Data fusion is the methodology of efficiently combining the relevant information from different sources. The goal is to achieve estimates and inferences with better confidence than those achievable by relying on a single source. Initial data fusion applications were predominantly in defense: target tracking, threat assessment and land mine detection. Nowadays, data fusion is applied to robotics (e.g., environment identification for navigation), medicine (e.g., medical diagnosis), geoscience (e.g., data integration from different sources) and industrial engineering (e.g., fault detection). This thesis focuses on data fusion for distributed multisensor tracking systems. In these systems, each sensor can provide the information as measurements or local estimates, i.e., tracks. The purpose of this thesis is to advance the research in the fusion of local estimates for multisensor multitarget tracking systems, namely, track fusion. This study also proposes new methods for track-to-track association, which is an implicit subproblem of track fusion. The first contribution is for the case where local sensors perform tracking using particle filters (Monte Carlo based methods). A method of associating tracks estimated through labeled particle clouds is developed and demonstrated with subsequent fusion. The cloud-to-cloud association cost is devised together with computation methods for the general and specialized cases. The cost introduced is proved to converge (with increasing clouds cardinality) toward the corresponding distance between the underlying distributions. In order to simulate the method introduced, a particle filter labeled at particle level was developed, based on the Probability Hypothesis Density (PHD) particle filter. The second contribution is for the case where local sensors produce tracks using Kalman filter-type estimators, in the form of track state estimate and track state covariance matrix. For this case the association and fusion is improved in both terms of accuracy and identity, by introducing at each fusion time the prior information (both estimate and identity) from the previous fusion time. The third contribution is for the case where local sensors produce track estimates under the form of MHT, therefore where each local sensor produces several hypotheses of estimates. A method to use the information from other sensors in propagating each sensor's internal hypotheses over time is developed. A practical fusion method for real world local tracking sensors, i.e., asynchronous and with incomplete information available, is also developed in this thesis. / Thesis / Doctor of Philosophy (PhD)