DDDAS-based Joint Nonlinear Manifold Learning for Target Localization

Abstract

The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data to update models. When big data streams are available, signal processing can be used for situation awareness. However, many times, the physical world prevents sensor measurements availability which provides an opportunity to use heterogeneous sensor information; but methods are needed for data normalization, sampling alignment, and data mining. In this paper, we highlight a joint nonlinear manifold learning approach to that incorporates advances in machine learning, data fusion, and model-based simulation propagators. The context of the situation monitoring includes a multiple moving targets, a video sensor, and distributed signal sensors. The challenge is to determine the moving emitter amongst a cluttered scene during an experiment with large data streams. Resolving the signature triangulation of the emitter with that of the video sensor is both a nonlinear tracking problem as well as data learning issue. The paper presents the joint nonlinear manifold learning approach that is theoretically developed such sensor models could not only be the tracking scenario; but that of structure health monitoring as well as internet of things (IoT) examples. The theoretical analysis, heterogeneous data set, machine learning, and results are presented to showcase the importance of real-time DDDAS modeling updates of kinematic models.