GNSS Dead Reckoning Fusion with Zero-Velocity Updates
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Solution Overview
Problem
Current positioning systems face challenges in providing accurate and reliable kinematic parameters, especially at low velocities and in environments with high short-term errors, where biases and errors are magnified, and in situations requiring attitude determination.
Innovation Solution
A system and method that fuse GNSS and sensor data, applying nonholonomic constraints and zero-velocity updates based on motion states to improve positioning solutions, and enable attitude determination by aligning hypotheses and testing for the most likely alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dead reckoning is used to maintain positioning during GNSS outages, then positioning availability is improved, but error accumulation degrades measurement precision over time
Solution Approach 1:
The patent combines dead reckoning positioning with GNSS positioning into a unified positioning system. The dead reckoning module and GNSS module work together, with dead reckoning providing continuous positioning during outages and GNSS providing periodic corrections to reset accumulated errors, thus maintaining both availability and accuracy.
Solution Approach 2:
The system implements feedback by using GNSS positioning results to correct and reset the accumulated errors in dead reckoning positioning. When GNSS signals are available, the system compares GNSS-derived position with dead reckoning position and applies corrections to eliminate drift, ensuring long-term accuracy while maintaining continuous availability.
2Measurement precision
If sensor fusion is applied to improve positioning accuracy, then measurement precision is improved, but computational complexity increases device complexity
Solution Approach 1:
The positioning system is segmented into independent functional modules: a dead reckoning module that processes inertial sensor data, a GNSS module that processes satellite signals, and a fusion module that integrates both. This modular segmentation allows each module to operate independently with optimized algorithms, reducing overall computational complexity while maintaining fusion benefits.
Solution Approach 2:
The system applies partial sensor fusion by selectively combining dead reckoning and GNSS data based on signal availability and quality. Rather than continuously fusing all sensor data, the system activates fusion only when beneficial, reducing computational overhead while maintaining positioning accuracy during critical periods.
3Measurement precision
If nonholonomic constraints and zero-velocity updates are applied based on motion states, then positioning accuracy at low velocities is improved, but system complexity increases
Solution Approach 1:
The system dynamically adjusts its processing based on detected motion states. When low velocity or stationary conditions are detected, the system activates nonholonomic constraints and zero-velocity updates to improve accuracy. During high-velocity motion, these constraints are relaxed or disabled, reducing computational complexity while maintaining accuracy when needed most.
Data Source
AI summary
A method can include receiving sensor data, receiving satellite observations, determining a positioning solution (e.g., PVT solution, PVA solution, kinematic parameters, etc.) based on the sensor data and the satellite observations. A system can include a sensor, a GNSS receiver, and a processor configured to determine a positioning solution based on readings from the sensor and the GNSS receiver.