Signal Segmentation for Object Detection
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Solution Overview
Problem
Current methods for detecting objects using sensor information from multiple signal streams require high technical effort for evaluation and weighting, making them complex and inefficient for forming object hypotheses.
Innovation Solution
The method involves dividing the received signal into segments, analyzing temporal and amplitude features, and using machine learning techniques to form object hypotheses, which can be processed efficiently and accurately, enabling quick and precise analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor information from multiple different sensor signal streams is used to form object hypotheses, then object detection capability is improved, but technical effort for evaluation and weighting increases
Solution Approach 1:
The patent segments the received signal into multiple time segments, allowing individual analysis of signal characteristics at different time points. This segmentation enables simplified evaluation of sensor information without requiring complex weighting of multiple signal streams, as each segment can be processed independently to form object hypotheses
2Productivity
If the received signal is divided into segments for analysis, then processing speed and precision are improved, but computational steps increase
Solution Approach 1:
The received signal is divided into multiple time segments, enabling parallel processing and rapid analysis of individual segments. This segmentation strategy improves processing speed by allowing simultaneous evaluation of multiple time points, while the computational complexity is managed through straightforward segment analysis rather than complex global processing
Solution Approach 2:
The signal segmentation is performed as a preliminary step before object hypothesis formation, preparing the data in advance for efficient analysis. This preliminary segmentation enables subsequent rapid processing and classification, improving overall productivity without requiring complex computational steps during the main analysis phase
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
This approach simplifies and enhances the object hypothesis formation process, allowing for rapid and reliable detection of objects, including dynamic ones, by extracting significant features from the signal segments and using adaptive classification methods.
Implementation Method 1
at least one transmitter (10) emitting a transmission pulse (14) as a wave, in particular as an acoustic or electromagnetic wave, which is at least partially reflected by objects (12) in the propagation space
Implementation Method 2
the reflected wave being detected by at least one receiver (20) as a received signal (32)
Data Source
Figure 1~3
Figure 4~5
AI summary
The invention relates to a method for detecting objects. At least one sensor emits a transmission pulse as a wave, in particular as an acoustic or electromagnetic wave, which is at least partly reflected by objects in the propagation area. The reflected wave is detected by at least one receiver as a received signal. According to the invention, the received signal of the reflected wave is divided into segments, information being obtained from the individual segments and used to determine an object hypothesis.