![]() This is the histogram representation of the velocity measurements. So there is one, and really only one, maximum value (unimodal) and a spread (variance). 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. I would like to first explain the idea of the Kalman filter (according to Rudolf Emil Kalman ) with only one dimension. ![]() The following explanation is borrowed from the Udacity CS373 course by Prof. Included is a sample LA92 driving cycle, battery parameters including internal resistance, and SOC-OCV curve for a Turnigy battery cell. Users also have the options of estimating SOC from -20C to 40C. In fact, one of the earliest uses of the Kalman Filter was to calculate the position of the Apollo space rockets by NASA to make sure it was on the right path. In order to perform the calculation optimally despite measurement noise, the “how strong” parameter must be known. The function can be used either an extended Kalman Filter (EKF) or adaptive-extended Kalman filter (AEKF). By Rekhit Pachanekar If we had to explain Kalman Filter in one line, we would say that it is used to provide an accurate prediction of a variable which cannot be directly measured. This “ how strong” is expressed with the variance of the normal distribution. This is determined once for a sensor that is being used and then uses only this “uncertainty” for the calculation. In the following, it is no longer calculated with absolute values but with mean values (μ) and variances σ ² of the normal distribution. The mean of the normal distribution is the value that we would want to calculate. The variance indicates how confidence level. 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). Let’s assume that the GPS signal has just been lost and the navigation system is completely unclear where you are. 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. Now comes a speed measurement from the sensor, which is also “inaccurate” with appropriate variance. These two uncertainties must now be linked together. With the help of Bayes rule, the addition of two Gaussian function is performed. Thrun explains this very clearly in the Udacity CS373 course. The two pieces of information (one for the current position and one for the measurement uncertainty of the sensor) actually gives a better result!. The narrower the normal distribution, the confident the result.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |