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Many popular direction-of-arrival (DOA) estimators rely on the fact that the array response vector of the array is Vandermonde, for example, that of a uniform linear array (ULA). Array interpolation is a preprocessing technique to transform the array response vector of a planar array of arbitrary geometry to that of a ULA over an angular sector. While good approximation within the target sector is attained in the existing array interpolation approaches, the response of the interpolated array in the out-ofsector region is at best partially controlled. Accordingly, out-ofsector signals, especially those highly correlated with the insector signals, can degrade significantly the performance of DOA estimators (e.g., MUSIC with spatial smoothing) that rely on the Vandermonde form to work correctly. In this paper, we propose an improved array interpolation approach that takes into account the array response over the full azimuth. We present also numerical examples to demonstrate the shortcomings of the existing approaches and the effectiveness of our proposal.

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Multiple Traveling Salesman Problem (MTSP) is able to model and solve various real-life applications such as multiple scheduling, multiple vehicle routing and multiple path planning problems, etc. While Traveling Salesman Problem (TSP) focuses on searching a path of minimum traveling distance to visit all cities exactly once by one salesman, the objective of the MTSP is to find m paths for m salesmen with a minimized total cost -the sum of traveling distances of all salesmen through all of the respective cities covered. They have to start from a designated depot which is the departing and returning location of all salesmen. Since the MTSP is a NP-hard problem, a new effective Genetic Algorithm with Local operators (GAL) is proposed in this paper to solve the MTSP and generate high quality solution within a reasonable amount of time for real-life applications. Two new local operators, Branch and Bound (BaB) and Cross Elimination (CE), are designed to speed up the convergence of the search process and improve the solution quality. Results demonstrate that GAL finds a better set of paths with a 9.62% saving on average in cost comparing to two existing MTSP algorithms.

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