Near-Field MUSIC Algorithm for Target Localization

The problem of detecting and localizing an unknown number of point-like targets from near-field single-snapshot electromagnetic scattered field data is addressed. Classical imaging algorithms give resolution that is limited by the measurement aperture and the frequency band. Super-resolving algorithms can beat the related Rayleigh's limits, but the data correlation matrix has to be full rank, which does not hold true for single-snapshot data. This drawback can be remedied by adopting some smoothing procedure. However, for near-field configurations, smoothing strategies cannot be exploited since the data matrix lacks the required Vandermonde structure. In order to cope with this issue, a modified version of the multiple signal classification algorithm, which relies on a preprocessing that restores the data matrix rank, is introduced and assessed via numerical analysis.