Recent results on adaptive track and multitrack fitting

Track reconstruction in the LHC environment is characterized by a high background of noise and low-momentum tracks. Adaptive algorithms are able to suppress automatically noise hits and are therefore prime candidates for track fitting at the LHC. They can be implemented by minimizing an energy function (Elastic Arms) or by iterating a recursive least-squares estimator (Deterministic Annealing Filter and Multitrack Filter). We present results from several simulation studies and show that the application of adaptive estimators can lead to substantial gains as compared to standard linear least-squares estimators.