TOF LiDAR Background-Light Control for Higher Detection Reliability
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Solution Overview
Problem
Lidar systems face challenges in accurately detecting objects due to interference from background light, which increases noise levels and false alarm rates, degrading their performance and reliability, especially in environments with multiple light sources.
Innovation Solution
The implementation of a detection control system that measures background light and generates real-time background signals to dynamically adjust the lidar system's parameters, such as the field of view and readout threshold, to improve the signal-to-noise ratio and reduce false alarms, while also providing confidence signals for the reliability of return signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the lidar system operates in environments with multiple light sources, then the detection capability is maintained, but the noise level increases and false alarm rate increases due to background light interference
Solution Approach 1:
The detection time interval is divided into multiple measurement time windows, with dedicated background measurement windows for measuring background light levels and signal measurement windows for detecting actual targets. This temporal segmentation allows separate characterization and subtraction of background noise from target signals, improving detection reliability in environments with multiple light sources.
Solution Approach 2:
The system implements real-time feedback by continuously measuring background light levels during background measurement windows and using this information to dynamically adjust detection parameters. The processed background signal is fed back to subtract from subsequent signal measurements, creating a closed-loop system that adapts to changing background conditions and maintains detection reliability.
2Measurement precision
If the lidar system increases the detection sensitivity to improve object detection accuracy, then the measurement precision improves, but the false alarm rate increases due to higher noise levels
Solution Approach 1:
The system extracts the background noise component from the total detected signal by measuring during dedicated background measurement windows when no target is present. This extracted background signal is then subtracted from the total signal measured during signal measurement windows, effectively removing the noise component and allowing higher detection sensitivity without increasing false alarm rates.
Solution Approach 2:
The system performs preliminary measurement of background light levels during background measurement windows before conducting target detection during signal measurement windows. This preliminary characterization of the noise environment enables subsequent signal processing to compensate for background interference, improving measurement precision without sacrificing reliability.
3Device complexity
If the lidar system uses a fixed field of view to simplify system design, then the device complexity is reduced, but the ability to adapt to varying background light conditions deteriorates
Solution Approach 1:
The system implements dynamic adaptability through temporal modulation of the field of view, switching between different measurement time windows with different FOV configurations. During background measurement windows, the FOV may be adjusted to optimize background sampling, while during signal measurement windows, it switches to detection-optimized settings. This dynamic approach provides adaptability to varying background conditions without requiring complex mechanical adjustment mechanisms.
Solution Approach 2:
The system employs periodic switching between background measurement mode and signal detection mode, with each mode having optimized field of view settings. This periodic action allows the system to adapt to different background light conditions by sampling backgrounds under various FOV configurations and processing the differences, achieving versatility without permanent complex hardware changes.
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 enhances the accuracy and reliability of lidar systems by reducing the impact of background light, improving the signal-to-noise ratio, and minimizing false alarms, thereby ensuring more precise object detection and distance measurement.
Implementation Method 1
a sensor to convert the received light to sensor signals
Implementation Method 2
determining a time of flight based on a time delay between the transmission of the optical probe signal and the reception of the reflected optical signal
Data Source
AI summary
Various methods and systems are disclosed to improve detection probability of time of flight lidar systems by generating real-time background signals for individual pixels of a sensor of the lidar detection system and use the background signals to dynamically control the detection system of the lidar. The background signals, that indicate the amount of background light received by different pixels, are used for controlling the detection system by adjusting at least one of the optical system, the sensor, and the readout system of the detection system in order to reduce the impact of the back ground light on the detection and range finding functionality of the lidar.