RF Radar Boolean Associator for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigation during foul weather and adverse conditions due to the limitations of optical sensors, which become ineffective in presence of air-borne and surface visual obscurants, leading to inaccurate position information and compromised GPS systems.
Innovation Solution
A high-definition RF radar system with two orthogonal linear arrays that use a Boolean associator to determine true target detection and position, providing object detection and scene imaging information to enhance localization and situational awareness, even in adverse weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical sensors are used for localization and situational awareness, then navigation accuracy is improved in clear weather, but reliability deteriorates in foul weather and adverse conditions
Solution Approach 1:
The system segments the sensing function into two independent parts: optical sensors for clear weather operation and RF radar sensors for foul weather operation. This allows the autonomous vehicle to maintain navigation capabilities across all weather conditions by switching between or fusing data from these segmented sensing systems.
Solution Approach 2:
The system changes the operating parameter of the sensing system by switching from optical wavelength (for clear weather) to RF radar wavelength (for foul weather). This parameter change allows penetration of visual obscurants like rain, fog, and smoke that block optical sensors but not RF waves.
2Loss of information
If GPS/INS systems are used for position estimation, then localization is achieved, but position accuracy deteriorates under GPS compromised conditions
Solution Approach 1:
The RF radar system acts as an intermediary sensing system that provides independent position and velocity information when GPS/INS systems are compromised. The radar can detect road features and obstacles to establish position without relying on GPS signals, thereby maintaining localization accuracy under GPS-denied conditions.
3Use of energy by moving object
If conventional radar with coarse angle resolution is used for velocity estimation, then velocity information is obtained, but measurement precision deteriorates
Solution Approach 1:
The system transitions from conventional 1D linear arrays to a 2D planar array configuration. This dimensional change enables the radar to form beams in both azimuth and elevation directions, providing coarse angle resolution in two dimensions and improving velocity estimation accuracy through enhanced spatial sampling.
Solution Approach 2:
The system applies digital beamforming and signal processing techniques beforehand to compensate for the coarse angle resolution of the physical array. By pre-processing the received signals through virtual array synthesis and spectral estimation algorithms, the system cushions against the inherent resolution limitations of the hardware configuration.
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
The system enables accurate object detection and scene imaging, improving navigation and localization accuracy for autonomous vehicles in all weather conditions by penetrating visual obscurants and providing reliable data for safe driving.
Implementation Method 1
The system enables accurate object detection and scene imaging, improving navigation and localization accuracy for autonomous vehicles in all weather conditions by penetrating visual obscurants
Data Source
AI summary
A low cost, all weather, high definition imaging system for an autonomous vehicle is described. The imaging system generates true target object data suitable for imaging, scene understanding, and all weather navigation of the autonomous vehicle. Data from multiple arrays is fed to a processor that performs data association to form true target detections and target positions. A Boolean associator uses an association method for determining true target detections and target positions to reduce many of the ghosts or incorrect detections that can produce image artifacts. The imaging system provides near optimal imaging in any dense scene for autonomous vehicle navigation, including during visually obscured weather conditions such as fog. The system and method can be applied to variety of imaging technologies, including an RF system, a Lidar system, a sonar system, an ultrasound system, and/or an optical system.