Key points are not available for this paper at this time.
Five different methods suitable for tracking single targets in clutter are compared: the nearest neighbor algorithm, the probabilistic multi-hypothesis tracking filter, the probabilistic data association filter, the mixture reduction algorithm, and the mean-field event-averaged maximum likelihood estimator. Across a range of clutter densities, comparison results were generated for a common, fixed set of Monte Carlo target, target measurement, and clutter measurement realizations. The relative performances, as measured by track lifetime, RMS tracking error, and computational complexity are compared.
Pao et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: