| Abstract: | Systems and methods are disclosed that include tools that utilize Dynamic
Detector Tuning (DDT) software that identifies near-optimal parameter
settings for each sensor using a neuro-dynamic programming (reinforcement
learning) paradigm. DDT adapts parameter values to the current state of
the environment by leveraging cooperation within a neighborhood of
sensors. The key metric that guides the dynamic tuning is consistency of
each sensor with its nearest neighbors: parameters are automatically
adjusted on a per station basis to be more or less sensitive to produce
consistent agreement of detections in its neighborhood. The DDT algorithm
adapts in near real-time to changing conditions in an attempt to
automatically self-tune a signal detector to identify (detect) only
signals from events of interest. The disclosed systems and methods reduce
the number of missed legitimate detections and the number of false
detections, resulting in improved event detection. |