H∞ control techniques are prominently used as solutions for flight control problems. From the literature, a variety of techniques is reported in the last three decades with specific merits and demerits, which, when applied to multiple flight control scenarios, showing trade off in terms of performance and robustness. However, all these methods possess superior performance when compared with that of classical approaches. In this paper an attempt is made to provide an insight into the requirements and criticalities in the design of missile autopilot. This paper introduces some of the significant H∞ control techniques like H∞ mixed sensitivity, H∞ loop shaping and μ synthesis, with specific emphasis on analysis of autopilot design. A comparative account of modern control methods is presented on the basis of system performance and robustness, which will be helpful in the selection of the appropriate design method for specific application.

This paper gives the survey of the existing developments of Visual object target tracking using particle filter from the last decade and discusses the advantage and disadvantages of various particle filters. A variety of different approaches and algorithms have been proposed in literature. At present most of the work in Visual Object Target Tracking is focusing on using particle filter. The particle filters has the advantage that they deal with nonlinear models and non-Gaussian innovations, and they focus sequentially on the higher density regions of the state space, mostly parallelizable and easy to implement, so it gives a robust tracking framework, as it models the uncertainty and showing good improvement in the recognition performance compared to the kalman filter and other filters like Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF).Various features and classifiers that are used with particle filter are given in this survey.

This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.