Multiple wavelength sensing array design

We design finite antenna arrays for far-field sensing at multiple wavelengths, under two design paradigms. The first design paradigm is optimized for collection of measurements at multiple wavelengths, fusing these together for joint inference over an underlying scene. The second design paradigm is robust, in a sense that it is guaranteed to allow good inference over the scene at any one single wavelength at a time. We quantify inference quality via the D-Bayes optimality criterion and limit the design space by restricting the number of allowed sensors and the positions where these can be placed. We show that the resulting combinatorial optimization problems are instances of problems in a class known to have efficient guaranteed approximation algorithms, namely submodular optimization problems, and showcase the design of arrays under both paradigms utilizing simple greedy selection algorithms, and state-of-the-art robust submodular maximization algorithms.