Distributed Sensor Fusion for GPS-Denied Navigation
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Solution Overview
Problem
Existing navigation systems face challenges in accurately determining the position of nodes in GPS-denied environments due to reliance on infrastructure-dependent techniques, high costs, and accuracy issues with inertial navigation units, especially in scenarios where GPS signals are weak or unavailable.
Innovation Solution
A method and apparatus that fuse inertial and range sensor observations using a distributed network of nodes, employing Kalman and Extended Kalman Filters to refine position estimates based on inter-node range measurements, allowing nodes to determine their position with minimal infrastructure and improving accuracy through collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial navigation units (INUs) are used to track node position and orientation, then position tracking capability is improved, but size, weight, power consumption and cost increase
Solution Approach 1:
The patent combines multiple low-cost sensors (accelerometers, gyroscopes, magnetometers) into an integrated sensor suite that achieves positioning accuracy comparable to expensive INUs. By fusing data from these sensors through Kalman filtering, the system merges their individual capabilities to overcome the limitations of any single sensor while maintaining low weight and power consumption.
Solution Approach 2:
The patent uses software-based filtering algorithms (Kalman filters) to create a virtual representation of the navigation solution, copying the functionality of expensive hardware INUs through computational processing of cheaper sensor data. This allows the system to achieve similar positioning accuracy without the weight and cost of traditional INUs.
2Weight of moving object
If inertial navigation units with lower size, weight, power and cost are used, then device portability is improved, but measurement accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms through Kalman filters that continuously process sensor measurements and update the position estimate. The filter uses the predicted position from inertial data and corrects it based on actual measurements from other sensors, creating a closed-loop system that maintains accuracy despite using lower-cost components.
Solution Approach 2:
The patent creates a composite sensing system that combines multiple different sensor types (accelerometers, gyroscopes, magnetometers, and additional sensors like barometers or cameras) to achieve accuracy levels comparable to expensive single-sensor INUs. This composite approach leverages the strengths of each sensor type to compensate for the weaknesses of individual low-cost components.
3Device complexity
If systems using only inertial navigation unit (INU) measurements are used, then device simplicity is improved, but drift error accumulation increases
Solution Approach 1:
The patent introduces measurement fusion algorithms (Kalman filters) as intermediaries between the inertial sensors and the final position solution. These filters act as mediators that process inertial data in conjunction with measurements from other sensors, preventing drift error accumulation without requiring complex hardware modifications to the inertial sensors themselves.
Solution Approach 2:
The patent creates a multi-functional positioning system that can operate using inertial data alone when necessary, but also integrates with other sensing modalities when available. This universal approach maintains system simplicity while providing multiple pathways to achieve accurate positioning and prevent drift, adapting to different operational conditions.
4Device complexity
If systems based on inter-node ranging for sensor localization are used, then infrastructure requirements are reduced, but flip and rotation ambiguities increase
Solution Approach 1:
The patent uses feedback from multiple sensor modalities (accelerometers, gyroscopes, magnetometers) to resolve ambiguities in inter-node ranging measurements. The inertial sensors provide continuous orientation and position feedback that helps disambiguate flip and rotation uncertainties inherent in range-only measurements, maintaining simplicity while improving precision.
Solution Approach 2:
The patent creates a composite measurement system that combines inter-node ranging data with inertial sensor data and other measurements. This composite approach uses the strengths of each measurement type to compensate for their weaknesses, particularly using inertial orientation data to resolve the flip and rotation ambiguities that plague range-only systems.
Data Source
AI summary
A computer implemented method for fusing position and range measurements to determine the position of at least one node of a plurality of distributed nodes is disclosed. The method includes (a) measuring the position of at least one node; (b) computing an estimate of the position of the at least one node based on the measured position using a filter that takes account of past estimates of position; (c) receiving an estimate of position of at least a second node; (d) measuring inter-node range to the at least a second node; (e) combining the measured inter-node range with the estimate of position of at least a second node using a second filter that takes account of past estimates of position to generate a refined estimate of the position of the at least one node; and (f) when a change in the position of the at least one node is above a predetermined threshold value, setting the refined estimate of the position of the at least one node to the estimate of the position of the at least one node and repeating (c), (e), and (f).