Detection of Underwater UXOs in Mud

One of the most problematic areas where unexploded ordnance (UXO) can be situated is in an underwater environment, buried in sediment. Since burial frequently occurs in silt or mud, it is of great importance to have a capability for the detection of UXO in such buried conditions. This project consid...

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Bibliographische Detailangaben
Hauptverfasser:	Hunter, A J, Vossen, R van, Quesson, B A, Beckers, A L
Format:	Report
Sprache:	eng
Schlagworte:	Acoustic Detection and Detectors ALGORITHM IMPROVEMENTS ALGORITHMS Ammunition and Explosives BEAM STEERING BOSS(BURIED OBJECT SCANNING SONAR) BURIED OBJECTS DATA PROCESSING DATA PROCESSING CHAINS FIXED-FOCUS RESONANCE ENHANCEMENT INTERFEROMETRY LOW FREQUENCY MOTION MUD MUD(MINE UNDERWATER DETECTION) Navigation, Detection and Countermeasures RESONANCE REVERBERATION SAS(SYNTHETIC APERTURE SONAR) SEA CLUTTER SIDE LOOKING SONAR SYNTHETIC APERTURE SONAR TARGET DISCRIMINATION UNDERWATER OBJECTS UNEXPLODED AMMUNITION UXO(UNEXPLODED ORDENANCE) WAVELET TRANSFORMS WIDEBEAM MOTION COMPENSATION WIDEBEAM VERTICAL BEAMSTEERING
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Beschreibung
Zusammenfassung:	One of the most problematic areas where unexploded ordnance (UXO) can be situated is in an underwater environment, buried in sediment. Since burial frequently occurs in silt or mud, it is of great importance to have a capability for the detection of UXO in such buried conditions. This project considers detection of objects buried in mud using a side-looking low frequency (LF) sonar system mounted on a boat. The detection of buried UXO with a LF side-looking sonar system, however, is a challenging problem. This is primarily caused by high reverberation and possible clutter. In addition, the amplitude of the target echo of an object is reduced by burial. For these conditions, dedicated processing has to be developed for the detection of UXO and other targets of interest, and for the subsequent discrimination between targets and clutter contacts. In this project robust and advanced processing techniques are developed with the objective to improve detection and classification in operational conditions. These techniques have been demonstrated on experimental data.