Compressive Sensing Based Inference in Distributed Networks

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ObjectiveThe objective of this program is to develop efficient algorithms and obtain performance boundsfor decentralized inference in the reduced dimensional subspace obtained via Compressive Sens-ing based measurement schemes in resource-constrained distributed networks.Intellectual MeritThe intellectual merit of the proposed program is that it will advance the theory of CompressiveSensing to resource-constrained distributed systems with multiple measurement vectors. Whilethere is a considerable amount of work in the literature for Compressive Sensing based signalprocessing with a single measurement vector, its extension to multiple measurement vectors inresource-constrained distributed systems is still at its infancy. The transformative nature of theproposed research lies in its potential to identify and solve decentralized inference problems basedon low dimensional sampling schemes exploiting structured models of the multiple measurementvectors collected at distributed nodes.Broader ImpactsThe proposed research will have direct impact on advancing the theory and practice, as well ason education and training. The broader impacts are: 1) Development of efficient low dimensional signal processing techniques for decentralized inference in resource-constrained distributed networks exploiting structured properties (sparsity as well as models that go beyond the simple sparsity model) of large data sets. 2) Efficient design approaches for distributed inference networks with a wide variety of applications including environmental monitoring, remote sensing, radar networks, cognitiveradio networks and bio-medical applications with high dimensional data. 3) Development of graduate level courses on signal processing and inference for large data sets. Mentoring of graduate student in research methods.