Linear inversion of ban limit reflection seismograms
SIAM Journal on Scientific and Statistical Computing
Condition numbers of random matrices
Journal of Complexity
A Solution to the Next Best View Problem for Automated Surface Acquisition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Microwave Mobile Communications
Microwave Mobile Communications
Convex Optimization
Wireless Communications
ACM SIGGRAPH 2005 Papers
ICI mitigation for pilot-aided OFDM mobile systems
IEEE Transactions on Wireless Communications
A robust timing synchronization design in OFDM systems-part I: low-mobility cases
IEEE Transactions on Wireless Communications
Robust timing synchronization design in OFDM systems - Part II: high-mobility cases
IEEE Transactions on Wireless Communications
Sparse representations in unions of bases
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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In this paper we consider a mobile cooperative network that is tasked with building a map of the spatial variations of a parameter of interest, such as an obstacle map or an aerial map. We propose a new framework that allows the nodes to build a map of the parameter of interest with a small number of measurements. By using the recent results in the area of compressive sensing, we show how the nodes can exploit the sparse representation of the parameter of interest in the transform domain in order to build a map with minimal sensing. The proposed work allows the nodes to efficiently map the areas that are not sensed directly. To illustrate the performance of the proposed framework, we show how the nodes can build an aerial map or a map of obstacles with sparse sensing. We furthermore show how our proposed framework enables a novel non-invasive approach to mapping obstacles by using wireless channel measurements.