I n a modern aircraft, data fusion is the process by which data about the environment are gathered, coinbiaed, reasoned over, and presented to the pilot. Lkterinining which data to gather is obviously important to achieving effective data fusion. But the need for data depends OIL uncertain, interrelated and dynamic factors. This fact has pushed data-gathering determination beyond the ability of the human aitd led researchers to study structured decision-aiding systems called seiuor managers. This paper discusses sensor tnanagerneiit, focusing first O I L the problem it poses i n a modern tactical aircraft aitd then on attributes that would be desirable i n an effective sensor manager. Several techniques that offer promise are discussed.
Nonlinear regression with an intercept is investigated and a new nonlinear regression algorithm is developed. The application area considered is ballistic trajectory determination from battlefield radar measurements. Specifically, the geo-location of an enemy artillery piece is pursued. Careful modelling of the nonlinear measurement situation at hand and the inclusion of an intercept parameter in the nonlinear regression shows a considerable improvement over conventional iterative least squares estimation when nonlinearity is dominant. Moreover, the estimation performance does not degrade from standard iterative least squares (ILS) in cases where the nonlinearity is weak compared to the measurement noise in the equation error, provided that the data record is sufficiently long.
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