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Accurate estimation of mixing height is important, since it is an important parameter for lower atmospheric studies involving aerosol monitoring and pollutant dispersal models. Sodar happens to be one of the best instruments for monitoring the mixing height. But it suffers from the drawback of acoustic noise, which makes the measurement inaccurate. Conventional Kalman filter has been used to estimate atmospheric boundary layer by filtering the measurement noise involved in sodar data. But there are certain limitations of the accuracy available from conventional Kalman filter which may be overcome by proper adaptive design. The present work develops an adaptive scheme for estimation of mixing heights. It considers the selection of a proper meteorological system model from a bank of system models. Diurnal and seasonal changes in measurement noise statistics of the acoustic radar is taken into account by designing a fuzzy logic based adaptive scheme.

Adaptive robust synchronization method is proposed for chaos synchronization of Lurie systems with time-varying delay. To weaken the restrictions on the change rate of time-varying delay in synchronization problems, adaptive estimations related to the derivative's upper bound of the time-varying delay are designed, so this upper bound can be unknown. The Lipschitz constant of the nonlinear link is also adaptively estimated, rather than being pre-calculated. Whole design is in the framework of robust control, and the synchronization can endure disturbance effectively. In addition, the synchronization of chaotic Lurie system with time-varying delay is realized as an example.