Track fitting with ambiguities and noise: A study of elastic tracking and nonlinear filters Original Research Article

We present a study of track fitting in the presence of ambiguous measurements and noise. We consider four methods for solving the resulting assignment problem, two elastic arm algorithms and two nonlinear filters, including a novel development, the Deterministic Annealing Filter. We describe their basic features and investigate their relationships with each other and with other popular estimators, in particular with the EM algorithm. The performance of the methods is optimized by means of several simulation experiments. We study the influence of the annealing schedule on the performance and show that the application of advanced minimization methods is required in order to obtain reliable estimates of the track parameters. We compare the relative efficiencies and the computational costs of the four methods both under ideal conditions and with noise. A final experiment under realistic conditions focuses on the robustness of the proposed approach.