The work detailed in this document is the result of a collaborative effort of the LAAS-CNRS in Toulouse and MEAS-France / TE Connectivity during a period of three years.The goal here is to develop a methodology to design smart embedded sensors with the ability to estimate physical parameters based on multi-physical data fusion. This strategy tends to integrate sensors technologies, currently dedicated to lab measurements, in low powered embedded systems working in imperfects environments. After exploring model oriented methods, parameters estimations and Kalman filters, we detail various existing solutions upon which we can build a valid response to multi-physical data fusion problematics, for linear systems with the Kalman Filter, and for non-linear systems with the Extended Kalman Filter and the Unscented Kalman Filter.Then, we will synthesize a filter for hybrid systems, having a linear evolution model and a non-linear measurement model. For example, using the best of the two worlds in order to obtain the best complexity/precision ratio. Once we selected the estimation method, we detail computing power and algorithm complexity problematics in order to find available optimizations we can use to assess the usability of our system in a low power environment. Then we present the developed methodology application to the UQS sensor, sold by TE Connectivity, study case. This sensor uses near infra-red spectroscopy to determine the urea concentration in a urea/water solution, in order to control the nitrogen-oxyde depolluting process in gasoline engines. After a design principles presentation, we detail the model we created in order to represent the system, to simulate its behavior and to combine the measurement data to extract the desired concentration. During this step, we focus on the obstacles of our model calibration and the deviation compensation, due toworking conditions or to components aging process. Based on this development, we finally designed the hybrid models addressing the nominal working cases and the model re-calibration during the working duration of the product. After this, we presented obtained results, on simulated data, and on real-world measured data. Finally, we enhanced the methodology based on tabulated “black box” models which are easier to calibrate and cheaper to process. In conclusion, we reapplied our methodology to a different motion capture sensor, to compile all possible solutions and limits.