Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets

The datasets store both motion and observation information of a single
fluorescent sub-diffraction limit-sized particle moving in a
three-dimensional confined environment. The confined motion is following a
nonlinear model driven by non-Gaussian noise, the observation is formed by
engineered Double-helix (DH) point spread function (PSF) and captured by
scientific complementary metal-oxide semiconductor (sCMOS) camera. Based
on our prior computationally efficient application of Sequential Monte
Carlo - Expectation Maximization (SMC-EM), we extended it to handle the
DH-PSF for encoding the three-dimensional position of the particle in
two-dimensional image plane of the camera. We focus on studying the
datasets at low signal and low signal-to-background ratio (SBR). Based on
the datasets across different SBR and confinement lengths, a quantitative
comparison is conducted to show that in the low signal regime, the SMC-EM
approach outperforms the other methods while at higher
signal-to-background levels, SMC-EM and the MLE-based methods perform
equally well and both are significantly better than fitting to the MSD. In
addition, our results indicate that at smaller confinement lengths where
the nonlinearities dominate the motion model, the SMC-EM approach is
superior to the alternative approaches.