High precision load estimation during strong transients is still one of the challenges, which have to be solved in modern engine control units. One of the new methods, which deal with the request mentioned above, is an 'extended Kalman filter' (EKF), as described in data fusing for optimization of spark ignition engine control by M. Scherer. As this algorithm requires equidistant sampling of one sample every 45 degrees crank angle, i.e. sampling time of 1.25 ms at an engine speed of 6000 rpm, the hardware- platform as well as the software implementation must meet strict requirements in order to achieve on-line estimation and data processing. Furthermore, the crank angle synchronous manifold pressure and air mass flow pulsations must be considered in the modelling of the EKF. As the phase and amplitude of these pulsations are not constant over all operating points, methods to adjust these, e.g. considering the pulsation in the system model or making use of an on- line-FFT, must be applied in order to avoid large modelling errors. The matrix operations encountered in the algorithm are the most time- consuming operations and for this reason much attention must be paid to efficient software development. This paper presents methods required for an on- line-EKF filter implementation on a specially configured hardware platform and a dynamic elimination of pulsations with varying phase and amplitude.