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This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model.