Proc. SPIE. 7333, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Sensors, Error analysis, Fourier transforms, Palladium, Acoustics, Signal detection, Global Positioning System, Unattended ground sensors
Textron Systems (Textron) has been using geophones for target detection for many years. This sensing
capability was utilized for detection and classification purposes only. Recently Textron has been evaluating
multiaxis geophones to calculate bearings and track targets more specifically personnel. This capability will not
only aid the system in locating personnel in bearing space or cartesian space but also enhance detection and
reduce false alarms.
Textron has been involved in the testing and evaluation of several sensors at multiple sites. One of the
challenges of calculating seismic bearing is an adequate signal to noise ratio. The sensor signal to noise ratio is
a function of sensor coupling to the ground, seismic propagation and range to target. The goals of testing at
multiple sites are to gain a good understanding of the maximum and minimum ranges for bearing and detection
and to exploit that information to tailor sensor system emplacement to achieve desired performance. Test sites
include 10A Site Devens, MA, McKenna Airfield Ft. Benning, GA and Yuma Proving Ground Yuma, AZ.
Geophone sensors evaluated include a 28 Hz triax spike, a 15 Hz triax spike and a hybrid triax spike consisting
of a 10 Hz vertical geophone and two 28 Hz horizontal geophones.
The algorithm uses raw seismic data to calculate the bearings. All evaluated sensors have triaxial geophone
configuration mounted to a spike housing/fixture. The suite of sensors also compares various types of
geophones to evaluate benefits in lower bandwidth.
The data products of these tests include raw geophone signals, seismic features, seismic bearings, seismic
detection and GPS position truth data. The analyses produce Probability of Detection vs range, bearing
accuracy vs range, and seismic feature level vs range. These analysis products are compared across test sites
and sensor types.
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.