Second-Order Signal Matrix for Lidar Echo Discrimination
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Solution Overview
Problem
Lidar systems face challenges in distinguishing targets with overlapping echoes and are susceptible to noise, limiting their precision and accuracy in range measurement.
Innovation Solution
A second-order detection method that involves emitting and receiving signals, converting return signals into digital waveforms, creating a second-order signal matrix, and deriving time-of-flight information to determine range, using techniques such as echo decorrelation and eigendecomposition to enhance echo discrimination and noise immunity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lidar detection methods are used, then the system structure is simple, but the ability to distinguish targets with overlapping echoes is poor and noise immunity is weak
Solution Approach 1:
The patent transforms the detection problem from the time domain to the frequency domain by constructing a second-order signal matrix and performing eigendecomposition. This dimensional transformation enables better separation of overlapping echoes by exploiting the spectral characteristics of the signals, thereby improving measurement precision without requiring additional physical sensors.
Solution Approach 2:
The patent changes the detection parameter from direct time-domain signal amplitude to the eigenvalues and eigenvectors of the second-order signal matrix. By analyzing the spectral parameters derived from eigendecomposition, the system can distinguish targets with overlapping echoes more effectively, resolving the contradiction between precision and complexity.
2Reliability
If conventional detection methods are used, then computational requirements are low, but the minimum signal-to-noise ratio detection threshold is high
Solution Approach 1:
The patent extracts the essential signal characteristics by performing eigendecomposition on the second-order signal matrix and identifying signal-related eigenvalues versus noise-related eigenvalues. This extraction process separates the useful signal information from noise, improving noise immunity and lowering the detection threshold while managing computational complexity through focused analysis of dominant eigenvalues.
3Measurement precision
If conventional detection methods are used, then the detection range is limited, but the system is easier to operate
Solution Approach 1:
The patent performs preliminary signal processing by constructing the second-order signal matrix and conducting eigendecomposition before target detection. This preliminary action prepares the signal data in a form that enhances detection range and precision, allowing the system to detect targets at greater distances while maintaining operational simplicity through automated processing steps.
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 method improves the discrimination of partially superimposed echoes, increases the minimum signal-to-noise ratio detection threshold, and extends the detection range while reducing computational requirements.
Implementation Method 1
each return signal including one or more echoes to be detected produced by reflection of the respective emission signal from the one or more targets
Implementation Method 2
Using the time-of-flight (TOF) principle, lidar systems are configured to measure the time required for a pulsed optical signal to travel from a transmitter to a target and back to a receiver
Data Source
AI summary
A method and system for detecting and ranging targets within a scene are provided. The method can include emitting an emission signal onto the scene and receiving a return signal including echoes produced by reflection of the emission signal from the targets. The method can also include digitizing the return signal into a digital signal waveform including N real-valued samples representing the return signal at N sequential sampling times. The method can further include creating, from the digital signal waveform, an N×N second-order signal matrix having a main diagonal whose nth element is expressed in terms of the square of the nth sample of the digital signal waveform. The method can also include deriving, based on the signal matrix, time-of-flight information associated with the echoes and indicative of range information associated with the targets. The method and system can be used, for example, in lidar-based remote sensing applications.