Title: Timing recovery in data storage systems: framework and approach of Kalman filtering
Authors: Jin Xie, B.V.K. Vijaya Kumar
Addresses: Department of Electrical and Computer Engineering, Data Storage Systems Center, Carnegie Mellon University, PA 15213, USA. ' Department of Electrical and Computer Engineering, Data Storage Systems Center, Carnegie Mellon University, PA 15213, USA
Abstract: Recent error correction coding is able to work with low Signal-to-Noise Ratios (SNR), thus increasing the recording densities in data storage systems. With low SNR, conventional timing recovery is likely to lose stability and entire blocks of data may be lost. Reliable timing recovery is required in low SNR to reduce the loss of lock rate. In this paper, we introduce the framework of current timing recovery system, challenges of timing recovery in low SNR, and application of Kalman filtering to timing recovery. With Kalman filtering, timing recovery performance is improved in acquisition, tracking, and dropout compensation, and loss of lock rate is reduced.
Keywords: data storage; timing recovery; Kalman filter; low SNR; loss of lock; loop delay compensation; dropout compensation; signal-to-noise ratio; recording densities.
International Journal of Product Development, 2008 Vol.5 No.3/4, pp.430 - 446
Published online: 11 Mar 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article