE INVENTION
It is necessary to compensate the influence of the decentered phase patterns in the information recording medium on the relative physical relation between the rotation center and the head.
Therefore, the main object of the present invention is to perform high precision compensation for decentering, even if the amount of decentering of the phase patterns is large in contrast to the track density.
Other objects, features, and advantages of the present invention will be clarified in the description below.
In order to achieve the above object, the present invention adopts the following approach to reproduce phase information signals in an information recording device.
An information recording medium previously stores phase patterns for track location detection. The phase patterns are repetition patterns in the direction of the radius of the medium, and this repetition of phase patterns is the cause of data convolution.
In a first step, phase patterns on the information recording medium are read by the head to acquire the data row of phase information. The data row of the phase information is a convoluted data row, which consists of the remainders obtained by dividing each data in the phase information data row to be reconstructed by a predetermined value (2&pgr;, for example).
In a second step, a differential data row is generated which is a data row of the differential values between adjacent data in the data row of the convoluted phase information read by the head. In a third step, a two-step differential data row is generated which is a data row of the differential values between adjacent data in the differential data row. In a fourth step, in the two-step differential data row, after cumulative coefficient is calculated depending on the section region to which each data belong, a reconstructing process is performed to solve the convolution process, thereby generating a reconstructed differential data row. Finally, in a fifth step an integrating process is applied to the reconstructed differential data row, thereby acquiring the reconstructed phase information data row.
The above configuration can be expressed as follows with symbols so as to make it easier to understand.
Suppose the data row of the convoluted phase information read in the first step is Pw(i) (i=1, 2, . . . N). Letting the phase information data row to be reconstructed in the end is Pu(i), the cumulative coefficient is k(i), and the predetermined value of convolution is &agr;, the following equation can be obtained:
Pu
(
i
)=
Pw
(
i
)+&agr;
k
(
i
) (1)
Convolution is accompanied by repetition. The repetition period is generally typified by 2&pgr;. Letting &agr;=2&pgr;,
Pu
(
i
)=
Pw
(
i
)+2
&pgr;×k
(
i
) (2)
Supposing the differential data row to be generated in the second step is dPw(i),
dPw
(
i
)=
Pw
(
i
)−
Pw
(
i
−1) (3)
Supposing the two-step differential data row to be generated in the third step is &dgr;Pw(i),
&dgr;
Pw
(
i
)=
dPw
(
i
)−
dPw
(
i
−1) (4)
In the fourth step, it is determined which section region each data of the two-step differential data row &dgr;Pw(i) belong to, thereby controlling the cumulative coefficient k(i) depending on the section region where each data belong. For example, when the data belongs to the first section region, it is decremented, when it belongs to the second section region, it is kept in the current value, and when it belongs to the third section region, it is incremented. Then, a reconstructing process is performed to solve the convolution process by using the found cumulative coefficient k(i). Supposing the reconstructed differential data row obtained here is dPu(i),
dPu
(
i
)=&dgr;
Pw
(
i
)+2
&pgr;×k
(
i
) (5)
In the fifth and final step, a reconstructed differential data row dPu(i) is integrated to obtain a reconstructed phase information data row Pu(i). Supposing the integration constant is &ggr;,
Pu
(
i
)=&Sgr;
dPu
(
i
)+&ggr; (6)
As explained above, the section regions are discriminated by using the two-step differential data row &dgr;Pw(i); the differential data row dPu (i) is reconstructed by finding the cumulative coefficient k(i); and the phase information data row Pu(i) is reconstructed by integration. Therefore, even if the amount of decentering of the phase patterns on the information recording medium is large, it is possible to accurately reconstruct the phase signal waveform indicating the original track location from the phase signal waveform measured in a convoluted state.
The use of the results achieves the accurate detection of the relationship between the amount of decentering and the phase. Consequently, a high precision tracking control can be realized by performing the tracking control of the head, while compensating decentering with the use of the relation.
In the above explanation, the boundary values for the section regions can be in the mode of “−&pgr;” and “&pgr;”. As another mode, the boundary values can be “−3&pgr;”, “−&pgr;” “&pgr;”, and “3&pgr;”. The former mode might cause singularities where the reconstructed phase signal waveform loses smoothness in parts, whereas the latter mode can suppress the occurrence of such singularities, and reconstruct the phase signal waveform indicating the track location with high precision.
In the integrating process in the fifth step, it is preferable to apply numerical integration to the data row obtained by using the mean value Ea of the reconstructed differential data row dPu(i) and by subtracting the mean value Ea from the reconstructed differential data row dPu(i).
REFERENCES:
pate
Ban Yasuaki
Hamada Taizou
Matsushita Electric - Industrial Co., Ltd.
Olson Jason
Tran Sinh
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