LIDAR Noise Rejection via Pseudo-Random Interframe Delays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LIDAR systems face challenges in distinguishing between return pulses from their own transmitter and noise pulses from other LIDAR systems in a crowded environment, where multiple vehicles emit light pulses that can cause interference and make it difficult to accurately determine object distances.
Innovation Solution
The LIDAR system employs a sequence of frames with varying interframe delays, generated by a pseudo-random number generator, to differentiate between desired return pulses and noise pulses by correlating pulse arrivals with frame start times, using a spectrum memory to record and analyze time-of-arrival data, and incorporating spatial filters to limit detection to specific angles and wavelengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR systems operate in a crowded environment with multiple vehicles emitting light pulses, then the receiver detects a large number of light pulses, but it becomes difficult to distinguish between return pulses from the own transmitter and noise pulses from other LIDAR systems
Solution Approach 1:
The system uses periodic frame structures with fixed frame lengths and controlled interframe delays to create a repeating pattern of pulse emissions. This periodic action allows the receiver to correlate detected pulses with specific frame start times, enabling discrimination between own transmitter returns and noise from other LIDAR systems operating with different timing patterns.
Solution Approach 2:
The system dynamically adjusts the interframe delay time between consecutive frames, making the timing pattern variable rather than fixed. This dynamic timing variation prevents synchronization with noise pulses from other LIDAR systems while maintaining the ability to correlate and identify legitimate return pulses through the structured frame approach.
2Loss of information
If the receiver records information for all light pulses received until a fixed stop time, then complete data is captured, but noise pulses from other LIDAR systems are also recorded and increase processing complexity
Solution Approach 1:
The system extracts only the relevant information needed for pulse discrimination by recording arrival times relative to frame start times and using spectrum memory to organize data by time bins. This selective extraction approach captures essential timing information while filtering out unnecessary data, reducing processing complexity without losing critical detection information.
Solution Approach 2:
The system introduces an intermediary processing layer using spectrum memory and frame correlation logic that mediates between raw pulse detection and final object identification. This intermediary structure organizes arrival time data into structured frames and uses timing correlation to separate desired signals from noise, simplifying the overall discrimination task.
3Stability of the object's composition
If the LIDAR system uses a fixed frame length for each frame, then consistent measurement intervals are maintained, but noise pulses from other LIDAR systems may still correlate with frame start times causing interference
Solution Approach 1:
While maintaining a fixed frame length for structural consistency, the system dynamically varies the interframe delay time between consecutive frames. This dynamic adjustment of the delay period prevents synchronization with noise pulses from other LIDAR systems that may operate with fixed timing patterns, while the fixed frame length ensures consistent measurement intervals within each frame for stable data composition.
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 reduces noise interference by dispersing noise pulses across memory slots, allowing desired return pulses to constructively add and be distinguished from noise, thereby improving the accuracy of distance measurements in multi-LIDAR environments.
Implementation Method 1
a transmitter that emits a light pulse in response to a launch signal, a receiver that detects light pulses and determines a time of arrival for each detected light pulse; and a controller that generates an ordered sequence of frames
Implementation Method 2
the receiver includes a spatial filter that eliminates light pulses outside of a predetermined range of angles relative to the emission direction
Data Source
AI summary
A LIDAR system and a method for operating a LIDAR system are disclosed. The LIDAR system broadly includes a transmitter that that emits a light pulse in response to a launch signal, a receiver that detects light pulses and determines a time of arrival for each detected light pulse; and a controller that generates an ordered sequence of frames. The controller generates a launch signal at the start of each frame and records information specifying a time of arrival relative to the start time for all light pulses received by the receiver until a stop time. After the stop time, the controller waits for the interframe delay time before generating another launch signal. The interframe delay time is different for each frame in the sequence of frames. The controller determines a distance between the transmitter and an object from the recorded information.