Underwater Target Recognition Based on Dynamic Ensemble of Random Forest

• Published in: 水下无人系统学报 Vol. 32; no. 3; pp. 552 - 557
• Main Authors: Tao CAO, Jianjing DENG, Ling YUE, Yongsheng LI
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2024
• Subjects: dynamic ensemble selection; random forest; target recognition; underwater acoustic homing weapon
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2024-0054

Accurate recognition of the target is the key to attacking enemy for underwater acoustic homing weapon. A real-time target recognition method for underwater acoustic homing weapon was proposed based on dynamic ensemble selection technology. The statistical features of energy distribution and spatial distribution were extracted from the output of target wideband correlation detection by using the different reflection characteristics of the target irradiated by the active wideband detection waveform of the underwater acoustic homing weapon. In addition, a dynamic ensemble model based on a random forest was constructed, and it was trained and tested on the marine dataset. The simulation analysis shows that the dynamic ensemble model proposed in this paper has better recognition effects than other classification models and can be applied to target recognition by underwater acoustic homing weapon.