An intelligent swarm-based guidance and path planning algorithm for the Unmanned Arial Vehicles (UAV) provides
the ability to efficiently carry out grid surveillance, taking into account specific UAV constraints such as maximum
speed, maximum flight time and battery re-charging intervals to allow for continuous surveillance. The swarm-based
flight planning is based on enhancements of distributed computing concepts that have been developed for NASA's
launch danger zone protection. The algorithm is a modified version of an ant colony optimization theory describing ant
food foraging. Ants initially follow random paths from the nest, but if food is found, the ant deposits a pheromone
(modifying the local environment), which influences other ants to travel the same path. Once the food source is
exhausted, the pheromone decays naturally, which causes the trail to disappear. When an ant is on an established trail, it
may at any time decide to follow a new random path, allowing for new exploration. Using these concepts, in our system
for UAV, we use two units, the Rendezvous unit and the Patrol unit. The Rendezvous units will act as pheromone
deposit sites keeping a record of trails of interest (extra pheromone that decays over time), and obstacles (no
pheromone). The search area is divided into a grid of areas. Each area unit is assigned a pheromone weight. The patrol
unit picks an area unit based on a probabilistic formula consisting of parameters like the relative weight of trail
intensity, area visibility to the unit, the distance of the patrol unit from the area, and the pheromone decay factor.
Simulation of a UAV surveillance system based on the above algorithm showed that it has the ability to perform
independently and reliably without human intervention, and the emergent nature of the algorithm has the ability to
incorporate important aspects of unmanned surveillance.
A multi-sensor detection and fusion technology is described in this paper. The system consists of inputs from three
sensors, Infra Red, Doppler Motion, and Stereo Video. The technique consists of three processing parts corresponding
to each sensor data, and a fusion module, which makes the final decision based on the inputs from the three parts. The
signal processing and detection algorithms process the inputs from each sensor and provides specific information to the
fusion module. The fusion module is based on the bayes belief propagation theory. It takes the processed inputs from all
of the sensor modules and provides a final decision for the presence and absence of objects, as well as their reliability
based on the iterative belief propagation algorithm operating on decision graphs. This choice of sensors is designed to
give high reliability. The infra red and Doppler provide detection ability at night, while stereo video has the ability to
analyze depth and range information. The combination of these sensors has the ability to provide a high probability of
detection and a very low false alarm rate. A prototype system was built using this technique to study the feasibility of
intrusion detection for NASA's launch danger zone protection. The system verified the potential of the proposed
algorithms and proved the feasibility of high probability of detection and low false alarm rates compared to many
existing techniques.
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