Multi-Dimensional Sensing Apparatus for Object Location Detection
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Solution Overview
Problem
Existing methods for object location detection in multi-dimensional spaces face inefficiencies due to long scanning times and poor discrimination of objects in large volumes, especially when the data volume from the background overwhelms the object data, leading to detection failures and increased response times.
Innovation Solution
A method that configures a multi-dimensional sensing apparatus to generate multiple sets of sensed data at different resolutions, combines these data sets, performs averaging and comparison operations to enhance signal-to-noise ratios, and uses subtraction operations to create motion indication tables, allowing for efficient location prediction and background noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the scanning resolution is increased to improve object discrimination, then the detection precision is improved, but the scanning time becomes too long causing detection failure for motion traces
Solution Approach 1:
The patent divides the multi-dimensional space into multiple sub-spaces and performs scanning in a hierarchical manner. First, a coarse scan identifies potential object regions, then refined scans are performed only on those specific sub-spaces. This segmentation approach maintains high detection precision for objects while significantly reducing the total scanning time by avoiding exhaustive scanning of the entire space.
2Productivity
If the scanning resolution is reduced to shorten scanning time, then the response time is improved, but the discrimination of neighboring objects fails
Solution Approach 1:
The patent implements a dynamic scanning strategy where the resolution and scanning scope are adjusted based on detected object characteristics. After initial detection, the system dynamically focuses scanning resources on regions containing objects, increasing local resolution only where needed. This dynamic adaptation maintains high scanning speed while ensuring sufficient discrimination capability for detected objects.
3Area of stationary object
If the multi-dimensional space volume grows large, then the coverage area is improved, but the response time of sensing apparatus lengthens due to growing data volume
Solution Approach 1:
The patent extracts and processes only the essential information from the multi-dimensional sensing data. By using statistical operations (mean, standard deviation) and focusing on characteristic values rather than processing all raw data points, the system maintains comprehensive space coverage while significantly reducing data processing time and improving response speed.
4Area of stationary object
If the data volume from background is much larger than object data, then the background coverage is improved, but the detection efficiency becomes poor when only object information is needed
Solution Approach 1:
The patent extracts object information from background data by performing statistical operations and comparing characteristic values. The system calculates mean and standard deviation of sensed data, then identifies objects by detecting significant deviations from the background statistical profile. This extraction approach efficiently isolates object signals from the much larger background data volume, dramatically improving detection efficiency.
Solution Approach 2:
The patent applies different processing quality levels to different data regions. Background areas undergo statistical summarization with lower processing intensity, while regions containing objects receive focused analysis with higher processing quality. This local quality differentiation allows the system to maintain comprehensive background coverage while concentrating computational resources on object detection, improving overall efficiency.
Data Source
AI summary
A method and system for object location detection in a space, the method including the steps of: configuring the resolution of a multi dimensional sensing apparatus to divide a multi dimensional space into M first sub spaces; scanning the multi dimensional space to generate M first sensed data and at least one first locked space; configuring the resolution of the multi dimensional sensing apparatus to divide each of the at least one first locked space into N second sub spaces; scanning the at least one first locked space to generate at least one group of second sensed data and at least one second locked space; and combining at least one of the M first sensed data that corresponds to the at least one first locked space, with the at least one group of second sensed data to form a set of output sensed data.