Addressing Uncertainty in Signal Propagation and Sensor Performance Predictions

摘要

As advanced sensors are increasingly relied upon for force protection, rapid strike, maneuver support, and other tasks, expert decision support tools (DSTs) are needed to recommend appropriate mixes of sensors and placements that will maximize their effectiveness. These tools should predict effects on sensor performance of the many complexities of the environment, such as terrain conditions, the atmospheric state, and background noise and clutter. However, the information available for such inputs is often incomplete and imprecise. To avoid drawing unwarranted conclusions from DSTs, the calculations should reflect a realistic degree of uncertainty in the inputs. In this report, a Bayesian probabilistic framework is developed for this purpose. The initial step involves incorporating uncertainty in the weather forecast, terrain state, and tactical situation by constructing an ensemble of scenarios. Next, a likelihood function for the signal propagation model parameters specifies uncertainty at smaller spatial scales, such as that caused by wind gusts, turbulence, clouds, vegetation, and buildings. An object-oriented software implementation of the framework is sketched. Examples illustrate the importance of uncertainty for optimal sensor selection and determining sensor coverage.