Sampling Theory for Process Detection with Applications to Surveillance and Tracking

摘要

In this paper, we investigate the link between the rate at which events are observed by a monitoring system and the ability of the system to effectively perform its tracking and surveillance tasks. In general, higher sampling rates provide better performance, but they also require more resources, both computationally and from the sensing infrastructure. We have used Hidden Markov Models to describe the dynamic processes to be monitored and (α,β)-currency as a performance measure for the monitoring system. Our ultimate goal is to be able to determine the minimum sampling rate at which we can still fulfill the performance requirements of our system. Along with the theoretical work, we have performed simulation-based tests to examine the validity of our approach; we compare performance results obtained by simulation with the theoretical value obtained a priori from the scenario parameters and illustrate with a simple example a technique for estimating the required sampling rate to achieve a given level of performance.