Proceedings Article | 2 September 2004
KEYWORDS: Sensors, Performance modeling, Automatic target recognition, Lab on a chip, Systems modeling, Solid modeling, Error analysis, Forward looking infrared, Computer aided design, Data modeling
A performance model for FLIR automatic target recognition is discussed. Key aspects of this model are that (a) relationships between sensor optical resolution, sampling, noise and estimated P(ID) are implicitly defined, (b) premised on the use of the particular features that are used, the analysis of the matching structure leads to an explicit "shape similarity" measure between targets, (c) the notion of "shape" includes both internal signature attributes and external contour information; (d) the values of this shape measure can be measured for both true and false target models using combined CAD rendering, sensor models, and features, (e) in addition to the P(ID), the system also is able to predict the probability of declaration P(Declare|Target) for a given true target, (f) the system is able to predict the probability of false declaration for a given confuser or confuser to target similarity specification, (g) M (with M greater than or equal to 2) class problems are able to be handled, and (h) the diagonals along confusion matrices can be estimated directly using this approach. The model relies on analysis of performance of a particular type of shape-based features, with the goal of developing explicit relationships from low level features through high level model matching. Based on the predicted densities of the ensemble of features, the system approximates an expression for the likelihood of the observed features under noisy conditions with a given sensor, conditioned on the target type, aspect, and range. Using some engineering approximations that relate to the distance transform-type method of matching that is analyzed, a tractable form of a non-unique correspondence based approximate likelihood expression is obtained, which can be used to estimate bounds on the performance of similar sensor/ATR systems that rely on these features. Such an approach could also be applied to other phenomenologies, such as synthetic aperture radar, using an appropriate low level model of the extracted features. Predictive models using a CAD based target signature rendering package have been used to generate target signatures. Trade-offs for various combinations of sensor/algorithm design parameters are in principle able to be carried out quickly and easily using this approach.
