Multi-Target Motion Parameter Estimation Using Hyperbola-Warping transform

Abstract This study introduces an innovative method known as the Hyperbolic-warping transformation to tackle the issues of noise susceptibility and fluctuating parameter accuracy associated with the conventional Hough transform. By leveraging a comprehensive analysis of the entire time-frequency interference pattern, our technique enables the precise extraction of motion parameters for several targets identified by a solitary hydrophone. The proposed approach excels in its ability to accurately pinpoint the time of closest approach ( t CPA ) and calculate the velocity-to-distance ratio ( b = r CPA / v ) for each target. Comparative analyses through both simulated and maritime experimental settings reveal that our Hyperbolic-warping transformation outperforms the traditional Hough transform, showing a 10 dB enhancement in noise tolerance and a 20% extension in detection capabilities. The findings underscore the robustness and practicality of our proposed method in accurately estimating multi-target motion parameters, especially in noisy environments.