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Collaborative Self-Localization and Target Tracking Under Sparse Communication

The problem of collaborative self-localization and target tracking method under challenge environment is studied in this paper. Specifically, the scenario with general nonlinear process and sensing model as well as sparse communication is considered by combining the distributed tracking (DT) and the collaborative localization (CL) techniques. To better characterize the statistics after nonlinear transformations, the unscented transformation (UT) approach is adopted. Simulations are extensively studied to show that the proposed method have better performance on both self-localization and target tracking than the solo CL or DT method.