Abstract-In this paper, simulated annealing algorithms are applied to the analyses of nonlinearly loaded antenna arrays. The analysis is first transformed into an optimization problem and then be solved by simulated annealing algorithms. Numerical examples show that the results calculated by the proposed method are consistent with those of other published papers. Nearly global optimum solutions can be obtained since the simulated annealing algorithm is inherently a direct searching method. It should be noted that the array mutual coupling effects are included in the analyses of this paper.

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In this paper, radar target recognition is given by KSDA (kernel scatter-difference discriminant analysis) pattern recognition on RCS (radar cross section). The kernel method converts the traditional FLDA (Fisher linear discriminant analysis) to a nonlinear high-dimensional space and such a kernel technique is called KFDA (kernel Fisher discriminant analysis). The basic concept of KFDA is to map training samples in the original space to a high-dimensional feature space via a nonlinear mapping function. Pattern recognition is then implemented in the feature space through extracted nonlinear discriminant features. However, as the kernel within-class scatter matrix is singular, the optimal discriminant features can not be achieved directly. To improve this drawback of KFDA, this study utilizes the scatter difference as the discriminant function, i.e., KSDA, to implement radar target recognition. The KSDA can modify the Fisher discrimination function and then serves as an efficient tool of radar target recognition. As a result, the computational complexity is reduced and then the computational speed is increased. Of great importance, the proposed target recognition scheme (based on KSDA) can still work well even though the kernel within-class scatter matrix is singular. Our KDSA based target recognition scheme is accurate, efficient and has good ability to tolerate random noises.

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.