Abstract-In this work, we propose a method based

Abstract-In this paper, we have addressed three major problems of uniform linear array in case of a sensor failure at any position. We assume that sensor position is known. The problems include increase in sidelobe levels, displacement of nulls and diminishing of null depth. The desired null depth is achieved by making the weight of symmetrical counterpart element passive. Genetic algorithm (GA) along with pattern search (PS) is used for reduction of sidelobe levels, and adjustment of nulls. Fitness function minimizing the error between the desired and estimated beam pattern along with null constraints is used. Simulation results for diversified scenarios have been given to demonstrate the validity and performance of the proposed algorithm.

Abstract:In this paper, we propose a structure for independent null steering by decoupling complex weights. It has the advantage of independent steering of (N − 1) nulls for a linear array having N elements and the complex weights are decoupled such that each weight corresponds to a particular null. For this purpose, the position of each zero of the array factor on the unit circle in the complex plane is controlled by the corresponding weight in the structure. It means that if a single jammer changes its position, only a single corresponding complex weight has to be recalculated, which reduces computational complexity. Keywords: null steering, linear array, array factor, progressive phase Classification: Science and engineering for electronics References[1] H. Krim and M. Viberg, "Two decades of array signal processing research,"

An easy and efficient approach, based on artificial intelligence technique, is proposed to jointly estimate the amplitude, elevation, and azimuth angles of far field sources impinging on 2-L-shape array. In these proposed artificial intelligence techniques, the metaheuristics based on genetic algorithm and simulated annealing are used as global optimizers assisted with rapid local version of pattern search for optimization of the adaptive parameters. The performance metric is employed on a fitness evaluation function depending on mean square error which is optimum and requires single snapshot to converge. The proposed approaches are easy to understand, and simple to implement; the genetic algorithm specifically hybridized with pattern search generates fairly good results. The comparison of the given schemes is carried out with 1-L-shape array, as well as, with parallel-shape array and is found to be in good agreement in terms of accuracy, convergence rate, computational complexity, and mean square error. The effectiveness and efficiency of the given schemes are examined through Monte Carlo simulations and their inclusive statistical analysis.

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