Underwater Sonar Target Identification By leveraging advanced sonar systems, including active sonar, multibeam sonar, and acoustic imaging, users can identify, validate, and classify underwater targets with remarkable precision. Underwater dynamic targets often display significant blurriness in their forward looking sonar imagery, accompanied by sparse feature representation. this phenomenon presents several challenges, including disturbances in the trajectories of underwater targets and alterations in target identification throughout the tracking process, thereby.
Underwater Sonar Target Identification Compared to optical imaging, sonar target detection has the characteristics of strong penetration and long scanning distance, which makes it more suitable for tasks such as deep sea, turbid water, and long distance target detection. This paper presents a new detection framework that enhances the feature information by recording and fusing the acoustic shadows of sonar pictures to get rich feature information from sonar images and improve the identification capacity of the network to target features. This paper provides a systematic review of explainable models for underwater target recognition, elaborating on the core concepts and main methods of explainability. it also reviews research progress and representative achievements in sonar imaging, signal analysis, and autonomous navigation. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking.
Underwater Sonar Target Identification This paper provides a systematic review of explainable models for underwater target recognition, elaborating on the core concepts and main methods of explainability. it also reviews research progress and representative achievements in sonar imaging, signal analysis, and autonomous navigation. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking. This paper reviews the integration of machine learning (ml) techniques with sonar systems to enhance underwater target prediction. we discuss the types and applications of sonar systems, emphasizing the importance of accurate target detection and classification. For side scan sonar (sss) submarine pipeline and cable target feature extraction, there are some problems such as poor real time performance, high false detection rate, and difficulty in deploying edge equipment.with deep feature technology,this study applies a deep neural network to detect submarine pipeline and cable targets in order to solve the above problems.to enable real time detection. Sound navigation ranging, the method of gathering of underwater acoustic signal and processing it to detect a target's characteristic is known as ranging, or sonar, target identification. Is the ability to detect and identify submerged objects or targets accurately. these targets can range fr m shipwrecks and marine life to underwater vehicles and geological formations. traditional methods for underwater target detection have often relied on sonar technology.
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