Helicopters present a serious threat to high security facilities such as prisons, nuclear sites, armories, and VIP
compounds. They have the ability to instantly bypass conventional security measures focused on ground threats such as
fences, check-points, and intrusion sensors. Leveraging the strong acoustic signature inherent in all helicopters, this
system would automatically detect, classify, and accurately track helicopters using multi-node acoustic sensor fusion. An
alert would be generated once the threat entered a predefined 3-dimension security zone in time for security personnel to
repel the assault. In addition the system can precisely identify the landing point on the facility grounds.
The ability to perform generalized ground vehicle classification by unattended ground sensors (UGS) is an important
facet of data analysis performed by modern unattended sensor systems. Large variation in seismic signature propagation
from one location to another renders exploiting seismic measurements to classify vehicles a significant challenge. This
paper presents the results of using an adaptive methodology to distinguish between tracked and wheeled ground vehicle
mobility mechanisms. The methodology is a passive in-situ learning process that does not rely upon an explicit
calibration process but does require an estimated range to the target. Furthermore, the benefits of the seismic feature
adaptation are realized with a sparse information set. There exist scenarios in which the adaptation fails to provide
information when implemented as an independent process. These situations, however, may be mitigated by sharing
information with other classification algorithms. Once properly initialized, the in-situ adaptation process correctly
categorizes over 95% of ground vehicles.