Concurrent detection and classification of targets with multistage signal‐processing algorithms
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Concurrent detection and classification (CDAC) of targets stands as the goal in littoral mine‐hunting missions. CDAC systems commonly apply model‐based algorithms that include a priori known features of the target inside the detection algorithm. If the models are accurate, then this approach significantly reduces the false‐alarm rate inherent in detection‐only methods. When the possible targets are unknown, as may be the case in tactical situations, then these model‐based methods not only fail to reduce the false‐alarm rate, but may also reduce the probability of detection. Simultaneous optimization of detection and classification presents a challenge due to competing criteria; detection seeks to integrate signals to improve signal‐to‐noise ratio, while classification seeks to preserve small features of distinction within the signals. In this work, a method for robust CDAC is demonstrated that exploits the capabilities of autonomous underwater vehicles (AUVs) and multistage signal‐processing algorithms to...