LIDAR Backscatter Curve Segmentation for Particle Cloud Detection
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Solution Overview
Problem
Existing LIDAR devices fail to distinguish between reflection from particle clouds and object reflections, leading to degraded transit time measurements and incorrect recognition of particle clouds as objects, especially in adverse weather conditions.
Innovation Solution
The method involves conducting multiple transit time measurements, generating histograms of received optical power versus transit time, and determining a correlation between the backscatter curve and sensitivity curve to differentiate between particle clouds and objects, thereby assigning a particle cloud feature to each measurement, which helps in determining the presence and confidence level of a particle cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-of-flight measurements are performed in particle cloud conditions, then distance measurement capability is maintained, but measurement precision deteriorates due to incorrect detection of particle reflections as object reflections
Solution Approach 1:
The patent segments the backscatter curve into multiple sections corresponding to different time intervals, allowing differentiation between particle cloud reflections (early returns) and object reflections (later returns). This segmentation enables the system to identify and filter out erroneous measurements from particle clouds while preserving valid object distance measurements.
Solution Approach 2:
The patent introduces a sensitivity curve as an intermediary reference model that describes the expected backscatter pattern from particle clouds. By comparing the actual backscatter curve against this sensitivity curve, the system can identify when particle clouds are present and adjust measurements accordingly, preventing incorrect classification of particle reflections as object reflections.
2Device complexity
If particle cloud presence is not detected, then device complexity is reduced, but measurement precision deteriorates due to treating degraded measurements with same confidence as valid measurements
Solution Approach 1:
The patent performs preliminary analysis of the backscatter curve to detect particle cloud presence before finalizing distance measurements. By evaluating the backscatter curve characteristics in advance, the system can pre-identify when measurements are degraded by particle clouds and apply appropriate filtering or confidence adjustments, preventing propagation of erroneous data without requiring complex real-time processing during measurement.
Solution Approach 2:
The patent implements feedback by continuously monitoring the backscatter curve and comparing it against the sensitivity curve to detect particle cloud conditions. This feedback mechanism allows the system to adjust measurement confidence and filtering parameters dynamically, improving measurement precision while maintaining manageable complexity through automated detection and response protocols.
3Measurement precision
If multiple time-of-flight measurements are performed to improve accuracy, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent merges multiple time-of-flight measurements into a unified backscatter curve that captures reflection characteristics across different time intervals. By combining the data from multiple measurements into a single analytical structure, the system achieves improved measurement precision through statistical robustness while avoiding the complexity of processing each measurement separately, as the combined curve allows for efficient particle cloud detection and filtering.
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 effectively prevents the misclassification of particle clouds as objects, improving the accuracy of distance measurements and enabling better navigation in adverse weather conditions by providing a confidence measure for particle cloud detection and density assessment.
Implementation Method 1
Based on the time-of-flight principle, the speed of light is used to determine the distance to the objects from which the measurement pulses were reflected.
Implementation Method 2
They comprise a transmitter unit for transmitting measurement pulses and a receiver unit for receiving reflected measurement pulses from objects within a measurement range.
Implementation Method 3
the reflections from the particles and their corresponding points in the LIDAR point cloud are incorrectly detected as object reflections
Data Source
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Figure 3~4
AI summary
A method (100) for optical distance measurement is proposed, comprising the performance (101) of at least one time-of-flight measurement, wherein a time-of-flight measurement includes the emission (102) of at least one measurement pulse by means of a transmitting unit (12), the reflection (103) of at least one emitted measurement pulse, and the reception (104) of at least one reflected measurement pulse by means of a receiving unit (11). The method (100) comprises the generation (105) of a backscatter curve (20) based on the time-of-flight measurement and an evaluation (109) of the backscatter curve (20) for object detection (113).The method (100) further comprises the provision (106) of a sensitivity curve (21) for the evaluation (109) of the backscatter curve (20), wherein the evaluation (109) comprises the determination (110) of a correlation between the sensitivity curve (21) and the backscatter curve (20) in order to determine, by means of the at least one time-of-flight measurement, whether a particle cloud is located in a measurement area measured by means of the time-of-flight measurement and to assign a particle cloud feature to the time-of-flight measurement (120).