DETECTION AND CORRECTION OF FAULTY ARRAYS IN BEAMFORMING

Abstract

Detection and correction of faulty arrays is one of the important research areas in beamforming. If some elements in the array fail to work, the main objective is to steer the main beam in the desired direction and place the nulls in the direction of interferers. This research area has gained direct applications such as sonar, radar satellite, mobile communications etc. The work presented in this dissertation is mainly divided into two parts. In first part, our contribution is to develop an efficient method for the detection of faulty sensors. Specifically, the symmetrical structure of linear array is proposed, which has two advantages. Firstly instead of finding all damaged patterns, only half patterns are needed and, secondly, we require scanning the region from 0 0 to 90 0 instead of 0 0 to 180 0 , which obviously reduces the computational complexity to half. The basic tools designed to detect the location of faulty sensors are nature inspired heuristic computing and compressed sensing technique. These techniques are firefly algorithm, cultural algorithm with differential evolution, harmony search algorithm, cuckoo search algorithm and the compressed sensing techniques such as Parallel coordinate decent algorithm, separable surrogate functional algorithm and Iterative reweighted least square algorithms. Furthermore, the compressed sensing techniques are hybridized with evolutionary algorithm. The second part of this dissertation, the correction of faulty arrays is formulated in a unique way and five approaches are proposed to correct the faulty pattern. In the first approach, our contribution regarding correction of faulty sensors has achieved better null depth level (NDL) due to symmetrical element failure (SEF) technique. The symmetric element failure maintains the null depth almost close to that of the original array. The null depth of all nulls, especially the first one, has been achieved with the help of SEF technique. Null placement and sidelobe suppression have been achieved by hybridizing genetic algorithm with pattern search. In the secondvii approach, the symmetrical structure is used for the correction of faulty beams in failed array antenna. The corrected pattern has been achieved by a cultural algorithm with differential evolution using a proper fitness function. In the third approach, a cuckoo search algorithm (CSA) is developed based on SEF technique along with distance adjustment between the antenna elements for the correction of faulty beams. The proposed SEF technique along with distance adjustment d n based on CSA provides better results in terms of SLL and nulls in the direction of known interferers. In the fourth approach, a CSA is designed for the correction of single element failure. This time the proposed technique has used a new fitness function for the suppression of SLL and nulls in the direction of known interferers. In the fifth approach, using the advantage of symmetrical structure of linear array, a simple method has been developed for the reconstruction of faulty beams. The method recovers the failed element signal from its symmetrical counterpart element by taking its conjugate. The simulation results of detection and correction of faulty arrays are presented in comparison with the available techniques. In case of detection, our approach is computationally efficient to detect the complete as well as partial faulty elements. In case of correction we achieved better NDL which is of great importance in beamforming and nulls placement back to their original positions after the failure of elements.