Localization and target-tracking are both difficult yet essential and wide spread tasks in mobile robotics. Localization can be defined as determining the position of an object within a reference coordinate system, tracking consists of constructing a trajectory given a collection of spatially and temporally coherent localizations. We propose in this paper a swarm approach to address these issues using an interaction paradigm inspired from physics. The combination of different interactions such as attraction, repulsion, consumption and evaporation results in a self-organized process that builds patterns interpreted as solutions of the problem. After a description of the proposed model, this paper analyses the properties and the performance of our device through experiments in simulation and with real robots. In particular, a comparison with the standard Kalman filtering method demonstrates the relevancy of our approach. Moreover, it improves upon traditional ones int terms of robustness, adaptability, and data fusion capabilities.