The increasing availability of location-acquisition technologies (such as GPS and GSM networks) and mobile computing techniques has generated a lot of spatial-temporal trajectory data and indicates the mobility of diversified moving objects such as people, vehicles, and animals. This brings new opportunities to identify abnormal activities of moving objects. This paper describes our detection of anomalies in human trajectory data using a hybrid grid-based hierarchical clustering method based on Hausdorff distance, which is suitable for measuring the similarity between trajectories of different lengths. The trajectories were first transformed into grid-based trajectories using a grid structure. After that, the grid-based trajectories were clustered based on their pairwise Hausdorff distances by applying different versions of hierarchical clustering algorithms. We evaluated our research result using a reallife dataset (published by Microsoft Research Asia), ground truth reconstructed by us, and evaluation criteria widely used in data mining. The experimental results demonstrate that the proposed algorithm is more effective and much faster than the traditional hierarchical clustering algorithm according to the pairwise comparison results.
This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise variance and noise model, a bias correction term is added into the least squares estimate, and the system parameters and states are computed interactively. The proposed algorithm can generate the unbiased parameter estimate. Two illustrative examples are given to show the effectiveness of the proposed algorithm.
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