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,"
In rhis work a iiew technique is presented f a . blind chunnel rynaliza~ion. Must of the existing techniques perfor-m channel esrimatior? in firs1 phuse and eqzrdizotion in second phase. The algorithm pr.e.~enred hew provides not on1,v the direct blind equalizntion of fhe chcmnel oiitprrts but also provides the w h a l e d channe( in parallel. This technique utilizes three layered Artificial Neural Networks (ANN) model accompanied with learning algorithm for updufing of the weights. This learni*rg algorithm iriilizes the EiiclideaH distance error as well as the statistics to be moiiiraiiied ai three different lajrers of' ANN. The weights between first two layers provide an eqwlizatioiz matri.y and oufpiif of second layer gives estimrrte of s o w c e symbok. The weights between second and rhird layer provide channel esfimate.
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