Introductory Chapter: Kalman Filter - The Working Horse of Object Tracking Systems Nowadays [chapter]

Felix Goavers
2019 Introduction and Implementations of the Kalman Filter [Working Title]  
Introduction Sensordata fusion is the process of combining error-prone, heterogenous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses in order to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest, and since the
more » ... sensors involved most often are confronted with a dynamical world, the state of interest underlies an evolution process in time, which has to be reflected within the data processing. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. Kinematic laws and stochastic processes further provide the basis for evolution models in object tracking. The number of ingredients of the resulting Kalman filter is limited, but its applications are not. Invented many decades ago-Kalman's initial paper was published in 1960, and it is well known that similar solutions to the tracking problem were found even earlier-the Kalman filter is an algorithm with an extraordinary career. In the days it was invented, the Kalman filter was designed for tracking airplanes in the sky based on surveillance radars. As a consequence, the problems of measurement error and object dynamics were the key points, which the Kalman filter can cope with. Since then, sensor technology has evolved enormously. In recent decades, sensor technology has become increasingly important for numerous civilian and military applications, and it is obvious that this trend will continue in the future. High performance sensors have conquered many novel applications, and existing applications have been brought to a much higher level of technical complexity. This technological trend is accompanied with an evolution in the field of sensor data processing algorithms. Besides the family of Kalman filters, other solutions to the Bayesian approach to information processing have been developed-based on grids or Monte Carlo simulation for instance. However, the most often used approach for practical tracking system still is the Kalman filter, at least in one of its numerous variants. While the classical assumptions including linear models for sensor and dynamics, perfect data association, and known track existence are mostly handled in the academic and educational field, many extensions have been developed to bring the Kalman filter into practical and industrial systems. It is literally impossible to list all methods that have been developed
doi:10.5772/intechopen.84804 fatcat:ibc4zf3jivhw7gm5ymzlvpqb6e