This paper discusses selected aspects of an MIT Lincoln Laboratory effort developing information fusion techniques for
biodefense decision-support tasks, involving biological standoff (lidar - light detection and ranging) sensors,
meteorology, as well as point sensors and potentially other battlespace sensing and contextual information. The
Spatiotemporal Coherence (STC) fusion approach developed in this effort combines phenomenology aspects with
approximate uncertainty measures to quantify corroboration between the information elements. The results indicate that
STC can significantly reduce false alarm rates. Meandering Plume and Background Simulation is one of two techniques
developed for ground-truth data generation. Beyond the detection realm, developed techniques include information-fusion
based plume mapping and propagation prediction.
This paper discusses some of the techniques developed at MIT Lincoln Laboratory for information fusion of lidar-based
biological standoff sensors, meteorology, point sensors, and potentially other information sources, for biodefense
applications. The developed Spatiotemporal Coherence (STC) fusion approach includes phenomenology aspects and
approximate uncertainty measures for information corroboration quantification. A supervised machine-learning
approach was also developed. Computational experiments involved ground-truth data generated from measurements and
by simulation techniques that were developed. The fusion results include performance measures that focus explicitly on
the fusion algorithms' effectiveness. Both fusion approaches enable significant false-alarm reduction. Their respective
advantages and tradeoffs are examined.
Disparity and uncertainty of information sources are both significant problems in information fusion. This paper investigates the problem of disparity in general, and in conjunction with FLASH - a hybrid information-fusion cognitive-processing approach we developed. Different forms of disparity are identified and their categorization is presented, and their implications on the information fusion processes are discussed. The issue of feature-level vs. decision-level fusion is investigated, and the methods of coping with disparity within FLASH are presented. Source uncertainty estimation techniques are discussed as well. Disparity studies and the results of computational experiments related to them are presented. These studies are suggestive of the potential of the FLASH hybrid approach for fusion of disparate information sources.
A portable and extensible multisensor testbed for long-term multi-point aerosol background data collections has been developed. The primary objective of the testbed is to support investigations related to the information fusion, machine-intelligence based CB decision support architectrure, now under development at MIT Lincoln Laboratory. This paper describes major design features of the testbed and concentrates on the analysis and the results of multiple indoor data collections. Specifically, two deployments of the testbed for extensive indoor data collection campaigns are described. The indoor background characterization results are presented.
This paper presents the progress of an ongoing research effort in multisource information fusion for biodefense decision support. The effort concentrates on a novel machine-intelligence hybrid-of-hybrids decision support architecture termed FLASH (Fusion, Learning, Adaptive Super-Hybrid) we proposed. The highlights of FLASH discussed in the paper include its cognitive-processing orientation and the hybrid nature involving heterogeneous multiclassifier machine learning and approximate reasoning paradigms. Selected specifics of the FLASH internals, such as its feature selection techniques, supervised learning, clustering, recognition and reasoning methods, and their integration, are discussed. The results to date are presented, including the background type determination and bioattack detection computational experiments using data obtained with a multisensor fusion testbed we have also developed. The processing of imprecise information originating from sources other than sensors is considered. Finally, the paper discusses applicability of FLASH and its methods to complex battlespace management problems such as course-of-action decision support.
This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and other, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-speciific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncatalogued agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.
This paper investigates methods of decision-making from uncertain and disparate data. The need for such methods arises in those sensing application areas in which multiple and diverse sensing modalities are available, but the information provided can be imprecise or only indirectly related to the effects to be discerned. Biological sensing for biodefense is an important instance of such applications. Information fusion in that context is the focus of a research program now underway at MIT Lincoln Laboratory. The paper outlines a multi-level, multi-classifier recognition architecture developed within this program, and discusses its components. Information source uncertainty is quantified and exploited for improving the quality of data that constitute the input to the classification processes. Several methods of sensor uncertainty exploitation at the feature-level are proposed and their efficacy is investigated. Other aspects of the program are discussed as well. While the primary focus of the paper is on biodefense, the applicability of concepts and techniques presented here extends to other multisensor fusion application domains.
This paper presents an alternative, computational intelligence based paradigm for biological attack detection. Conventional approaches to this difficult problem include sensor technologies and analytical modeling approaches. However, the processes that constitute the environmental background as well as those which occur as the result of an attack are extremely complex. This phenomenological complexity, in terms of both physics and biology aspects, is a challenge difficult to overcome by conventional approaches. In contrast to such approaches, the proposed approach is centered on automatic learning to discriminate between sensor signals that are in a normal range from those that are likely to represent a biological attack. It is argued that constructing machine learning methods robust enough to perform such a task is often more feasible than constructing an adequate model that could form a basis for bioattack detection. The paper discusses machine learning and multisensor information fusion methods in the context of biological attack detection in a subway environment, including recognition architecture and its components. However, the applicability of the proposed approach is much broader than the subway bioattack protection case, extending to a wide range of CBR defense applications.
This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.
The optical quadrature imaging technique, as derived and extended from microwave and laser radar quadrature detection techniques, provides an efficient method for obtaining phase information from a sample that has little or no amplitude contrast. We are able to resolve internal structures of a sample that are defined by relatively small refractive index differences without the use of dyes or stains, while using much lower light levels than conventional techniques. We have constructed a prototype system for imaging microscopic phase- only objects. In this paper, we present its capabilities, as well as the imaging and reconstruction methods used to obtain quantitative information about a sample.