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Resource-Constrained Adaptive Search for Sparse Multi-Class Targets with Varying Importance by Newstadt, Gregory E.; Mu, Beipeng; Wei, Dennis; How, Jonathan P.; Hero, Alfred O. is a scholarly article available to read on EtoBox.

What is Resource-Constrained Adaptive Search for Sparse Multi-Class Targets with Varying Importance about?

In sparse target inference problems it has been shown that significant gains can be achieved by adaptive sensing using convex criteria. We generalize previous work on adaptive sensing to (a) include multiple classes of targets with different levels of importance and (b) accommodate multiple sensor models. New optimization policies are developed to allocate a limited resource budget to simultaneously locate, classify and estimate a sparse number of targets embedded in a large space. Upper and lower bounds on the performance of the proposed policies are derived by analyzing a baseline policy, which allocates resources uniformly across the scene, and an oracle policy which has a priori knowledge of the target locations/classes. These bounds quantify analytically the potential benefit of adaptive sensing as a function of target frequency and importance. Numerical results indicate that the proposed policies perform close to the oracle bound (<3dB) when signal quality is sufficiently high (e.g.~performance within 3 dB for SNR above 15 dB). Moreover, the proposed policies improve on previous policies in terms of reducing estimation error, reducing misclassification probability, and increa

Author

Newstadt, Gregory E.; Mu, Beipeng; Wei, Dennis; How, Jonathan P.; Hero, Alfred O.

Published

2014

Language

EN