Stochastic process modeling and Monte Carlo simulation: assisting higher-level decision making for aided target recognition networks

Dalton S. Rosario

doi:10.1117/12.438237

27 August 2001 Stochastic process modeling and Monte Carlo simulation: assisting higher-level decision making for aided target recognition networks

Dalton S. Rosario

Author Affiliations +

Proceedings Volume 4382, Algorithms for Synthetic Aperture Radar Imagery VIII; (2001) https://doi.org/10.1117/12.438237
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Higher-level decisions for AiTR (aided target recognition) networks have been made so far in our community in an ad-hoc fashion. Higher level decisions in this context do not involve target recognition performance per se, but other inherent output measures of performance, e.g., expected response time, long-term electronic memory required to achieve a tolerable level of image losses. Those measures usually require the knowledge associated with the steady-state, stochastic behavior of the entire network, which in practice is mathematically intractable. Decisions requiring those and similar output measures will become very important as AiTR networks are permanently deployed to the field. To address this concern, I propose to model AiTR systems as an open stochastic-process network and to conduct Monte Carlo simulations based on this model to estimate steady state performances. To illustrate this method, I modeled as proposed a familiar operational scenario and an existing baseline AiTR system. Details of the stochastic model and its corresponding Monte-Carlo simulation results are discussed in the paper.

Citation Download Citation

Dalton S. Rosario "Stochastic process modeling and Monte Carlo simulation: assisting higher-level decision making for aided target recognition networks", Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); https://doi.org/10.1117/12.438237

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