Multi-sonar target detection using multi-channel coherence analysis

The use of multiple disparate platforms in many remote sensing and surveillance applications allows one to exploit the coherent information shared among all sensory systems thereby potentially reducing the risk of making single-sensory biased detection and classification decisions. This paper introduces a target detection method based upon multi-channel coherence analysis (MCA) framework which optimally decomposes the multi-channel data to analyze their linear dependence or coherence. This decomposition then allows one to extract MCA features that can be used to implement a coherence-based detector. This detector is applied to a data set of underwater side-scan sonar imagery provided by the Naval Surface Warfare Center Panama City Division. This database contains data from 2 disparate sonar systems, namely one high frequency (HF) sonar and one broadband (BB) sonar coregistered over the same region on the sea floor. Test results illustrate the effectiveness of the proposed multi-platform detection system in terms of probability of detection, false alarm rate, and receiver operating characteristic (ROC) curves.