Computer-implemented method of determining operational performance parameters of a device, in particular of a vehicle (VCL), with device mounted sensors (SNS) and computer-implemented models (MDS, MD2), comprising the steps: (a) measuring a first parameter set (PS1) during operation of the device (DVC) using said sensors (SNS), (b) providing a computer-implemented supplemental model (MD1) and determining a second parameter set (PS2) by the supplemental model (MD1), (c) combining parameters of the first parameter set (PS1) with at least one selected parameter (SPS) of the second parameter set (PS2) obtaining a hybrid parameter set (PSH), (d) providing the parameters of the hybrid parameter set (PSH) to a Kalman-Filter-module (EKF), (e) the Kalman-Filter-module (EKF) predicting a third parameter set (PS3) by a second model (MD2) being equivalent to the hybrid parameter set (PSH) regarding the respective parameter types, (f) the Kalman-Filter-module (EKF) comparing the parameters of the hybrid parameter set (PSH) and the third parameter set (PS3), (g) the Kalman-Filter-module (EKF) estimating an output parameter set (PSO), the method characterized in comprising: (i) determining a deviation (DVT) between respectively one distinct parameter (DCP) of the first parameter set (PS1) and the equivalent parameter of said second parameter set (PS2), (ii) providing a correlation (CVL) between said deviation (DVT) values and covariance values (COV) for at least one parameter of said third parameter set (PS3), (iii) determining a covariance value (COV) from said correlation (CVC) on basis of the determined deviation (DVT), (iv) forwarding said covariance value (COV) to said Kalman-Filter-module (EKF) for estimating said output parameter set (PSO). (a) measuring a first parameter set (PS1) during operation of the device (DVC) using said sensors (SNS), (b) providing a computer-implemented supplemental model (MD1) and determining a second parameter set (PS2) by the supplemental model (MD1), (c) combining parameters of the first parameter set (PS1) with at least one selected parameter (SPS) of the second parameter set (PS2) obtaining a hybrid parameter set (PSH), (d) providing the parameters of the hybrid parameter set (PSH) to a Kalman-Filter-module (EKF), (e) the Kalman-Filter-module (EKF) predicting a third parameter set (PS3) by a second model (MD2) being equivalent to the hybrid parameter set (PSH) regarding the respective parameter types, (f) the Kalman-Filter-module (EKF) comparing the parameters of the hybrid parameter set (PSH) and the third parameter set (PS3), (g) the Kalman-Filter-module (EKF) estimating an output parameter set (PSO), (i) determining a deviation (DVT) between respectively one distinct parameter (DCP) of the first parameter set (PS1) and the equivalent parameter of said second parameter set (PS2), (ii) providing a correlation (CVL) between said deviation (DVT) values and covariance values (COV) for at least one parameter of said third parameter set (PS3), (iii) determining a covariance value (COV) from said correlation (CVC) on basis of the determined deviation (DVT), (iv) forwarding said covariance value (COV) to said Kalman-Filter-module (EKF) for estimating said output parameter set (PSO). <EOS>