The objective of this program is to develop efficient algorithms and obtain performance bounds for decentralized inference in the reduced dimensional subspace obtained via Compressive Sens- ing based measurement schemes in resource-constrained distributed networks.
Intellectual Merit
The intellectual merit of the proposed program is that it will advance the theory of Compressive Sensing to resource-constrained distributed systems with multiple measurement vectors. While there is a considerable amount of work in the literature for Compressive Sensing based signal processing with a single measurement vector, its extension to multiple measurement vectors in resource-constrained distributed systems is still at its infancy. The transformative nature of the proposed research lies in its potential to identify and solve decentralized inference problems based on low dimensional sampling schemes exploiting structured models of the multiple measurement vectors collected at distributed nodes.
Broader Impacts
The proposed research will have direct impact on advancing the theory and practice, as well as on 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, cognitive radio 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.
