Fractional Lower-Order Moment Algorithm for Alpha-Stable Noise Suppression

In the described context, the Fractional Lower-Order Moment algorithm successfully suppresses alpha-stable noise, enabling more accurate estimation of target source location and characteristics. The algorithm's core methodology employs polynomial fitting techniques to calculate lower-order moments of signals, significantly improving estimation precision. Implementation typically involves calculating fractional moments using specialized functions that handle the heavy-tailed distributions characteristic of alpha-stable noise. Furthermore, this algorithm demonstrates versatility in applications spanning image processing and audio signal processing domains, where it enhances both processing quality and accuracy. The algorithm's robustness makes it particularly valuable for complex signal processing scenarios operating in high-noise environments, with practical implementations often incorporating adaptive thresholding and optimization routines for real-time performance. Consequently, the Fractional Lower-Order Moment algorithm presents broad application prospects in signal processing, especially when dealing with sophisticated signals under challenging noise conditions.