Radio Wave Target Detection Using Cluster Centroid Association
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Solution Overview
Problem
Existing detection systems struggle to accurately determine the position of targets, particularly in complex environments, and fail to effectively capture biological information from multiple individuals using radio wave sensors.
Innovation Solution
A detection system comprising an obtainer, generator, classifier, centroid calculator, associator, and combiner, which processes radio wave signals to generate scattered points, classify them into clusters, calculate centroids, and associate these centroids with targets, improving positional accuracy and biological information capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scattered points are generated from reception signals without clustering, then the detection process is simple, but the positional accuracy and stability are insufficient
Solution Approach 1:
The patent segments scattered points into clusters based on temporal and spatial proximity. Points generated during consecutive frames are grouped into clusters, and centroids are calculated for each cluster. This segmentation allows the system to process multiple points systematically while improving positional accuracy through cluster-based centroid calculation rather than using individual scattered points alone.
Solution Approach 2:
The patent performs preliminary actions by storing scattered points in memory before final position determination. Scattered points from multiple frames are accumulated and stored, then processed into clusters and centroids. This preliminary accumulation allows the system to leverage historical data for more accurate position estimation, improving measurement precision without requiring complex real-time processing.
2Stability of the object's composition
If only current scattered points are used for position detection, then the response time is fast, but positional stability over time is insufficient
Solution Approach 1:
The patent implements continuous accumulation of scattered points in memory across multiple frames. Instead of processing only current points, the system continuously stores and processes points from consecutive frames, creating a continuous history of position data. This continuous action enables the system to maintain positional stability over time by referencing historical data while still responding to current measurements.
Solution Approach 2:
The patent performs preliminary storage of scattered points in memory before final position determination. By accumulating points from previous frames in advance, the system prepares historical data that can be used for stable position estimation. This preliminary action allows the system to balance time responsiveness with positional stability by having pre-computed cluster centroids available for immediate use.
3Measurement precision
If multiple scattered points from different frames are processed without clustering, then the computational load is low, but the ability to identify stable target positions is reduced
Solution Approach 1:
The patent segments scattered points into clusters based on temporal and spatial proximity. Points generated during consecutive frames are grouped into clusters, and centroids are calculated for each cluster. This segmentation allows the system to process multiple points systematically while improving positional accuracy through cluster-based centroid calculation rather than using individual scattered points alone.
Solution Approach 2:
The patent merges scattered points from multiple frames into unified clusters. By combining points that are spatially and temporally close, the system creates consolidated cluster representations of target positions. This merging process improves target position identification accuracy by aggregating information from multiple frames while maintaining computational efficiency through structured combination rather than exhaustive processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of detecting target positions and capturing biological information by stabilizing cluster associations over time, reducing errors, and improving the reliability of detection.
Implementation Method 1
a sensor that emits a radio wave from a transmitting antenna to a target and receives a reflected radio wave from the target via a plurality of receiving antennas
Data Source
AI summary
A detection system includes an obtainer, a generator, a classifier, a centroid calculator, associator, and combiner. The obtainer obtains a reception signal from a radio wave sensor. The generator generates a scattered point including positional information of a target, based on the reception signal. The classifier classifies a scattered point group into one or more clusters. The scattered point group is a set of scattered points generated by the generator during a predetermined length of time. The centroid calculator calculates a centroid for each of the one or more clusters classified by the classifier. The associator associates any of the one or more clusters classified by the classifier with the target. The combiner combines, as the scattered point, the centroid calculated by the centroid calculator during a preceding or earlier period with the scattered point group.