Enhanced Estimation for Direction of Arrival Calibration Based on Fusion of Fuzzy Type-2 Weighted Iterative Learning Control

The direction of arrival (DOA) estimation theorem is effective in the advanced sonar, satellite systems, radar, instrumentation, and cellular communications. A novel array calibration method is proposed in this article, focusing on DOA in narrowband signals. This method describes an array calibration method for adaptive beamforming and direction finding that includes unknown phase and active sensor gain perturbations. The main objective of this article is to estimate DOA in one, two, or more known signal sources using the weighted iterative learning control (ILC) technique. Along with the learning capability of ILC, the amount of computation required is reduced. As regards ILC being model-free and requiring little model knowledge, it is suitable for use in DOA. The high-performance fuzzy type-2 PD-WILC model is used together with the narrowband DOA model to achieve better accurate target spacing in the face of non-repetitive disturbances in sonar. ILC performance is significantly reduced due to nonrepetitive disorders. By introducing the integration of each weighted vector dimension in the DOA model to fuzzy type-2 PD-ILC, this article improves the robustness and accuracy of target detection of the DOA problem against nonrepetitive and unwanted disturbances. Simulation and practical results confirmed the improvement of problem performance by the proposed method.