David Lawless

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We present and demonstrate a particle filtering approach to data fusion and situation assessment for military operations in urban environments. Our approach views such an environment as a physical system whose state vector is composed of a large number of both discrete and continuous variables representing properties of tracked entities. Inference on such(More)
We study the problem of monitoring goals, team structure and state of agents, in dynamic systems where teams and goals change over time. The setting for our study is an asymmetric urban warfare environment in which uncoordinated or loosely coordinated units may attempt to attack an important target. The task is to detect a threat such as an ambush, as early(More)
We describe a novel clustering approach for aggregating mobile (typically potentially hostile) units in cluttered urban environments. The approach consists of a suite of spatiotemporal clustering algorithms that leverage the wealth of military sensor data available to provide insight into "what is strange" about a given situation, without knowing beforehand(More)
1 Charles River Analytics, Inc., 625 Mount Auburn St., Cambridge, MA 02138, USA. Email: {sdas, dlawless}@cra.com Abstract We present a Network-based Truth Maintenance System (NTMS) for problem solvers based on Bayesian belief network (BN) technology. BN technology has been proven to be effective in various domains, e.g. assessing battlefield situations,(More)