Relative Vehicle Navigation Using Interferometric Mesh Imaging
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Solution Overview
Problem
Existing navigation systems for mobile vehicles face limitations in precision and reliability, particularly in adverse weather conditions and the need for external datasets, with existing technologies lacking sufficient accuracy and robustness across various environments.
Innovation Solution
A self-contained navigational system utilizing an active coherent imaging sensor array with multiple receivers and a digital processing component to produce pair-wise interferometric images, enabling precise three-dimensional location and orientation without external inputs, employing forward propagation of navigation solutions or inertial measurements for error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If optical cameras are used for localization, then the system can operate with external illumination, but it cannot operate in fog or other inclement weather
Solution Approach 1:
The system employs multiple sensor types (optical cameras, LiDAR, acoustic sounders, radar, sonar) each optimized for different environmental conditions. This multi-functional sensor array ensures the system can operate reliably across diverse weather conditions by switching between or combining sensor modalities based on environmental suitability.
2Adaptability or versatility
If LiDAR is used for localization, then the system provides its own illumination, but its performance degrades in inclement weather
Solution Approach 1:
The system combines LiDAR with weather-robust sensors (acoustic sounders, radar, sonar) that provide their own illumination and are insensitive to weather conditions. This allows the system to maintain self-illumination capability while compensating for LiDAR's weather sensitivity through alternative sensor modalities.
3Reliability
If acoustic sounders, radar and sonar are used for localization, then the system is robust against weather and other environmental factors, but it lacks sufficient accuracy
Solution Approach 1:
The system merges data from multiple sensor types (optical cameras, LiDAR, acoustic sounders, radar, sonar) into a unified localization solution. Each sensor type contributes its strengths: optical and LiDAR provide high precision in suitable conditions, while acoustic, radar, and sonar provide weather robustness. The fusion algorithm combines these complementary measurements to achieve both high accuracy and weather resilience.
4Measurement precision
If GPS is used for position information, then the system can obtain location data, but external support systems can be intentionally or unintentionally blocked or otherwise corrupted
Solution Approach 1:
The system performs self-contained localization by using its own sensor array to directly measure its position and orientation relative to the environment, rather than relying on external GPS satellites. The sensor array captures environmental features and uses pattern recognition and triangulation methods to determine location independently, making the system immune to GPS blocking or corruption while maintaining high position accuracy.
5Ease of operation
If Inertial Measurement Units (IMUs) are used for location and orientation information, then the system can provide navigation data, but it is prone to drift over time without an external mechanism to constrain the drift
Solution Approach 1:
The system uses the sensor array to continuously capture environmental features and compare them with stored maps or previously observed features. This provides external feedback that constrains and corrects IMU drift in real-time. The feedback loop continuously refines the position and orientation estimates, maintaining high accuracy over extended periods without external GPS assistance.
6Measurement precision
If a single InSAR Interferogram is captured and compared to a Digital Elevation Model (DEM) reference, then interferogram matching can provide location shift information, but the system requires external reference datasets
Solution Approach 1:
The system builds and maintains its own environmental feature database through continuous observation and mapping. Instead of requiring external DEM references, the sensor array autonomously captures and stores environmental features, enabling self-contained interferogram matching and location determination. This eliminates dependency on external reference datasets while maintaining the ability to provide precise location shift information.
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
Achieves micron-level localization and milli-degree orientation with improved accuracy and robustness across diverse environments, including unexplored regions and planetary surfaces, without reliance on external systems.
Implementation Method 1
The system employs an active radar or acoustic sensor array that transmits energy toward the environment and receives the reflected energy
Implementation Method 2
The reflected energy from the environment is captured and digitized at the receivers and sent to the digital processing unit. Using this motion information and the information from the receivers, the digital processing unit creates coherent images of the environment from the received signals
Implementation Method 3
The created images are compared on a pair-wise basis and coherently interfered to produce pair-wise interferograms
Data Source
AI summary
A self-contained, high precision navigation method and system for a mobile vehicle includes an active coherent imaging sensor array with multiple receivers that observes the surrounding environment and a digital processing component that processes the received signals to form interferometric images and determine the precise three-dimensional location and three-dimensional orientation of the vehicle within that environment. A mesh navigation system for a network of mobile vehicles is provided where each mobile vehicle hosts an active coherent imaging sensor that observes a common area in the environment that surrounds the network of mobile vehicles. The navigation system on each mobile vehicle receives signals from the other mobile vehicles reflected from the common area in the environment. These signals are processed onboard each mobile vehicle to form interferometric images and determine the precise three-dimensional location of each mobile vehicle relative to the others operating and moving within the network.