Automated Radar Waveform Clustering Using Quantile Thresholds
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Solution Overview
Problem
Existing algorithms for clustering radar waveforms struggle with qualitative and symbolic data, often grouping nearly identical descriptions as similar due to their complexity and variability, leading to ambiguities and manual detection challenges.
Innovation Solution
An automated method using a combination of distance calculation and clustering algorithms to group nearly identical radar waveform descriptions, employing iterative merging steps and threshold-based quantile separation to form homogeneous groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated clustering algorithms are used to group waveform descriptions, then productivity is improved, but manufacturing precision deteriorates because algorithms group data that are very far apart as similar
Solution Approach 1:
The patent segments the clustering process into multiple hierarchical levels. First, descriptions are grouped into initial clusters using a first algorithm. Then, representative descriptions from each cluster are formed and used to create second-level clusters. This multi-level segmentation allows the system to process large datasets automatically while maintaining precision by considering both local similarities (within clusters) and global structure (between clusters).
Solution Approach 2:
The patent introduces representative descriptions as intermediary elements between individual waveform descriptions and final clusters. Each representative description is formed by selecting the description farthest from other descriptions within its cluster, serving as a mediator that captures the essential characteristics of the group. These intermediaries enable more accurate higher-level clustering while reducing computational complexity.
2Device complexity
If traditional clustering algorithms are used, then device complexity is reduced, but difficulty of detecting and measuring increases because they cannot handle qualitative and symbolic data
Solution Approach 1:
The patent employs universal distance calculation methods that can handle multiple data types including qualitative variables, intervals, and histograms simultaneously. The distance calculation framework is designed to work with symbolic data from waveform descriptions while maintaining compatibility with traditional numerical clustering algorithms. This universality allows the system to process diverse waveform characteristics without requiring separate specialized algorithms for each data type.
3Measurement precision
If manual searching is used to find identical descriptions, then measurement precision is improved, but loss of time increases due to the large number of waveforms and complexity of descriptions
Solution Approach 1:
The patent performs preliminary clustering actions that organize waveform descriptions into groups before final identification. By pre-grouping descriptions based on their characteristics and forming representative descriptions, the system prepares the data structure in advance to facilitate rapid and accurate identification of identical or similar waveforms. This preliminary organization significantly reduces the time required for subsequent analysis while maintaining detection precision.
Data Source
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AI summary
Method for grouping N > 3 descriptions D1, ..., DN of waveforms included in a database, said method comprising the following steps: A. for each description Di, i ∈ [1; N] of the database: -calculate a symbolic data distance Δik between said description Di and each of the k other descriptions Dk, with k ∈ [1; N], k ≠ i, -determine the description Dj, i ≠ j , for which the distance Δij is minimal, and form a pair Dij from the descriptions Di and Dj, B. let Q∈ℕ>2, form Q - 1 thresholds S1, ...,SQ-1 such that, for p ranging from 1 to Q - 1, the p/Q-th of the pairs Dij each have a distance Δij less than the threshold Sp, C. form Q sets, C1, ..., CQ, of pairs Dij from the Q - 1 thresholds S1, ...,SQ-1 D.for x ranging from 1 to Q: perform at least one iterative merging step grouping the descriptions of the pairs Dij included in the set Cx into groups Gx according to the threshold Sx, said at least one iterative merging step not grouping the description(s) belonging to a group Gy formed from the sets Cy with y<x lorsque x > 1, so as to obtain groups Ga each having a maximum distance dmax,x between two descriptions which are different from those of the other groups Gb, b ≠ a, a set formed by all the groups obtained being called a data partition.