Novel Turbo Equalization Methods for the Magnetic Recording Channel Elizabeth Chesnutt 95 Pages Directed by Dr. John R. Barry The topic of this dissertation is the derivation, development, and evaluation of novel turbo equalization techniques that address the colored noise problem on the magnetic recording channel. One new algorithm presented is the noise-predictive BCJR, which is a soft-output detection strategy that mitigates colored noise in partial-response equalized magnetic recording channels. This algorithm can be viewed as a combination of the traditional BCJR algorithm with the notion of survivors and noise prediction. Additionally, an alternative equalization architecture for magnetic recording is presented that addresses the shortcomings of the PRML approach, which dominates magnetic recording. Specifically, trellis-based equalizers are abandoned in favor of simple equalization strategies based on nonlinear filters whose complexity grows only linearly with their length. This research focuses on the linear-complexity SFE algorithm and on investigating the possibility of lowering the SFE filter calculation complexity. The results indicate that with using the proposed novel SFE method, it is possible to increase the information density on magnetic media without raising the complexity. The most important result presented is that partial-response equalization needs to be reconsidered because of the amount of noise enhancement problems that it adds to the overall system. These results are important for the magnetic recording industry, which is trying to attain a 1 Tb/cm2 information storage goal.