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Multi-senor Data Fusion, Image-based Fusion, Data Fusion Via Non-linear Convolution, Situation Assessment

Sensor data fusion is the process of combining data collected from multi sensors of homogeneous or heterogeneous modalities to perform inferences that may not be possible using a single sensor. This process encompasses several stages to arrive at a sound reliable decision making end result. These stages include: senor-signal preprocessing, sub-object refinement, object refinement, situation refinement, threat refinement and process refinement. Every stage draws from different domains to achieve its requirements and goals. Popular methods for sensor data fusion include: ad-hock and heuristic-based, classical hypothesis-based, Bayesian inference, fuzzy inference, neural networks, etc. in this work, we introduce a new data fusion model that contributes to the area of multi-senor/source data fusion. The new fusion model relies on image processing theory to map stimuli from sensors onto an energy map and uses non-linear convolution to combine the energy responses on the map onto a single fused response map. This response map is then fed into a process of transformations to extract an inference that estimates the output state response as a normalized amplitude level. This new data fusion model is helpful to identify sever events in the monitored environment. An efficiency comparison with similar fuzzy-logic fusion model revealed that our proposed model is superior in time complexity as validated theoretically and experimentally.