Computational Framework for Target Tracking Information Fusion Problems

In this work, we propose computationally tractable techniques for extracting valuable information from diverse data sources collected by multiple sensors in a variety of formats (visual, sonar, quantitative, qualitative, social information, etc.). More specifically, we develop an integrated approach consisting of two algorithms for extracting information and achieving a consensus-based, robust solution. The first algorithm extracts solutions from sensors within each data source, whereas the second algorithm reaches a compromise among the generated solutions from the previous algorithm across all data sources. To accomplish these goals, we initially transform the multisensor multitarget tracking problem (MSMTT) problem into a multidimensional assignment problem. Subsequently, we introduce a decomposition-based multisensor recursive approach referred to as a revised multisensor recursive algorithm, which can efficiently deliver a robust solution for each single data source MSMTT problem. In the second algorithm, we extend our methodology to the multisource MSMTT problem by introducing a connection-based symmetric nonnegative matrix factorization technique, which is shown to be computationally feasible and efficient in obtaining high-quality solutions.