Dr. Ashish Bhargave Profile

Dr. Ashish Bhargave

Research Scientist at Broadata Communications Inc

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Proceedings Article | 2 May 2007 Paper

An intelligent algorithm for unmanned aerial vehicle surveillance

Ashish Bhargave, Barry Ambrose, Freddie Lin, Manthos Kazantzidis

Proceedings Volume 6561, 65611I (2007) https://doi.org/10.1117/12.719596

KEYWORDS: Unmanned aerial vehicles, Surveillance, Computer simulations, Optimization (mathematics), Visibility, Distributed computing, Algorithms, Surveillance systems, Sensors, Safety

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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.

Proceedings Article | 9 April 2007 Paper

Multisensor detection and fusion technique

Ashish Bhargave, Barry Ambrose, Freddie Lin, Manthos Kazantzidis

Proceedings Volume 6571, 657109 (2007) https://doi.org/10.1117/12.719505

KEYWORDS: Sensors, Video, Detection and tracking algorithms, Image processing, Doppler effect, Prototyping, Signal processing, Infrared sensors, Video surveillance, Surveillance

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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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