We present the “Bayesian Occupancy Filter” (BOF) and the “Fast Clustering- Tracking” algorithms as a framework for robust sensing and multi-target tracking using multiple sensors. Perceiving of the surrounding physical environment reliably is a major demanding in smart systems requiring a high level of safety such as car driving assistant, autonomous robots, and surveillance. The dynamic environment need to be perceived and modeled according to the sensor measurements which could be noisy. To fit such a requirement, we propose a hierarchical approach in which two filtering layers are used: (i) Robust grid-level sensor fusion using the “Bayesian Occupancy Filter” algorithm in order to construct an occupancy/velocity grid representation of the environment. (ii) Robust object-level tracking using the “Fast Clustering-Tracking”.