Thanuka L. Wickramarathne

Judicious processing of High-Dimensional Sensing (HDS) data streams is often the first step in a data-driven decision-support system. However, how one goes about processing large amounts of incoming HDS data streams for enhancing situational awareness (SA) is a non-trivial, and often challenging task due to the interwoven nature of operational environments, especially in complex surveillance applications (e.g., predictive policing, urban reconnaissance). Toward addressing this challenge, a novel data processing framework called ENSURE (ENhanced Situational Understanding with Ubiquitous-sensing REsources) is presented. In particular, by using density-based clustering in tandem with variable length sequence decoding methods, ubiquitous HDS data streams are clustered and then processed for early identification of ‘events’ and their sequences of interest to a (surveillance) task at hand. Then, by leveraging notions in data association, ENSURE ‘tracks’ the occurrence of such event sequences and their evolution over time to ‘perceive’ and ‘comprehend’ what’s happening in the operational environment. Therefore, ENSURE can be interpreted as an information fusion approach for HDS systems for object recognition, classification, tracking, and decision-making tasks. Use of ENSURE is illustrated via a simulated experiment to provide insights into its behavior, performance and sensitivity to parameters.

Developing technical capability that enables the utilization of ubiquitous sensing for situational awareness will likely catalyze a paradigm shift in data fusion, especially in light of recent advances in computing and data-driven techniques. Via the use of appropriate models, the well-entrenched data fusion methods can be utilized to mitigate some of the current challenges that are hindering the progress in this direction. However, due to the inherent challenges associated with ubiquitous sensing and randomness of data acquisition, adequate evaluation of advanced detection and sensor fusion systems remains a major challenge that hampers the progress in this direction. In this paper, a simulation approach for generating a synthetic dataset for comprehensive evaluation of Situational Awareness Systems (SASs) that utilize ubiquitous sensing for data acquisition is presented. With a particular emphasis on recording and organizing spatio-temporal observations, the steps towards construction of a comprehensive dataset that takes into account various spatiotemporal parameters corresponding to location, hazards and detectors is presented. The presented approach is then utilized to illustrate a simplified evaluation of an efficient data storage and inference system designed for asynchronous detections that are characteristic of ubiquitous sensed SASs.

A general method to discover and embed variable orders of dependencies in a network representation.

We study convergence of iterated belief revision in complex fusion environments, which may consist of a network of soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors and where agent communications may be asynchronous and the link structure may be dynamic. In particular, we study the problem in which network agents exchange and revise belief functions (which generalize probability mass functions) and are more geared towards handling the uncertainty pervasive in soft/hard fusion environments. We focus on belief revision in which agents utilize a generalized fusion rule that is capable of generating a rational consensus. It includes the widely used weighted average consensus as a special case. By establishing this fusion scheme as a pool of paracontracting operators, we derive general convergence criteria that are relevant for a wide range of applications. Furthermore, we analyze the conditions for consensus for various social networks by simulating several network topologies and communication patterns that are characteristic of such networks.

In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies `general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence. such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex fusion environments, which may involve communication delays, ad-hoc paths, etc. In this paper, we analyze consensus of a Dempster-Shafer theoretic (DST) fusion operator by formulating the consensus problem as finding common fixed points of a pool of paracontracting operators. Due to its DST basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DST models.

Dempster-Shafer (DS) belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. The recent work on the DS theoretic (DST) conditional approach, which is based on the Fagin-Halpern (FH) DST conditionals, appears to demonstrate the suitability of DS theory for incorporating both soft (generated by human-based sensors) and hard (generated by physics-based sources) evidence into the fusion process. However, the computation of the FH conditionals imposes a significant computational burden. One reason for this is the difficulty in identifying the FH conditional core, i.e., the set of propositions receiving nonzero support after conditioning. The conditional core theorem (CCT) in this paper redresses this shortcoming by explicitly identifying the conditional focal elements with no recourse to numerical computations, thereby providing a complete characterization of the conditional core. In addition, we derive explicit results to identify those conditioning propositions that may have generated a given conditional core. This “converse” to the CCT is of significant practical value for studying the sensitivity of the updated knowledge base with respect to the evidence received. Based on the CCT, we also develop an algorithm to efficiently compute the conditional masses (generated by FH conditionals), provide bounds on its computational complexity, and employ extensive simulations to analyze its behavior.

In many contexts, one is confronted with the problem of extract ing information from large amounts of different types soft data (e.g., text) and hard data (from e.g., physics-based sensing systems). In handling hard data, signal and data processing offers a wealth of methods related to modeling, estimation, tracking, and inference tasks. However, soft data present several challenges that necessitate the development of new data processing methods. For example, with suitable statistical natural language processing (NLP) methods, text can be converted into logic statements that are associated with various forms of associated uncertainty related to the credibility of the statement, the reliability of the text source, and so forth. In combining or fusing soft data with either soft or hard data, one must deploy methods that can suitably preserve and update the uncertainty associated with the data, thereby providing uncertainty bounds related to any inferences regarding semantics. Since standard Bayesian probabilistic approaches have problems with suitably handling uncertain logic statements, there is an emerging need for new methods for processing heterogeneous data. In this paper, we describe a framework for fusing soft and hard data based on the Dempster-Shafer (DS) belief theoretic approach which is well-suited to the task of capturing the types of models and uncertain rules that are more typical of soft data. Since the effectiveness of traditional DS methods has been hampered by high computational requirements, we base the processing framework on our new conditional approach to DS theoretic evidence updating and fusion. We address the issue of laying the foundation for a theoretically justifiable, and computationally efficient framework for fusing soft and hard data taking into account the inherent data uncertainty such as reliability and credibility. Moreover, we present an illustrative ex ample that highlights the potential for the DS conditional approach for fusing heterogeneous data.