Sorry I was referring to the AR . <| sep |> Exactly what I meant . Thanks ! <| sep |> I ' m not yet iFwend , buddy ! <| sep |> I am not an ESA anything guy , but I have some knowledge of positioning problems . The article makes it clear that the inertial measurement unit was saturated , meaning the actual accelerationdeceleration was higher than the maximum value of the unit . Let ' s say your sensor can measure up to 10 g , but you decelerate at 15 g the sensor still reports 10 g and your algorithms that are supposed to estimate the height use 10 g in their calculations . Now the estimated velocity is higher than the actual velocity and the estimated position is lower than the actual position . Why is such an estimation based on inertial measurements used anyway ? Because the more accurate and absolute measurement , using the dopplerradar in this case , takes time . Inertial measurements produce errors that increase over time since estimation of the current position uses a measurement of accelerationdeceleration with a small error and the previous estimation of position and velocity , which itself is based on the position and velocity before and another measurement of acceleration with also a small error . and so on . If you wait long enough , an estimate of position with nothing but measurements of acceleration is as worthless as it can be . But it can be used to interpolate values for short periods until a new absolute measurement of the position is available . Normally such an algorithm is implemented as a KalmanFilter , where measurement and inertia based predictions are combined to an estimate , essentially weighted by their trustworthiness . This is a wonderful and also very stable algorithm , but faulty measurements can even fool the mighty Kalman , at least for some time .