Tactical sensing on the battlefield involves real-time information processing of acoustic, seismic, electromagnetic, and environmental sensor data to obtain and exploit an automated situational awareness. The value-added of tactical sensing is to give the war-fighter real-time situational information without requiring human interpretation of the underlying scientific data. For example, acoustic, seismic, and magnetic signatures of a ground vehicle can be used in a pattern recognition algorithm to identify a tank, truck, or TEL, but all the war-fighter wants to know is how many of each vehicle type are present and which way are they going. However, the confidences of this automated information processing are dependent on environmental conditions and background interference. A key feature of tactical ground sensing is the ability to integrate objective statistical confidences into the process to intelligently suppress false alarms, thus allowing the war-fighter to concentrate on war fighting. This paper presents how the situation confidence metric is generated starting with the sensor SNR going all the way through to the target classification and track confidences. This technique also allows modeling of ground sensor performance in hypothetical environments such as bad weather.