In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield
targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a
composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground
sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised
algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of
system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve
beyond the period of system operation in which the training data are representative.
To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of
ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for
target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised
routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines
(SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from
battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and
video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based
location and tracking of detected targets by active nodes.
The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection,
recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected
according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the
dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified
versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of
unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines
and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published
system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with
previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.