Trackability in complex situations and environments

Valentino Crespi; Yong Sheng; George Cybenko

doi:10.1117/12.670132

19 May 2006 Trackability in complex situations and environments

Valentino Crespi, Yong Sheng, George Cybenko

Proceedings Volume 6231, Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII; 62310U (2006) https://doi.org/10.1117/12.670132
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

The concept of trackability is intimately related to the establishment of optimal trade-offs between the nosiness of the environment, due to poor sensing, and the randomness of the kinematics of the phenomena being examined, due to poor knowledge of their behaviors. Classically, a sensor system receives low level data in the form of numerical or analog signals and then through signal processing produces a high level observation suitable for a higher level state estimation process. These two phases may be further refined into a hierarchical chain of "tiers", where observations at each level are obtained through the computation of a set of properties of the system's estimated state at the lower level. An important factor that seems to have an impact on the overall ability to track high level phenomena in real time is the computational complexity of deciding those properties when generating observations between the tiers. And this complexity characterizes the accuracy of what can be computed within a bounded time frame. In this paper we intend to investigate the "real time" trackability of phenomena through the analysis of the complexity of individual models in relation to the computational complexity of computing observations in any multi-tiered tracking system.

Citation Download Citation

Valentino Crespi, Yong Sheng, and George Cybenko "Trackability in complex situations and environments", Proc. SPIE 6231, Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII, 62310U (19 May 2006); https://doi.org/10.1117/12.670132

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KEYWORDS

Sensors

Process modeling

Sensor networks

Computing systems

Radar

Signal processing

Environmental sensing

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