LiDAR Noise Estimation via Recursive Time-Window Aggregation
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Solution Overview
Problem
LiDAR systems face errors in distance measurement due to noise coupling with returned signals, necessitating real-time noise parameter estimation for improved accuracy and efficiency.
Innovation Solution
A system and method for analyzing noise data in LiDAR systems, which involves a processor that determines estimated noise values by aggregating noise data from sequential time windows, allowing for real-time noise power and DC bias estimation to update detection thresholds and enhance signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise estimation is performed using traditional methods, then the LiDAR system can operate, but the measurement precision deteriorates due to noise coupling with returned signals
Solution Approach 1:
The patent segments the received signal into distinct components: returned laser signals and noise signals. By separating these components through statistical analysis and time-windowed processing, the system can estimate noise parameters independently and remove noise effects from distance measurements, thereby improving measurement precision
Solution Approach 2:
The patent introduces noise estimation as an intermediary process between signal reception and distance calculation. By first estimating noise parameters (mean and variance) from the received signals and then using these estimates to correct the distance measurements, the system effectively mediates the harmful noise coupling effect
2Measurement precision
If real-time noise estimation is implemented, then the detection accuracy improves, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The patent implements self-service by having the LiDAR system estimate its own noise parameters using its received signals. The system uses its own received signal samples to compute noise mean and variance, eliminating the need for external calibration equipment or additional dedicated noise measurement hardware, thus limiting the increase in device complexity
Solution Approach 2:
The patent changes the processing parameters by dividing time into windows and updating noise estimates recursively rather than performing full batch processing. This parameter change in the estimation approach reduces computational complexity while maintaining real-time detection accuracy
3Measurement precision
If noise data from multiple time windows is aggregated, then the noise estimation accuracy improves, but the loss of time increases due to sequential processing
Solution Approach 1:
The patent maintains continuity by performing noise estimation continuously across multiple time windows rather than performing a single batch estimation. The recursive update approach allows the system to continuously refine noise parameters as new data arrives, improving estimation accuracy without stopping the LiDAR operation
Solution Approach 2:
The patent performs preliminary noise estimation using data from the first time window before processing the second time window. This preliminary estimate is then aggregated with the second window's data to improve the final estimate, allowing parallel processing of multiple windows while maintaining accuracy
Data Source
AI summary
Embodiments of the disclosure provide a system for analyzing noise data for light detection and ranging (LiDAR). The system includes a communication interface configured to sequentially receive noise data of the LiDAR in time windows, at least one storage device configured to store instructions, and at least one processor configured to execute the instructions to perform operations. Exemplary operations include determining an estimated noise value of a first time window using the noise data received in the first time window and determining an instant noise value of a second time window using the noise data received in the second time window. The second time window is immediately subsequent to the first time window. The operations also include determining an estimated noise value of the second time window by aggregating the estimated noise value of the first time window and the instant noise value of the second time window.