estimation problems encountered in several areas of engineering and science. In particular, it is shown how to formulate such estimation problems as instances of a general dynamical system state estimation problem and how to derive the mathematical solution of the latter problem. Then it is shown that, for a linear Gaussian system, such a solution yields the well-known Kalman filter. Further, approximate techniques (e.g. extended and unscented Kalman filters, particle filter, etc.) are presented for the case of nonlinear and/or non-Gaussian systems, for which an exact closed-form solution cannot be found. To conclude the theoretical part, theoretical limitations (i.e. the Cramer-Rao lower bound) on the quality of estimation are discussed. In the second part of the course, we illustrate some applications of linear/nonlinear Kalman filtering (e.g., tracking, robotic navigation, environmental data assimilation).

The course includes a demonstrative part where a practical implementation of the standard IEEE 802.15.7 for vehicular communications will be discussed and demonstrated through a dedicated electronic system. In particular, the integration in FPGA of the digital sections of the transmitter and the receiver will be analysed; and the analog electronics necessary to drive and modulate a power LED, and to detect the signal from the photosensor will be reviewed. Finally, live communication over a VLC link will be demonstrated.