Tracking and data association
Fundamentals of digital image processing
Fundamentals of digital image processing
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A 3D world model builder with a mobile robot
International Journal of Robotics Research
Cramer-Rao bound for tracking in clutter and tracking multiple objects
Pattern Recognition Letters
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Neural networks and intellect: using model-based concepts
Neural networks and intellect: using model-based concepts
Digital Photogrammetry
High resolution terrain mapping using low altitude aerial stereo imagery
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI
IEEE Transactions on Neural Networks
Language and cognition interaction neural mechanisms
Computational Intelligence and Neuroscience
Distributed filtering over sensor networks for autonomous navigation of UAVs
Intelligent Service Robotics
Journal of Global Information Management
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We are developing a probabilistic technique for performing multiple target detection and localization based on data from a swarm of flying sensors, for example to be mounted on a group of micro-UAVs (unmanned aerial vehicles). Swarms of sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, for deployment of swarms to be feasible, UAVs must operate more autonomously. The current approach is designed to reduce the load on humans controlling UAVs by providing computerized interpretation of a set of images from multiple sensors. We consider a complex case in which target detection and localization are performed concurrently with sensor fusion, multi-target signature association, and improved UAV navigation. This method yields the bonus feature of estimating precise tracks for UAVs, which may be applicable for automatic collision avoidance. We cast the problem in a probabilistic framework known as modeling field theory (MFT), in which the pdf of the data is composed of a mixture of components, each conditional upon parameters including target positions as well as sensor kinematics. The most likely set of parameters is found by maximizing the log-likelihood function using an iterative approach related to expectation-maximization. In terms of computational complexity, this approach scales linearly with number of targets and sensors, which represents an improvement over most existing methods. Also, since data association is treated probabilistically, this method is not prone to catastrophic failure if data association is incorrect. Results from computer simulations are described which quantitatively show the advantages of increasing the number of sensors in the swarm, both in terms of clutter suppression and more accurate target localization.