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Unattended ground sensors (UGS) provide the capability to inexpensively secure remote borders and other
areas of interest. However, the presence of normal animal activity can often trigger a false alarm.
Accurately detecting humans and distinguishing them from natural fauna is an important issue in security
applications to reduce false alarm rates and improve the probability of detection. In particular, it is
important to detect and classify people who are moving in remote locations and transmit back detections
and analysis over extended periods at a low cost and with minimal maintenance. We developed and
demonstrate a compact radar technology that is scalable to a variety of ultra-lightweight and low-power
platforms for wide area persistent surveillance as an unattended, unmanned, and man-portable ground
sensor. The radar uses micro-Doppler processing to characterize the tracks of moving targets and to then
eliminate unimportant detections due to animals as well as characterize the activity of human detections.
False alarms from sensors are a major liability that hinders widespread use. Incorporating rudimentary
intelligence into sensors can reduce false alarms but can also result in a reduced probability of detection.
Allowing an initial classification that can be updated with new observations and tracked over time provides
a more robust framework for false alarm reduction at the cost of additional sensor observations. This paper
explores these tradeoffs with a small radar sensor for border security.
Multiple measurements were done to try to characterize the micro-Doppler of human versus animal and
vehicular motion across a range of activities. Measurements were taken at the multiple sites with realistic
but low levels of clutter. Animals move with a quadrupedal motion, which can be distinguished from the
bipedal human motion. The micro-Doppler of a vehicle with rotating parts is also shown, along with
ground truth images. Comparisons show large variations for different types of motion by the same type of
animal.
This paper presents the system and data on humans, vehicles, and animals at multiple angles and directions
of motion, demonstrates the signal processing approach that makes the targets visually recognizable,
verifies that the UGS radar has enough micro-Doppler capability to distinguish between humans, vehicles,
and animals, and analyzes the probability of correct classification.
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