3D Runs Test for Sonar Randomness Classification
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Solution Overview
Problem
Existing sonar signal processing methods fail to accurately classify data sets with a small number of measurements, often mislabeling nonrandom distributions as random, particularly in naval sonar systems where distinguishing random noise from potential vessel signals is critical.
Innovation Solution
A multistage method involving a three-dimensional runs test is employed, where data points are scored and partitioned into smaller subspaces, with a predefined route passing through each subspace to generate a series of ones and zeros, determining the number of runs and using statistical analysis to assess randomness, including Gaussian statistics and multiple correlation tests to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If large sample statistical methods are used to classify small data sets, then the classification process is simplified, but the accuracy deteriorates and nonrandom distributions are mislabeled as random
Solution Approach 1:
The patent segments the classification process into multiple stages: first applying a runs test for initial classification, then applying additional statistical tests (such as correlation tests, spectral analysis, or higher-order moment tests) to borderline or ambiguous cases. This multi-stage segmentation allows simple classification for clear cases while providing deeper analysis for problematic cases, thereby maintaining both simplicity and accuracy.
Solution Approach 2:
The patent changes the parameters used for classification by employing multiple different statistical parameters and tests rather than relying on a single large-sample parameter. By using diverse parameters (e.g., run lengths, correlation coefficients, spectral density ratios) that are appropriate for small samples, the classification accuracy improves while the process remains manageable through systematic parameter evaluation.
2Ease of operation
If traditional randomness assessment methods are applied to sparse data, then the analysis is straightforward, but false alarms increase due to incorrect classification
Solution Approach 1:
The patent performs preliminary analysis using the runs test before proceeding to more complex classification. This preliminary action identifies obvious random and nonrandom cases early, allowing straightforward classification for clear cases while triggering more rigorous analysis only for ambiguous cases. This preliminary filtering reduces false alarms without sacrificing analysis simplicity for clear cases.
Solution Approach 2:
The patent incorporates feedback mechanisms where the results of initial classification inform subsequent analysis steps. If the runs test produces ambiguous or borderline results, the system automatically triggers additional statistical tests and re-evaluation. This feedback loop ensures that potentially erroneous classifications are caught and corrected, thereby reducing false alarms while maintaining operational simplicity through automated decision rules.
3Productivity
If automated classification systems are used for sonar signal processing, then productivity increases, but the ability to detect subtle nonrandom patterns in sparse data decreases
Solution Approach 1:
The patent implements a dynamic classification system that automatically adjusts the depth and type of analysis based on data characteristics. For clear random patterns, the system uses simple runs test classification for high throughput. For ambiguous or sparse data showing potential nonrandom patterns, the system dynamically activates additional statistical tests and analysis stages. This dynamic adaptation maintains high productivity for straightforward cases while ensuring precise detection of subtle nonrandom patterns when needed.
Solution Approach 2:
The patent adds another dimension to the analysis by incorporating multiple types of statistical tests that examine different aspects of the data. Rather than relying on a single throughput-optimized algorithm, the system uses runs tests for speed, correlation tests for pattern detection, spectral analysis for periodicity, and other dimensional approaches. This multi-dimensional analysis ensures subtle nonrandom patterns are detected while maintaining overall productivity through selective application.
Data Source
AI summary
A multi-stage method is provided for automatically characterizing data sets containing data points which are each defined by measurements of three variables as either random or non-random. A three-dimensional Cartesian volume which is sized to contain all of a total number N of data points in the data set which is to be characterized. The Cartesian volume is partitioned into equal sized cubes, wherein each cube may or may not contain a data point. A predetermined route is defined that goes through every cube one time and scores each cube as a one or a zero thereby producing a stream of ones and zeros. The number of runs is counted and utilized to provide a Runs Test which predicts if the N data points in any data set are random or nonrandom. Additional tests are used in conjunction with the Runs Test to increase the accuracy of characterization of each data set as random or nonrandom.