Stochastic process modeling and Monte Carlo simulation: assisting higher-level decision making for aided target recognition networks
Dalton S. Rosario · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.