The problem solved in this paper is easily stated: for a scenario with 𝑛 networked and moving imaging sensors, 𝑚 moving targets and 𝑘 independent observers searching imagery produced by the 𝑛 moving sensors, analytically model system target acquisition probability for each target as a function of time. Information input into the model is the time dependence of 𝘗_∞ and 𝜏, two parameters that describe observer-sensor-atmosphere-range-target properties of the target acquisition system for the case where neither the sensor nor target is moving. The parameter 𝘗_∞ can be calculated by the NV-IPM model and 𝜏 is estimated empirically from 𝘗_∞. In this model 𝑛, 𝑚 and 𝑘 are integers and 𝑘 can be less than, equal to or greater than 𝑛. Increasing 𝑛 and 𝑘 results in a substantial increase in target acquisition probabilities. Because the sensors are networked, a target is said to be detected the moment the first of the 𝑘 observers declares the target. The model applies to time-limited or time-unlimited search, and applies to any imaging sensors operating in any wavelength band provided each sensor can be described by 𝘗_∞ and 𝜏 parameters.

Date Published: 29 May 2014
PDF: 33 pages
Proc. SPIE 9071, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXV, 90710O (29 May 2014); doi: 10.1117/12.2054695

Published in SPIE Proceedings Vol. 9071:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXV
Gerald C. Holst; Keith A. Krapels; Gary H. Ballard; James A. Buford Jr.; R. Lee Murrer Jr., Editor(s)