Summary
The identification of faulty elements in large (hundreds to thousands of elements) antenna arrays, e.g., radiotelescopes, from measurement of the radiation field is an important nasty problem in Applied Electromagnetics [1], [2]. Given the set of (complex) feeding currents and radiator effective heights, the state of a faulty array can be represented by a point in the lattice {0, 1}N, where N is the number of radiators, and the states “0” and “1” denote a faulty and a working antenna, respectively. One is led to the minimization of a functional, measuring the distance between two M—dimensional vectors, representing the measured data, and the fields (intensities) produced by all possible states of the array, respectively. The measured data can be either fields (complex, amplitude and phase), or intensities (real, squared amplitudes), leading respectively to linear and nonlinear minimization problems.
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Castaldi, G., Pierro, V., Pinto, I.M. (1999). Mean-Field Neural Networks for Antenna Array Diagnostics. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-98. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0811-5_17
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DOI: https://doi.org/10.1007/978-1-4471-0811-5_17
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