Wave-based signal processing
In many ways the electromagnetics and signal processing communities are at similar levels of development; in both fields there are many mature techniques available to address problems of interest. It is a good time to cross-fertilize between these fields. Specifically, understanding and physical insight from electromagnetics may be used to buttress the utility of signal processing algorithms. Prof. Felsen recognized this opportunity about a decade ago, and in this paper we report on the state of the art of this field. We address the problem of adaptive sensing based on multiple sensor modalities. Assume we consider a multi-modality sensing problem, that physical insight is available, and that a certain quantity of data has been collected thus far from the scene under test. The objective is to optimally choose what new multi-sensor data should be collected, with the objective of maximizing classification performance while minimizing sensing costs (e.g., battery use, time, etc.). It is desirable that the algorithm be non-myopic, in the sense that it accounts for the immediate utility of a given sensor, as well as the properties of sensing over a discounted infinite horizon. For example, a sequence of inexpensive measurements may have the same utility as a single expensive measurement, and the inexpensive measurements will only be preferred if the algorithm operates non-myopically (i.e., the algorithm addresses long-term performance, looking ahead, rather than myopically and greedily choosing the sensor that yields the immediate best performance, independent of cost). The problem is solved via a partially observable Markov decision process (POMDP), and in this talk we explain how the underlying wave physics is employed to improve POMDP sensing performance. The basic framework has been applied successfully to several sensing scenarios, and in this talk we focus on the specific problem of multi-modality sensing of buried land mines. © 2006 IEEE.
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