This manuscript describes a methodology to combine environmental models, acoustic signal predictions, statistical detection models and operations research to form a framework for calculating and communicating performance. This methodology has been applied to undersea target detection systems and has come to be known as Performance Surface modeling. The term Performance Surface refers to a geo-spatial representation of the predicted performance of one or more sensors constrained by all-source forecasts for a geophysical area of operations. Recent improvements in ocean, atmospheric and underwater acoustic models, along with advances in parallel computing provide an opportunity to forecast the effects of a complex and dynamic acoustic environment on undersea target detection system performance. This manuscript describes a new process that calculates performance in a straight-forward "sonar-equation" manner utilizing spatially complex and temporally dynamic environmental models. This performance model is constructed by joining environmental acoustic signal predictions with a detection model to form a probabilistic prediction which is then combined with probabilities of target location to produce conditional, joint and marginal probabilities. These joint and marginal probabilities become the scalar estimates of system performance. This manuscript contains two invited articles recently accepted for publication. The first article describes the Performance Surface model development with sections on current applications and future extensions to a more stochastic model. The second article is written from the operational perspective of a Naval commanding officer with co-authors from the active force. Performance Surface tools have been demonstrated at the Naval Oceanographic Office (NAVOCEANO) and the Naval Oceanographic Anti-Submarine Warfare (ASW) Center (NOAC) in support of recent naval exercises. The model also has recently been a major representation for the "performance" layer of the Naval Meteorological and Oceanographic Command (NAVMETOCCOM) in its Battlespace on Demand strategy for supporting the Fleet with oceanographic products.

Sound wave has much better transmission in ocean environment than electromagnetic waves, therefore sonar systems are widely applied in underwater investigations. However, not only the target signal is received by the sonar but also the noise from different directions. The noise will affect the performance of the sonar, so the understanding of ocean ambient is an important issue both in academic study and military applications. The ambient noise data of this research was collected by a passive acoustic recording system deployed in the southwest sea of Taiwan, along with the information of wind velocity in the experimented area. The influence on noise level fluctuations by the variation of the wind velocity was first discussed in light of correlation analysis. The fluctuations were expressed in terms of statistic distribution, mean value, standard deviation in different time series. As results, 500 Hz and 1.5k Hz were saturated by high levels signal from unknown sources in spring and summer, so the average sound levels were higher than in fall and winter, about 10 dB and 5 dB higher for 500 Hz and 1.5k Hz respectively. In seasonal analysis, 2.4k and 3.6k Hz have quite stable the mean levels and their standard deviations were around 3 dB. Especially, the noise level of 3.6 Hz has the least fluctuation throughout the year than any other frequencies analyzed. It was also observed that the noise level was decreased with the increase of frequency. Calculated by linear regression, this research worked out the estimation equation for the ambient noise level at high wind speed. However, the estimated values are higher than the measured data, it is due to the distribution of wind velocity. The wind data in this study was skewed towards the lower velocity, consequently the predicted values were overestimated.