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Acoustic sensors are the primary sensor of choice to detect threat
submarines operating below periscope depth. However, the increasingly
quieter nuclear threat and the diesel-electric-on-battery threat limits
traditional narrow-band processing, yielding shorter detection ranges
and requires more array gain via more sensors and adaptive signal
processing to counter the quieting trends. One of the key
functionalities of embedded sensor networks is to detect events of
interest efficiently. We have developed new technologies used for
undersea submarine detection. Our system is based on the signal cluster
trending analysis for submarine detection and statistical model for
false alarm mitigation. We have tested our system with DADS data from
six sensors placed in two sensor arrays. Based on our test results, we
have been able to detect all the targets listed in the ground truth. The
system developed in Phase I has three major components: (1) cluster
trend construction, (2) abnormal event detection, and (3) false alarm
mitigation. The test results have shown that we have been able to detect
all targets given in the ground truth. |
