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In order to achieve maximum noise reduction inside an aircraft cabin through the use of an active noise control system (ANCS), it is important that the number and positions of the sensors for monitoring the noise field; the control system for driving the actuators; and the number and positions of the actuators that generate the secondary noise field, which partially cancels the primary noise field, must be optimally determined. An optimization strategy for the positioning of the actuators, based on genetic algorithms (GA), is presented, assuming a fixed sensor configuration and a given control system. The application of the developed GA to a propeller aircraft is also discussed. The work presented was performed under the CEC BRITE/EURAM‐Aeronautics project “ASANCA”, in which a demonstrator ANCS was developed.

Abshnnct -The area of underwater passive target tracking has received considerable attention in the past decades, due to both its theoretical interest and its practical importance in several applications. Many powerful tools fhm the fields of signal processing, image processing, and estimation theory have been brought to bear for the solution of the passive target tracking problem. Among the latter, techniques based on Kalman filtering and techniques based on partitioning filters have been successfully used. The approaches based on Kalman filtering do not usually perform adequately when facing a maneuvering target, whereas the techuiques based on partitioning filters perform very satisfactorily in the same case. In this paper, four approaches to the problem of underwater passive target tracking, based on the partitioning theory are reviewed and discussed. Their performance is also checkd against that of Kalman filtering-based approaches in both maneuvering and nonmaneuvering targets scenaria.