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Fronts have been recognized as hotspots of intense biological activity. They are therefore important targets for observation to understand coastal ecology and transport in a changing ocean. With high spatial and tem- poral variability, detection and event response for frontal zones is challenging. Robotic platforms like autonomous underwater vehicles (AUVs) have shown their versatility in using automated approaches to detect a range of features; directing them using in-situ and on-shore capabilities for front detection then becomes an important tool for observ- ing such rapid and episodic changes. We introduce a novel momentum-based front detection (MBFD) algorithm de- signed to automatically detect frontal zones. MBFD utilizes a Kalman filter and a momentum accumulator function to identify significant temperature gradients associated with upwelling fronts. MBFD is designed to work at a number of levels including onboard an autonomous underwater vehicle (AUV); on-shore with a sparse, real-time data stream and post-experiment on a hi-resolution data set gathered by a robot. Such a multi-layered approach plays an important role in mixed human-computer decision making for oceanographers making coordinating sampling and asset allocation strategies in large multi-robot field experiments in the coastal ocean.
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