2008 Winner: Rudolf Kalman
For the development and dissemination of the optimal digital technique (known as the Kalman Filter) that is pervasively used to control a vast array of consumer, health, commercial and defense products.
Biography
Rudolf Kalman was born in Budapest, Hungary, on May 19, 1930. The son of an electrical engineer he decided to follow in his father's footsteps. He immigrated to the United States and obtained a bachelor's and master's degree in electrical engineering from M.I.T. in 1953 and 1954 respectively. He continued his studies at Columbia University where he received his Sc.D. in 1957. He is widely regarded as the creator of modern control theory and system theory. His research reshaped the field of control engineering and placed the groundwork for future research and innovation. His most widely known accomplishment is his development of the Kalman Filter, a mathematical method now widely used in navigation, particularly in aviation. He developed the Kalman Filter while at the Research Institute for Advanced Study in Baltimore from 1958 until 1964.
He was a professor at Stanford University from 1964 until 1971, and Graduate Research Professor, and Director, at the Center for Mathematical System Theory, University of Florida, Gainesville from 1971 until 1992. Starting in 1973, he simultaneously filled the chair for Mathematical System Theory at the Swiss Federal Institute of Technology in Zurich.
He is a member of the United States National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences. He is a foreign member of the Hungarian, French, and Russian academies of science. He has many honorary doctorates.
He received the IEEE Medal of Honor in 1974, the IEEE Centennial Medal in 1984, the Inamori Foundation's Kyoto Prize in High Technology in 1985, the Steele Prize of the American Mathematical Society in 1987, and the Richard E. Bellman Control Heritage Award in 1997.
Kalman Filter
The Kalman Filter is a mathematical technique that makes it much easier to track moving objects (and similar phenomena) in spite of noisy, imprecise data. It is most commonly used in radar tracking and stabilizing video, but it is also useful for a wide variety of applications ranging from weather forecasting to speech processing to economic predictions. In order to use it, a person or computer gathers some data (for example a blurry radar image showing a plane). Using the Kalman Filter, the user can then estimate where the plane or other object is and predict where it will be when the next set of data arrives. Then, as new data come in, the filter allows the user to constantly improve the estimates, getting closer and closer to a correct assessment of where the object is and how it is moving.
