RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation

• Published in: Applied soft computing Vol. 162; p. 111791
• Main Authors: Raiguru, Priyadarshini, Swain, Bhanja Kishor, Rout, Susanta Kumar, Sahani, Mrutyunjaya, Mishra, Rabindra Kishore
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Direction of arrival (DOA); Field programmable gate array (FPGA); Reduced deep convolutional stack encoder (RDCSAE); Robust kernel random vector functional network (RKRVFLN); Root mean square error (RMSE); Robust kernel random vector functional network (RKRVFLN); Root mean square error (RMSE); Field programmable gate array (FPGA); Reduced deep convolutional stack encoder (RDCSAE); Direction of arrival (DOA)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2024.111791

This paper introduces an innovative unified deep learning (DL) model, “reduced deep convolutional stack autoencoder (RDCSAE)-robust kernel-based random vector functional link network (RKRVFLN)” (RDCSAE-RKRVFLN), for addressing practical challenges like phase and gain uncertainty, mutual coupling effects, and element position errors in direction of arrival (DOA) estimation. The RDCSAE utilizes deep convolutional neural network (DCNN) and stack autoencoder (SAE) for robust feature extraction. The RKRVFLN integrates a concise SAE output with kernel-based learning. Thus, the integration of RDCSAE (suitable for unsupervised feature extraction) and RKRVFLN (for supervised learning) in the developed model results in efficient use of limited training data. Performance metrics of this method include among others the accuracy (separation between sources), root mean square error (RMSE), signal-to-noise ratio (SNR), etc. The method performs better than existing data driven methods in terms of computational efficiency, faster learning speed, enhanced model generalization, higher accuracy, and shorter event regression time and empirical methods. In the presence of above mentioned four perturbations, this method successfully resolves multiple sources with a resolution of 0.3° with a RMSE of 0.00376. For real time application, the paper implements RDCSAE-RKRVFLN on a high-speed reconfigurable Xilinx Virtex-5 field-programmable gate array (FPGA) realizing a digital DOA estimator for one source. The hardware estimates the DOA with a maximum error of 0.01953125 which follows closely the simulation results indicating the efficiency and feasibility of the method. •A framework in the presence of array imperfection for the direction of the arrival estimation problem.•Reduced deep convolutional stack autoencoder network architecture produces high-level discriminative representations.•The less computational complex robust kernel RVFLN regression method shows outstanding performance.•An FPGA implementation proves the developed method’s robustness for direction finding.