Underwater Multi-Target Detection Algorithm (AIC) - Implementation and Optimization

The Underwater Multi-Target Detection Algorithm (AIC) is a widely used signal processing method specifically designed for multi-target recognition tasks in underwater environments. This algorithm employs the Akaike Information Criterion (AIC) to optimize the target detection process, effectively distinguishing characteristic signals from different targets while minimizing noise interference.

In MATLAB implementation, the AIC algorithm typically involves several critical steps: First, underwater signals are collected through sensors and undergo preprocessing operations such as filtering and noise reduction using functions like filter() or wavelet denoising techniques. Next, signal features are extracted through time-frequency analysis or statistical methods, followed by AIC criterion calculations to determine the optimal number of targets using information-theoretic approaches. Finally, target classification and localization are performed based on the computational results, often employing clustering algorithms or pattern recognition methods.

This algorithm demonstrates simplicity and efficiency, making it suitable for beginners to learn. Detection performance can be further optimized by adjusting parameters such as window size (controlling the analysis segment length) or threshold values (affecting sensitivity). The AIC algorithm not only applies to underwater applications but can also be extended to other signal processing domains like radar and sonar systems through appropriate parameter tuning and feature adaptation.