Detection, dependence modeling, heterogeneous sensing, model selection, sensor fusion, information fusion.
Electrical and Computer Engineering
In this paper, we consider the problem of detection for dependent, non-stationary signals where the non-stationarity is encoded in the dependence structure. We employ copula theory, which allows for a general parametric characterization of the joint distribution of sensor observations and, hence, allows for a more general description of inter-sensor dependence. We design a copula-based detector using the Neyman-Pearson framework. Our approach involves a sample-wise copula selection scheme, which for a simple hypothesis test, is proved to perform better than previously used single copula selection schemes. We demonstrate the utility of our copula-based approach on simulated data, and also for outdoor sensor data collected by the Army Research Laboratory at the US southwest border.
H. Hao, A. Subramanian, P. K. Varshney, and T. Damarla, "Fusing heterogeneous data for detection under non-stationary dependence," 2012 15th International Conference on Information Fusion (FUSION 2012), pp. 1792-9, 2012.