Multi-Feature Fusion TOA Algorithm Based on Deep Learning and Compressed Sensing

In the field of signal processing, traditional time of array(TOA) algorithms, as well as those based on compressed sensing, excel in determining time estimates for data. However, these algorithms typically operate within linear models and may not fully extract data information, thereby affecting estimation accuracy and resolution. Capitalizing on their robust nonlinear processing capabilities, deep learning algorithms demonstrate proficiency in extracting features from data. Consequently, we propose a time delay estimation algorithm that initially preprocesses data through compressed sensing. Subsequently, the data undergo processing through a constructed multimodal model, where various features are fused, leading to the final time delay estimation result. By comprehensively processing various features, the algorithm extracts information more thoroughly, resulting in heightened resolution. Experimental assessments confirm the commendable performance of the algorithm.