KEYWORDS: Sensors, Signal processing, Data communications, Image processing, Data processing, Telecommunications, Data fusion, Infrared sensors, 3D image processing, Unmanned aerial vehicles
In the electro-optical sensors and processing in urban operations (ESUO) study we pave the way for the European Defence Agency (EDA) group of Electro-Optics experts (IAP03) for a common understanding of the optimal distribution of processing functions between the different platforms. Combinations of local, distributed and centralized processing are proposed. In this way one can match processing functionality to the required power, and available communication systems data rates, to obtain the desired reaction times. In the study, three priority scenarios were defined. For these scenarios, present-day and future sensors and signal processing technologies were studied. The priority scenarios were camp protection, patrol and house search. A method for analyzing information quality in single and multi-sensor systems has been applied. A method for estimating reaction times for transmission of data through the chain of command has been proposed and used. These methods are documented and can be used to modify scenarios, or be applied to other scenarios. Present day data processing is organized mainly locally. Very limited exchange of information with other platforms is present; this is performed mainly at a high information level. Main issues that arose from the analysis of present-day systems and methodology are the slow reaction time due to the limited field of view of present-day sensors and the lack of robust automated processing. Efficient handover schemes between wide and narrow field of view sensors may however reduce the delay times. The main effort in the study was in forecasting the signal processing of EO-sensors in the next ten to twenty years. Distributed processing is proposed between hand-held and vehicle based sensors. This can be accompanied by cloud processing on board several vehicles. Additionally, to perform sensor fusion on sensor data originating from different platforms, and making full use of UAV imagery, a combination of distributed and centralized processing is essential. There is a central role for sensor fusion of heterogeneous sensors in future processing. The changes that occur in the urban operations of the future due to the application of these new technologies will be the improved quality of information, with shorter reaction time, and with lower operator load.
Recent field trials have shown that modulated retro-reflective (MRR) optical communications is a potentially feasible technique for applications demanding high data rates. Data rates over 10 Mbit/s has been demonstrated with Multiple Quantum Well (MQW) modulators in experimental MRR systems. An MQW-based MRR has a variable reflectance and information can thus be transmitted to the receiver by modulating the intensity of the reflected signal. However, current experimental systems normally use binary modulation (e.g. on-off keying) and the data rate is then strictly limited by the modulation speed of the retro-reflector. Instead, by employing a multiple-level modulation scheme the data rate can be increased substantially. Herein, we discuss if the use of different signal processing techniques, commonly used in radio communication systems, may improve the robustness and capacity of MRR free-space optical communication links. Techniques of interest are mainly error-correcting codes, link adaptation and high-level modulation schemes. Furthermore, we apply some of these techniques on measured channel data that has been collected in recent field trials. Through simulations we demonstrate the potential gains that can be achieved through the use of link adaptation and multiple-level modulation. Finally, a brief comparison with competing radio techniques is given.
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