Localization and Target-Tracking are both tough and wide spread tasks in mobile robotics. As the localization issue can be defined as the process to determine the position of an object within a reference coordinate system, the tracking consists in constructing a trajectory thanks to a collection of spatially and temporally coherent localizations. We propose in this paper a swarm approach for addressing this issue using an interaction paradigm inspired from physics. The combination of different inter-agent or agent-environment interactions such as attraction, repulsion, consumption and evaporation results in a self-organised 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 considered as moving targets. In particular, a comparison with the standard Kalman filtering method demonstrates the relevancy of our approach. Moreover, it overpasses traditional ones concerning its robustness, adaptability, data fusion capabilities.