Dynamic Data Driven Applications System Concept for Information Fusion

AbstractWe present a framework of Information Fusion (IF) using the Dynamic Data Driven Applications Systems (DDDAS) concept. Existing literature at the intersection of these two topics supports environmental modeling (e.g., terrain understanding) for context enhanced applications. Taking advantage of sensor models, statistical methods, and situation- specific spatio-temporal fusion products derived from wide area sensor networks, DDDAS demonstrates robust multi-scale and multi-resolution geographical terrain computations. We highlight the complementary nature of these seemingly parallel approaches and propose a more integrated analytical framework in the context of a cooperative multimodal sensing application. In particular, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between IF and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements – an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and identification

A Normalized Fractionally Lower-Order Moment Algorithm for Space-Time Adaptive Processing

A new space-time adaptive processing algorithm is proposed for clutter suppression in phased array radar systems. In contrast to the commonly used normalized least mean square (NLMS) algorithm which uses the second order moments of the data for adaptation, the proposed method uses the lower order moments of the data to adapt the weight coefficients. The normalization is also performed based on the data sample dispersion rather than the variance. Processing results using simulated and measured data show that the proposed algorithm converges faster than the NLMS algorithms in Gaussian and non-Gaussian clutter environments. It also provides better clutter suppression than the NLMS algorithm under heavy-tailed, impulsive, non-Gaussian environments. It in turn improves the target detection performance