The Error Analysis Based on the Kalman Gain in a Position Predicting Algorithm of an Occluded Object

Detecting occluded objects is a crucial exercise in many spheres of application. For example in Strafing (attacking ground targets from low flying aircrafts) or vehicular tracking, continuous detection of the object even when it is occluded by another object is essential. Failing to track the occluded object may result in completely losing its location or another object to be mistakenly tracked. Both of which will result in disastrous consequences. There are various methods to handle occlusions. In a previous research which was done by the author, a novel noise filtration mechanism based on the corrector equation of the Kalman filter which can be used with greater accuracy to handle lengthy occlusions was made. In this presentation, a further analysis of the error of the algorithm will be presented. The algorithm when compared with existing algorithms under the same test conditions gives promising results.