In many civilian and military applications of infrared sensors, there is a need to be able to classify objects in the sensor's field of vision and discriminate between them. The informational needs of discrimination systems seem to have shifted with the use of passive sensors. Unlike radar signals, signals from passive sensors are basically nonperiodic; thus, information is not easily extracted from the frequency domain. This may make traditional sampling analog-to-digital signal converters inefficient. An innovative approach to analog-to-digital signal conversion is being investigated that appears very promising when used with passive sensor systems, for example, infrared cameras. Such an approach would generate better information for discrimination algorithms, improve the discrimination process, and use less complicated circuitry. Many civilian applications of sensor systems have the need for discrimination systems that are similar to those used in military applications. Automated discrimination is used in blood cell counters, nuclear magnetic resonance systems and other forms of medical diagnostic imaging. Discrimination and object recognition also play a large role in areas like robot vision, industrial process control and automated quality control.
