Patent ID: 7409323
Filing Date: 2008-08-05
Classification: G01S

Abstract:
1. A method for characterizing a plurality of data sets in a Cartesian space, said method comprising the steps of: utilizing at least one sonar array to produce a plurality of data sets; reading in data points from a first data set from said plurality of data sets; counting said data points to determine a total number N of said data points; creating a polygon envelope containing said data points of said first data set; forming a grid to partition said polygon envelope into a plurality of cells; testing for adequacy of a first prediction model and a second prediction model for predicting an expected number of said plurality of cells which contain at least one of said data points if said first data set were randomly dispersed; if said first prediction model and said second prediction model from said step of testing are found to be adequate then classifying said first data set as small if said number N is less than fifteen, classifying said first data set as large if said number N is at least fifteen, and utilizing said first data set classification to determine whether to use said first prediction model or said second prediction model for said step of predicting; determining a measured number of said plurality of partitions which actually contain at least one of said data points; determining a range of values utilizing said determined one of said first prediction model and said second prediction model such that if said measured number is within said range of values, then said first data set is characterized as random in structure, and if said number is outside of said range of values, then said first data set is characterized as non-random; and labeling said first data set in accordance with its characterization as being random in structure or non-random.