LIDAR Extended Detection Periods for Range Aliasing Detection
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Solution Overview
Problem
LIDAR systems face range aliasing issues due to the inability to disambiguate between signals from within and outside their nominal detection range, leading to false echoes and ambiguity in object detection, particularly when detecting retroreflective objects beyond the maximum unambiguous range.
Innovation Solution
Implementing extended detection periods in the LIDAR system, where standard detection periods are supplemented by extended periods of longer duration, allowing the system to determine if return light pulses originated from objects outside the nominal detection range by calculating time delays relative to emission times, thereby extending the detection range and reducing range ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extended detection periods are used for range aliasing detection, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary range aliasing detection using a first detection period before final target identification. This preliminary action filters out potential range aliasing cases early, allowing the system to maintain high detection precision while reducing the overall time loss by avoiding extended detection periods for all targets.
Solution Approach 2:
The detection process is segmented into multiple stages: initial detection with first detection period, range aliasing detection with second detection period, and final target identification. By segmenting the detection process, the system applies extended detection periods only when necessary (during range aliasing detection), thereby maintaining measurement precision while minimizing overall time loss.
2Reliability
If range aliasing detection is performed, then reliability is improved, but device complexity increases
Solution Approach 1:
The system uses feedback mechanisms where detection results from the first detection period inform the second detection period. Range aliasing cases identified in the first period trigger extended detection, and results feed back into final target identification. This feedback loop improves reliability by systematically addressing potential errors without requiring complex additional hardware.
Solution Approach 2:
The detection system dynamically adjusts its operation based on detected conditions. When range aliasing is detected, the system automatically extends the detection period; otherwise, it proceeds with standard detection. This dynamic adaptation improves reliability for edge cases while maintaining simple operation for typical scenarios, avoiding the need for permanently complex system architecture.
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 determines if objects are within or beyond the nominal detection range, reducing computational costs and ambiguity, and enables accurate identification and distance measurement of objects previously undetectable due to range limitations.
Implementation Method 1
a light detection and ranging (LIDAR) system
Implementation Method 2
a light detection and ranging (LIDAR) system
Data Source
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AI summary
A computing system may operate a LIDAR device to emit and detect light pulses in accordance with a time sequence including standard detection period(s) that establish a nominal detection range for the LIDAR device and extended detection period(s) having durations longer than those of the standard detection period(s). The system may then make a determination that the LIDAR detected return light pulse(s) during extended detection period(s) that correspond to particular emitted light pulse(s). Responsively, the computing system may determine that the detected return light pulse(s) have detection times relative to corresponding emission times of particular emitted light pulse(s) that are indicative of one or more ranges. Given this, the computing system may make a further determination of whether or not the one or more ranges indicate that an object is positioned outside of the nominal detection range, and may then engage in object detection in accordance with the further determination.