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The purpose of this paper is to develop a new method for the two‐dimensional direction‐of‐arrival (DOA) estimation of multiple coherently distributed (CD) sources, which can provide lower computational complexity while sustaining the estimation performance within a tolerable level.

Using three parallel uniform linear arrays (ULAs), a new method for parametric estimation of multiple coherently distributed sources is proposed. The proposed method is based on the Taylor approximation to the generalized steering vectors (GSVs) of shifted ULAs, and utilizes the special array geometry. In addition, a simple parameter matching procedure is also given in this paper.

Several numerical experiments have been designed. The experiments are based on coherently distributed source model, and the noise is assumed to be zero‐mean and spatially and temporally white and Gaussian. Numerical results show that the proposed method can exhibit good estimation performance under small angular spread and be applicable to the multisource scenario with different angular distributions.

This research is limited to CD sources. Furthermore, the proposed method is based on the small angular approximation to GSV. Hence, it is fitter for the case of small angular extension.

Without any spectrum‐peak searching, the proposed method provides lower computational cost compared to the classical spectrum‐based methods. Moreover, it does not depend on the prior knowledge about angular distribution shape and is hence robust to mismodeling.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

The purpose of this paper is to present the methodology to the robust stability analysis of a vision‐based control loop in an uncalibrated environment. The type of uncertainties considered is the parametric uncertainties. The approach adopted in this paper utilizes quadratic Lyapunov function to determine the composite Jacobian matrix and ensures the robust stability using linear matrix inequality (LMI) optimization. The effectiveness of the proposed approach can be witnessed by applying it to two‐link robotic manipulator with the camera mounted on the end‐effector.

The objective of this research is the analysis of uncertain nonlinear system by representing it in differential‐algebraic form. By invoking the suitable system representation and Lyapunov analysis, the stability conditions are described in terms of linear matrix inequalities.

The proposed method is proved robust in the presence of parametric uncertainties.

Through a differential‐algebraic equation, LMI conditions are devised that ensure the stability of the uncertain system while providing an estimate of the domain of attraction based upon quadratic Lyapunov function.