DATA COMPRESSION WITH SUBBAND WALNUT

Data links are considered to be of major importance for the Unmanned Air Vehicle (UAV) in its role as a reconnaissance/surveillance platform. Secure, reliable and timely data communication will increase the acceptance of UAVs in the military community. The large amount of communication data needed for video images together with jamming-resistant overhead techniques may not permit timely transmission of images. Thus data compression techniques are needed. This proposal intends to investigate a data compression concept named Walsh Neural Network Unmanned Vehicle Image Transmission (WALNUT) based on vector quantization techniques. Vector quantization (VQ) is emerging as a leading and powerful data compression technique because of its ability to utilize peraceptually meaningful nonlinear correlation techniques. Competitive learning based on neural network (NN) technology applied to VQ brings effective adaptation to motion and scene changes and superior performance needed for near real time compression of image sequences obtained from UAVs operating in a military environment. Such a system has the potential of providing: (1) Economical quantization of pixel information, (2) fast compression and expansion techniques, and (3) protection against channel noise and jamming. In addition VQ with NN learning also inherently maps into parallel computing design makeing possible an even greater data communication speedup.