Enhanced Neural Network for Target Recognition Improvement System

To address the Army’s need for advanced target recognition algorithms in gun-launched munitions, Intellisense Systems, Inc. (ISI) proposes to develop a new Enhanced Neural Network for Target Recognition Improvement (ENNTRI) system, based on deep neural network (DNN)-based sensor image enhancement, fusion, and automatic target recognition. Specifically, the innovation in flexible and computationally efficient DNN-based automatic target enhancement and recognition framework and its implementation on COTS accelerated computing hardware (such as GPU and FPGA) will enable the ENNTRI system to overcome the resolution limitations of onboard sensors and improve the performance of target recognition in terms of accuracy. As a result, ENNTRI offers real-time target recognition capabilities in a compact, power-efficient, hardware-accelerated and ruggedized package suitable to 155 mm artillery size, weight, power, cost, shock, and temperature constraints and operational requirements (extended slant ranges and large field-of-view). In Phase I, we will develop and prototype ENNTRI algorithms on representative hardware for testing and demonstration of improved target recognition capabilities. In Phase II, we will further mature the ENNTRI system, integrate it with a representative gun-fired munition, and test it at a government test range to demonstrate its multi-target discrimination ability within the timing required in multiple environmental conditions.