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.

In this paper, we propose a method based on evolutionary computations for joint estimation of amplitude, Direction of Arrival and range of near field sources. We use memetic computing in which the problem starts with a global optimizer and ends up with a local optimizer for fine tuning. For this, we use Genetic algorithm and Simulated annealing as a global optimizer while Interior Point Algorithm as a rapid local optimizer. We set up Mean Square Error as a fitness evaluation function which defines an error between actual and estimated signal. This fitness function is optimum and is derived from Maximum likelihood principle. It requires only single snapshot to converge and does not require any permutations to link it with the angles found in the previous snapshot as in some other methods. The efficiency and reliability of the proposed scheme is tested on the basis of Monte-Carlo simulations and its inclusive statistical analysis.

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Abstract:In orthogonal frequency division multiplexing (OFDM), sidelobes of the modulated subcarriers cause high out-of-band (OOB) radiation, resulting in interference to licensed and un-licensed users in a cognitive radio system environment. In this work, we present a novel technique based on a generalized sidelobe canceller (GSC) for the reduction of sidelobes. The upper branch of the GSC consists of a weight vector designed by multiple constraints to preserve the desired portion of the input signal. The lower branch has a blocking matrix that blocks the desired portion and preserves the undesired portion (the sidelobes) of the input signal, followed by an adaptive weight vector. The adaptive weight vector adjusts the amplitudes of the undesired portion (the sidelobes) so that when the signal from the lower branch is subtracted from the signal from the upper branch, it results in cancellation of the sidelobes of the input signal. The effectiveness and strength of the proposed technique are verified through extensive simulations. The proposed technique produces competitive results in terms of sidelobe reduction as compared to existing techniques. OPEN ACCESSAppl. Sci. 2015, 5 895

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