Effects of data representation and network architecture variation on multi-aperture vision system performance

This research focuses on the effects of data representation and variations in neural network architecture on the tracking accuracy of a multi-aperture vision system (MAVS). A back-propagation neural network (BPNN) is used as a target location processor. Six different MAVS optical configurations are simulated in software. The system's responses to a point source target, in the form of detector voltages, and the known target location form a training record for the BPNN. Neural networks were trained for each of the optical configurations using different coordinate systems to represent the location of the point source target relative to the optical axis of the central eyelet. The number of processing elements in the network's hidden layer was also varied to determine the impact of these variations on the task of target location determination. A figure-of-merit (FOM) for the target location systems is developed to facilitate a direct comparison between the different optical and BPNN models. The results are useful in designing a MAVS tracker.