1/3/2023 0 Comments Scilab kalman filter![]() ![]() The narrower the normal distribution, the confident the result. The two pieces of information (one for the current position and one for the measurement uncertainty of the sensor) actually gives a better result!. Thrun explains this very clearly in the Udacity CS373 course. With the help of Bayes rule, the addition of two Gaussian function is performed. These two uncertainties must now be linked together. Now comes a speed measurement from the sensor, which is also “inaccurate” with appropriate variance. The Uncertainty is High, as the variance is in a large magnitude.( image-source) Normal distribution with variance = 20 and mean = 0 The variance is high, the curve corresponding is really flat. Let’s assume that the GPS signal has just been lost and the navigation system is completely unclear where you are. The narrower the normal distribution (low variance), the confident the sensors are with the measurements.Ī sensor that measures 100% exactly has a variance of σ ²= 0 (it does not exist). The variance indicates how confidence level. The mean of the normal distribution is the value that we would want to calculate. In the following, it is no longer calculated with absolute values but with mean values (μ) and variances σ ² of the normal distribution. This is determined once for a sensor that is being used and then uses only this “uncertainty” for the calculation. This “ how strong” is expressed with the variance of the normal distribution. In order to perform the calculation optimally despite measurement noise, the “how strong” parameter must be known. The following explanation is borrowed from the Udacity CS373 course by Prof. I would like to first explain the idea of the Kalman filter (according to Rudolf Emil Kalman ) with only one dimension. Idea of the Kalman filter in a single dimension If this is the case, we can do the calculation very well with a trick nevertheless. So there is one, and really only one, maximum value (unimodal) and a spread (variance). This is the histogram representation of the velocity measurements. ![]()
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