The Kalman filter is an algorithm that estimates the state of a system from measured data. It was predominantly developed by the Hungarian engineer Rudolf Kalman, for whom the filter is named. The filters algorithm is a two-step process: the first step predicts the state of the system, and the second step uses noisy measurements to refine the estimate of system state. There are now several variations of the original Kalman filter. The Kalman filter has plentiful applications in technology. A common application is for guidance, navigation and control of vehicles, particularly aircraft and spacecraft. Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics. Kalman filters also are one of the main topics in the field of robotic motion planning and control, and they are sometimes included in trajectory optimization. Kalman filters are used for object tracking to predict an objects future location, to account for noise in an objects detected location, and to help associate multiple objects with their corresponding tracks. The output of the Kalman filter is denoted by the red circles and the object detection is denoted in black. Notice when the ball is occluded and there are no detections; the filter is used to predict its location. The purpose of the book entitled Kalman Filter Recent Advances and Applications is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. This book corresponding to modern advances in Kalman filtering theory, solicitations in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.