Abstract: Identification of maximum power scatters in an N-dimensional dataset generally requires two basic steps. The first step is to identify the max power scatters of the dataset and the second step removes neighboring power scatters (e.g., “hits”) of lower power. Current naïve approaches utilize an inefficient and computationally intensive brute force implementation which requires multiple comparisons of each initial “hit” power to all “hits” of lesser power. Such brute force implementations require 2×N×(M?1)! comparisons, where N is the number of dimensions and M is the number of “hits.” Embodiments of the present disclosure utilize vectorization to identify a plurality of neighboring hits for each max power scatter and removes the neighboring hits of lesser power that are within a predetermined isolation region. Advantageously, embodiments of the present invention perform M?1 comparisons.

Abstract: Identification of maximum power scatters in an N-dimensional dataset generally requires two basic steps. The first step is to identify the max power scatters of the dataset and the second step removes neighboring power scatters (e.g., “hits”) of lower power. Current naïve approaches utilize an inefficient and computationally intensive brute force implementation which requires multiple comparisons of each initial “hit” power to all “hits” of lesser power. Such brute force implementations require 2×N×(M?1)! comparisons, where N is the number of dimensions and M is the number of “hits.” Embodiments of the present disclosure utilize vectorization to identify a plurality of neighboring hits for each max power scatter and removes the neighboring hits of lesser power that are within a predetermined isolation region. Advantageously, embodiments of the present invention perform M?1 comparisons.