Integral in locating point scatterers in Synthetic Aperture Radar (SAR) data is the ability to match location
estimates in each dimension. This is due in some sense to the fact that the fundamental theorem of algebra nds
unique locations only in one dimension. In SAR images this involves at least a search of four possible combination
for two scatterers. In a set of multiple elevation SAR (3-D) images with more than one scatterer combinations
increase dramatically. The paper examines several suboptimal methods and their e¢ ciency matching scatterers
in one or more dimensions to their unique locations compared to the (un-achievable) exhaustive search. Many
heuristic methods exist in two dimension (location maxima, alternating maximization in each dimension) and
some (radar tracking) methods exist in three dimensions (Munkres, probabilistic maximization). Algorithms
range from simply selecting maximums (easy in 2D; complex in multiple images, 3D) to multidimensional con-
strained interpolations. In some algorithms the extra degrees of freedom present in two dimensional localization
are exploited to increase accuracy. These methodologies can also be extended to three dimensions. The paper
examines proposed combinations of these especially suitable to the 3-D SAR problem. Simulations with results
for di¤erent algorithms compare promising alternatives to solve this problem.
Matthew Pepin, "The efficency of algorithms to match unique scatterer locations in joint 3D azimuth and elevation synthetic aperture radar scenarios," Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 102010H (Presented at SPIE Defense + Security: April 13, 2017; Published: 28 April 2017); https://doi.org/10.1117/12.2262867.
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