LiDAR Receiver Matched Filter and Threshold Adaptation
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Solution Overview
Problem
LiDAR systems in autonomous driving and assistance face challenges in maintaining high accuracy and sensitivity over large ranges and varied environmental conditions, often requiring a trade-off between probability of detection and probability of false alarm.
Innovation Solution
A LiDAR system with a dynamically determined matched filter and threshold levels, based on real-time noise and interference data, to improve detection performance and maintain a low constant false alarm rate under different operation conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the LiDAR system increases sensitivity to detect weak returned signals, then the probability of detection improves, but the probability of false alarm increases due to noise and interference
Solution Approach 1:
The patent implements dynamic threshold adjustment based on real-time noise and interference measurements. The threshold is not fixed but adapts to changing environmental conditions, allowing the system to maintain high detection sensitivity while controlling false alarm rates. The threshold calculation incorporates measured noise power and interference characteristics to dynamically set appropriate detection thresholds.
Solution Approach 2:
The patent changes the detection parameter (threshold value) based on measured noise and interference levels. By calculating the threshold as a function of noise power and interference characteristics, the system adjusts its detection sensitivity parameter to maintain optimal performance across varying environmental conditions, resolving the trade-off between detection probability and false alarm rate.
2Device complexity
If the LiDAR system uses fixed threshold levels for detection, then the system complexity is reduced, but the detection performance deteriorates under varied environmental conditions
Solution Approach 1:
The system performs self-adjustment by automatically measuring noise and interference levels and computing appropriate detection thresholds without external intervention. The LiDAR system serves itself by adapting its detection parameters based on real-time environmental measurements, eliminating the need for manual threshold calibration across different conditions while maintaining performance.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously measures noise and interference levels, uses this information to compute updated detection thresholds, and applies these thresholds to subsequent detections. This closed-loop feedback approach allows the system to adapt to changing environmental conditions automatically, improving detection performance without requiring complex pre-programming for each scenario.
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 solution achieves a high probability of detection while maintaining a low constant false alarm rate, ensuring reliable object detection across various environmental conditions.
Implementation Method 1
the range of a point on a target to the LiDAR system can be determined based on the time of flight (ToF) of the pulsed light beam from the transmitter to the receiver of the LiDAR system
Implementation Method 2
A LiDAR system transmits a light beam (e.g., a pulsed laser beam) by a transmitter to illuminate at least a portion of a target and measures the time it takes for the transmitted light beam to arrive at the target and then return to a receiver
Data Source
AI summary
Disclosed are techniques for improving the probability of detection and the probability of false alarm of a light detection and ranging (LiDAR) system. A receiver of the LiDAR system is configured to obtain a noise signal vector for an operation condition and determine the coefficients of a matched filter based on the noise signal vector. The matched filter is used to filter a returned signal vector corresponding to returned light detected by the receiver. The receiver detects an object in the field of view of the LiDAR system based on identifying, in the returned signal vector filtered by the matched filter, a pulse having a peak higher than a threshold value. In some embodiments, the receiver is configured to determine the threshold value based on the noise signal vector, energy of the transmitted signal, and a desired false alarm rate.