19 October 2012 Modelling systems of weakly characterised sensors
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The modelling of the Automatic Target Detection, Recognition and Identification performance in systems of multiple sensors and/or platforms is important in several respects. For example, in the selection of sensors or sensor combinations of sufficient performance to achieve operational requirements or; for understanding how the system might be best exploited. To this end a simulation framework has been developed examining sensor options across different sensor types, parameterisations, search strategies, and applications. It uses Bayesian Decision Theoretic principles, along with simple sensor models and Monte-Carlo simulation, to derive the expected performance of single deployed sensors and of sensor combinations. The basic framework has been significantly extended to include recognition and identification problems along with the detection problem for which it was originally designed. The framework has also been expanded to treat cases in which the sensors are poorly characterised, and recommendations for parameterisation in this mode are made. The sensor system modelling framework has been applied to a number of illustrative problems. These range from simple target detection problems using sensors of differing performance or of different regional search schemes, through to examinations of: the number of measurements required to reach threshold performance; the effects of sensor measurement cost; issues relating to the poor characterisation of sensors within the system, and; the performance of a more elaborate combined detection and recognition sensor system. Generally, these results tend to show that the method is able to quantify qualitative expectations of performance, and is sufficiently powerful to highlight some unexpected aspects of operation.
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Chris J. Willis, Chris J. Willis, } "Modelling systems of weakly characterised sensors", Proc. SPIE 8540, Unmanned/Unattended Sensors and Sensor Networks IX, 854003 (19 October 2012); doi: 10.1117/12.963743; https://doi.org/10.1117/12.963743

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