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Because detecting the ball's position will have noise caused by camera or image processing, or the chosen object detected algorithm could go undetected to the ball. We use the Kalman Filter algorithm to solve this problem. Kalman Filter can deal with this noise problem and help us to get a more accurate ball's position. The operation of the Kalman filter involves estimating the system's state by combining the system model and observed data. It consists of two main phases, which are the prediction phase and the update phase. In the prediction phase, the current state is predicted based on the previous moment's state as a reference. The predicted state is corrected in the update phase based on the current observed values. Through this recursive process, the accuracy of the estimation continuously improves. In this figure, we represent the Kalman Filter's main idea. First, we have the previous state called x k minus one. Next, we use the system model equation to predict the current state called x k negative. Then, we are in the update phase. We will use the current measurement to update our state so we can get a more accurate state called x k. This is the basic principle of the Kalman Filter, and the equation of Kalman Filter will be introduced in the next page. |
IEEE/ICACT20240248 Slide.10
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