Lidar Signal Processing for Image Resolution and Detection Efficiency
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Solution Overview
Problem
Existing lidar systems are inadequate in terms of performance, particularly in automated vehicles and drones, due to limitations in signal strength and receiver sensitivity, which affect image resolution and efficiency.
Innovation Solution
The implementation of a lidar system that includes an optical transmitter for projecting chirped light, a receiver for converting reflected light to electrical signals, and digital signal processing techniques such as fast Fourier transform, constant false alarm rate detection, and maximum likelihood detection to improve data processing and image resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing lidar signal processing methods are used, then the system can operate with current hardware, but the image resolution and detection efficiency are insufficient
Solution Approach 1:
The patent divides the received light signal into multiple channels (e.g., I and Q channels) and processes each channel separately through FFT and CFAR detection. This segmentation allows parallel processing of different signal components, improving both resolution and efficiency simultaneously by distributing the computational load across multiple processing paths.
Solution Approach 2:
The patent applies preliminary signal processing steps (FFT transformation and CFAR detection) before final clustering and MLD. By performing these preprocessing operations first, the system prepares the data in an optimized format that enables more efficient subsequent processing, thereby improving overall detection efficiency without sacrificing image resolution.
2Measurement precision
If more data is processed to improve image resolution, then measurement precision increases, but processing time and computational load increase
Solution Approach 1:
The patent extracts only the most relevant signal components through CFAR detection, which identifies and isolates significant targets from the processed data. By extracting only the essential information needed for detection rather than processing all data points equally, the system achieves high image resolution with reduced processing time and computational resources.
Solution Approach 2:
The patent combines multiple processed channels (I and Q channels) and clusters the resulting data sets to form comprehensive target information. This merging approach consolidates information from multiple sources into unified detections, achieving high-resolution imaging while reducing the overall processing burden by combining operations rather than handling each data point separately.
3Reliability
If conventional detection methods are used, then the system structure remains simple, but receiver sensitivity and signal strength are insufficient
Solution Approach 1:
The patent introduces intermediate processing stages (FFT module and CFAR module) between the basic signal reception and final detection. These intermediary components transform the raw signal into a more suitable format for detection, enhancing receiver sensitivity by properly conditioning the signal before final analysis, while the modular structure keeps the overall system complexity manageable.
Solution Approach 2:
The patent replaces complex hardware-based signal enhancement mechanisms with software-based digital signal processing techniques. Instead of using additional physical components to amplify or filter signals, the system uses computational methods (FFT, CFAR, clustering) to achieve the same effect, improving receiver sensitivity without proportionally increasing device complexity.
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 enhances lidar detection and imaging efficiencies, providing improved image resolution and operating efficiency by processing smaller amounts of data, while being compatible with existing systems and manufacturing processes.
Implementation Method 1
an optical transmitter configured to project chirped light signal
Implementation Method 2
an optical receiver configured to receive a reflected light signal, which is based on the chirped light signal reflected off one or more objects
Implementation Method 3
a transimpedance amplifier configured to convert the reflected light signal to an electrical signal
Data Source
AI summary
The present invention is directed to lidar systems and methods thereof. More specifically, a lidar receiver converts received light signal to electrical signal. The electrical signal is converted to digital signal. Fast Fourier transform is performed on the digital signal to generate n channels of data. Constant false alarm rate detection is performed to generate n data sets, which is grouped into m clusters of data. Maximum likelihood detection is performed on the m clusters of data.