This thesis considers source localization and tracking when both the signal propagation model and the source motion dynamics are unknown. Algorithms are developed for different scenarios. The algorithms are discussed when a source is stationary or mobile, under the condition when sensors are fixed or mobile. These algorithms exploit the strictly decreasing properties of the model in terms of distance, but do not depend on the form and the values of the models. Therefore, these algorithms could be applied when the signal propagation models and the source motion are unknown. The only assumption made is that the signal propagation strength decreases in distance. For a given performance specification, the optimal number and placement of the sensors is also discussed. Convergence and other properties of the algorithms are established under various noise assumptions.