Publications

Abstract not provided.

Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase significantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classification steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.

Abstract not provided.

Abstract not provided.

The widespread adoption of aerial, ground and sea-borne unmanned systems (UMS) for national security applications provides many advantages; however, effectively controlling large numbers of UMS in complex environments with modest manpower is a significant challenge. A control architecture and associated control methods are under development to allow a single user to control a team of multiple heterogeneous UMS as they conduct multi-faceted (i.e. multi-objective) missions in real time. The control architecture is hierarchical, modular and layered and enables operator interaction at each layer, ensuring the human operator is in close control of the unmanned team at all times. The architecture and key data structures are introduced. Two approaches to distributed collaborative control of heterogeneous unmanned systems are described, including an extension of homogeneous swarm control and a novel application of distributed model predictive control. Initial results are presented, demonstrating heterogeneous UMS teams conducting collaborative missions. Future work will focus on interacting with dynamic targets, integrating alternative control layers, and enabling a deeper and more intimate level of real-time operator control. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Abstract not provided.