LiDAR Air Data Fusion with Supplementary Sensors
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Solution Overview
Problem
Current LiDAR systems face challenges in achieving high accuracy for air data measurements due to noise in the backscattered lineshape, making it difficult to distinguish between air pressure and temperature, which limits their effectiveness as a replacement for legacy air data sensors in aircraft.
Innovation Solution
The method involves inputting data from LiDAR sensors and supplementary sensors like static pressure and total air temperature sensors to process and enhance air data parameters, reducing noise and improving the fit of the LiDAR backscatter lineshape, thereby increasing the accuracy of air data output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR is used to remotely interrogate air volume for air data acquisition, then reliability and aerodynamic drag are improved, but measurement precision deteriorates due to noise in the backscattered lineshape
Solution Approach 1:
The patent combines LiDAR backscatter signal processing with supplementary sensor data (static pressure and/or total air temperature sensors) to create a hybrid measurement system. This merging allows the system to leverage the reliability and aerodynamic advantages of LiDAR while compensating for its measurement precision limitations through data fusion with traditional sensors.
Solution Approach 2:
The patent introduces supplementary sensors as intermediary elements that mediate between the LiDAR system and the final air data output. These sensors provide additional measurement data that helps resolve the ambiguities and noise in the LiDAR backscatter signal, particularly in distinguishing between pressure and temperature effects on the lineshape.
2Loss of energy
If LiDAR backscatter signal is used to derive air data parameters, then aerodynamic drag is reduced, but measurement precision worsens due to difficulty in deconvolving the signal
Solution Approach 1:
The patent merges LiDAR backscatter signal analysis with supplementary sensor measurements to create a combined data processing approach. This allows the system to maintain the low aerodynamic drag advantage of LiDAR while improving measurement precision through the additional information provided by static pressure and/or total air temperature sensors.
Solution Approach 2:
The patent implements a feedback mechanism where supplementary sensor data is used to inform and refine the LiDAR signal processing. The additional measurements provide feedback that helps constrain the deconvolution process, making it easier to accurately separate pressure and temperature effects from the backscatter lineshape.
3Device complexity
If LiDAR technology is used to displace legacy air data technology, then device complexity is reduced, but measurement precision deteriorates due to noise in the backscattered lineshape
Solution Approach 1:
The patent segments the air data measurement system into multiple independent measurement channels: LiDAR backscatter signal processing and supplementary sensor measurements. This segmentation allows each component to perform its specialized function optimally while the combined output achieves higher overall precision than either component alone.
Solution Approach 2:
The patent creates a multi-functional measurement system where the LiDAR provides both primary air data measurement capability and supplementary information for calibration. The system can operate with different combinations of sensors depending on operational requirements, providing universal applicability across various flight conditions.
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 the accuracy of air data parameters such as airspeed, true air temperature, and static pressure, providing more reliable data for aircraft control systems while retaining the benefits of LiDAR technology like accuracy at high angles of attack and low velocity without aerodynamic drag.
Implementation Method 1
The optical beam can be reflected based on Mie scattering only, Rayleigh scattering only, or a combination of Mie scattering and Rayleigh scattering. Mie scattering and Rayleigh scattering both contribute to the LiDAR backscatter lineshape
Implementation Method 2
Rayleigh scattering describes the elastic scattering of light by spheres that are much smaller than the wavelength of light. Rayleigh scattering has a strong wavelength dependence and increases rapidly with decreasing wavelength
Implementation Method 3
The air speed vector is derived from the Doppler shift of the backscattered return. The Doppler shift is proportional to the Δv=(v1−v2)
Implementation Method 4
The width (W) of the molecular lineshape is dictated by the air temperature
Implementation Method 5
The intensity (i.e., the area under the curve) of the molecular lineshape is dictated by the density, which is directly related to the air pressure. The air temperature and air pressure are interrelated via the well known ideal gas law (PV=nRT)
Data Source
AI summary
A method of enhancing LiDAR data is provided. The method includes inputting LiDAR data from at least one LiDAR sensor; inputting data from at least one of: at least one static pressure sensor; and at least one total air temperature sensor; and extracting accurate air data parameters by processing one of: the LiDAR data and static pressure data from the static pressure sensor; the LiDAR data and true temperature data from the total air temperature sensor; or the LiDAR data, the static pressure data from the static pressure sensor, and the true temperature data from the total air temperature sensor. The method also includes generating augmented air data based on the extracted accurate air data parameters and outputting the augmented air data.