Artificial Intelligence Enhanced Information Processing

Performance of robotic systems would be enhanced through artificial intelligent fusion of the automatic target recognition (ATR) results (and associated underlying features ) of either a single platform or multiple platforms viewing the target/object from different position, i.e., multi-look fusion. Such fusion would be particularly relevant for partially obscured and/or background blended targets. We propose a multi-layer perceptron neural net for implementing the multi-look fusion. This neural net would be similar to that recently used by other ARL ATR researchers. Performance would be further enhanced by smart fusion of individual sensors classifier outputs, either on the same or multiple platforms, i.e., multi-sensor fusion. Our approach would start with the fusion scheme being investigated by us under another SBIR for Target Acquisition/Target Recognition (TATR) encompassing man-in-the-loop decision making. For the ARL SBIR, we will enhance the TATR fusion scheme by building an artificial intelligent agent that augments and automates aided fusion function, e.g., adaptive thresholding for multi-sensor fusion. This intelligent agent would also incorporate production rules for associating different views of the same object for multi-look fusion. the resulting algorithms will be applicable to ATR fusion implementations both on vehicles and fusion stations. Our multi-look and multi-sensor classifier fusion approaches are very practical for operational scenarios, because neither depends on precise co-registration of the various disparate sensors.