Probabilistic framework for characterizing uncertainty in the performance of networked battlefield sensors

As reliance upon advanced networked sensors increases, 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 paper, a Bayesian probabilistic framework is developed that provides sensor performance predictions given explicit uncertainties in the weather forecast, terrain state, and tactical scenario. A likelihood function for the signature propagation model parameters is specified based on the forecast and additional local information that may be supplied by the user. The framework also includes a likelihood function for the signal/noise features as a function of the propagation model parameters and tactical scenario. Example calculations illustrate the significant impact of uncertainty in optimal sensor selection and DST predictions.