We compare three methods of superresolving SWI distance measurements: Local Linear Regression (LLR), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and Nonlinear Least Squares (NLS).  At longer distances, LLR provides the fastest unbiased estimates.  LLR and NLS are more noise tolerant than ESPRIT and are found to operate close to the Cramér-Rao bound. 
LLR
We compare three methods of superresolving SWI distance measurements: Local Linear Regression (LLR), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and Nonlinear Least Squares (NLS).  LLR and NLS are more noise tolerant than ESPRIT and are found to operate close to the Cramér-Rao bound. 
ESPRIT
We compare three methods of superresolving SWI distance measurements: Local Linear Regression (LLR), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and Nonlinear Least Squares (NLS).  For very short distances, between 2 and 20 times the SWI system's Fourier transform-limited resolution, NLS provides unbiased estimates with the least uncertainty.  LLR and NLS are more noise tolerant than ESPRIT and are found to operate close to the Cramér-Rao bound. 
NLS