Signal Estimation

Signal estimation focuses on recovering an unknown signal from noisy or incomplete measurements, aiming to minimize estimation error and improve accuracy. Current research emphasizes developing efficient algorithms, such as projected gradient descent and approximate message passing, often incorporating generative models or leveraging graph signal processing techniques to handle complex data structures and noise types (e.g., impulsive noise). These advancements are crucial for diverse applications, including wireless communication, image processing, and biological data analysis, where robust signal recovery is essential for accurate interpretation and decision-making. The development of minimax-optimal estimators and the exploration of novel signal transforms, like the signed cumulative distribution transform, are also driving progress in the field.