Modelling and simulation framework for ATR design evaluation

Automatic Target Recognition (ATR) and target tracking are fundamental functions in many military systems and so have a significant impact on a sensor system's performance. In response to the demand for increased capability, ATR designs have evolved from relatively simple filters to increasingly complex algorithms, using techniques such as artificial intelligence. Assessing the performance of image processing algorithms is a significant challenge, particularly with their increased design complexity. Increasing the complexity of a processing design tends to result in greater sensitivity to variations in scene conditions, rendering the system performance more nuanced with respect to the image content. There is a need to develop modelling and simulation design tools that better reflect the impact of image processing on the overall system performance when subjected to a wide variation in input scene and sensor platform characteristics. In this paper, the development and design of the System Performance Model (SPM) is described which provides the modelling and simulation of different image processing algorithms such as ATR. The approach taken within the SPM is to use real imagery and convert this to imagery that would be generated by the modelled camera. This process, which is described in the paper, is critical to the design of the SPM and underpins its effectiveness and accuracy. Example results are given that illustrate the design of the SPM's image conversion process.