Optical Sensor Background-Adaptive Filtering for False-Positive Control
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Solution Overview
Problem
Existing optical sensors, particularly lidar systems, struggle to reliably distinguish true measurement signals from false-positive signals due to varying environmental conditions, leading to inconsistent false-positive rates and unnecessary filtering of true signals.
Innovation Solution
An optical sensor system with a transmission unit, reception unit, and control and evaluation unit that uses a calibration data set to determine signal threshold values based on current background levels, independent of noise distribution assumptions, ensuring a constant false-positive rate by adapting to actual noise conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conservative or relatively largely selected difference relative to the mean value is used to ensure a specific false-positive rate, then the false-positive rate is controlled, but an unnecessarily large number of true-positive measurement signals are filtered out
Solution Approach 1:
The signal threshold is made dynamic by adapting it to the current background level. Instead of using a fixed conservative threshold, the system calculates a background-specific threshold value that changes with environmental conditions. This dynamic adaptation allows the threshold to be optimized for each specific background level, preventing the loss of true signals while maintaining false-positive rate control.
Solution Approach 2:
The invention changes the parameter of the signal threshold based on the background level. By establishing a relationship between background level and threshold value (through calibration data or statistical analysis), the system adjusts the threshold parameter to match current environmental conditions. This parameter adaptation resolves the contradiction by allowing tight threshold control at low background levels while maintaining signal detection capability at high background levels.
2Reliability
If filter methods based on predefined distance from mean value are used, then false-positive signals can be filtered, but the filtering quality varies with different environmental conditions due to noise not following specific distribution
Solution Approach 1:
The system performs preliminary calibration to establish the relationship between background level and signal threshold before actual measurement. During calibration, the system characterizes the noise properties at different background levels and stores this information for later use. This preliminary action allows the system to adapt to specific environmental conditions and achieve consistent filtering quality across varying conditions.
Solution Approach 2:
The system uses feedback from the measured background level to adjust the signal threshold. By continuously monitoring the background level and selecting or calculating the appropriate threshold based on this feedback, the system maintains optimal filtering performance across different environmental conditions. The threshold is not fixed but is determined by the current operating conditions.
3Ease of operation
If a fixed signal threshold is used to filter false-positive signals, then the filtering process is simple, but the false-positive rate varies with different background levels
Solution Approach 1:
The signal threshold transitions from a static fixed value to a dynamic value that adapts to background level. The system implements a background-dependent threshold selection mechanism that automatically adjusts the threshold based on the measured background conditions, ensuring consistent false-positive rate control while maintaining operational simplicity through automated adaptation.
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
The system reliably filters out false-positive signals while maintaining a consistent false-positive rate across varying environmental conditions, preventing unnecessary loss of true signals.
Implementation Method 1
the reception unit is configured to detect a reflected or remitted portion of the transmission signal
Implementation Method 2
the distance between an object and the lidar system can, for example, be measured by determining the time of flight of laser pulses transmitted by the lidar system
Implementation Method 3
to determine a current background level and to select one of the signal threshold values of the calibration data set based on the current background level
Implementation Method 4
to identify output signals that are derived from the operating measurement signals as invalid signals if the respective output signal is smaller than the selected signal threshold value
Data Source
AI summary
An optical sensor includes a transmission unit for emitting an optical transmission signal, a reception unit for detecting a reflected portion of the transmission signal, and a control and evaluation unit configured to receive and store a calibration data set before an operating phase. The calibration data set includes a plurality of signal threshold values that are associated with a respective background level that corresponds to a respective predetermined light intensity. The control and evaluation unit is further configured to receive operating measurement signals from the reception unit when the transmission unit is activated, to determine a current background level and to select one of the signal threshold values of the calibration data set based on the current background level, thereby being able to identify output signals derived from the operating measurement signals as invalid signals if the respective output signal is smaller than the selected signal threshold value.