Optimizing Machine Learning Algorithms for Dynamic Direction Finding

We build, analyze, and compare six machine learning algorithms for direction-finding on dynamic systems. Each model is optimized to decrease its computational complexity to make quicker angle-of-arrival estimates and maintain accuracy. The extracted angle determination compares favorably in terms of accuracy and speed to the MUSIC algorithm, with some models exceeding its performance.