The coordination generalized particle model—An evolutionary approach to multi-sensor fusion

The rising popularity of multi-source, multi-sensor networks supporting real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on the coordination generalized particle model (C-GPM) which is founded on the laws of physics. C-GPM treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed C-GPM approach can model the autonomy of as well as the social coordinations and interactive behaviors among sensors in a decentralized paradigm. Although the other existing evolutionary algorithms have their respective advantages, they may not be able to capture the entire dynamics inherent in the problem, especially those that are high-dimensional, highly nonlinear, and random. The C-GPM approach can overcome such limitations. We develop the C-GPM approach as a physics-based evolutionary approach that can describe such complex behaviors and dynamics of multiple sensors.