Digital Map Tracking Using Sensor Fusion for GPS-Denied Navigation
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Solution Overview
Problem
Existing navigation systems rely heavily on GPS data, which can be unreliable or unavailable in certain conditions, leading to inaccuracies and inability to track movement on digital maps without continuous satellite signal.
Innovation Solution
A system that utilizes motion sensors, optical sensors, and directional data to determine a current position and update an indicator on a digital map, even when GPS is unavailable, by correlating data from pedometers, altimeters, compasses, and optical sensors to enhance accuracy and maintain navigation functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used for navigation tracking, then position accuracy is improved, but system reliability deteriorates when GPS signals are unavailable
Solution Approach 1:
The patent combines multiple sensing systems (GPS receiver, motion sensors including accelerometers and gyroscopes, optical flow sensors, barometers, magnetometers) into an integrated navigation system. These sensors work together through sensor fusion algorithms to provide continuous position tracking, where motion sensors and optical flow sensors compensate for GPS unavailability, thereby maintaining navigation reliability while preserving position accuracy through multi-source data integration
Solution Approach 2:
The system dynamically changes operational parameters based on GPS signal availability. When GPS signals are strong, the system relies primarily on GPS data for high-precision positioning. When GPS signals are weak or unavailable, the system transitions to using motion sensor data, optical flow data, and inertial navigation algorithms, adjusting the weighting and fusion methods of different sensor inputs to maintain reliable navigation functionality across varying signal conditions
2Reliability
If multiple sensors are integrated for continuous tracking, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional sensor platform where a single integrated system performs multiple navigation functions. The same sensor suite (motion sensors, optical flow sensors, barometers, magnetometers) serves both for position determination and for compensating GPS limitations, while the processor executes multiple algorithms (inertial navigation, optical flow processing, barometric altitude measurement, magnetometer-based heading determination) through a unified sensor fusion framework, reducing the need for separate dedicated systems for each function
Solution Approach 2:
The patent introduces sensor fusion algorithms as an intermediary layer that processes and integrates data from multiple diverse sensors. This intermediary processing layer combines inputs from GPS, motion sensors, optical flow sensors, barometers, and magnetometers through weighted fusion and Kalman filtering techniques, transforming complex multi-sensor data into coherent navigation estimates, thereby managing system complexity through structured intermediate processing rather than direct sensor-to-output connections
3Adaptability or versatility
If motion sensors and optical sensors are used instead of GPS, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements preliminary calibration and initialization procedures for motion sensors and optical flow sensors before navigation begins. The system performs sensor bias estimation, scale factor calibration, and initial position/velocity/heading determination using known reference points or stationary periods. This preliminary action establishes accurate baseline parameters for the inertial and optical sensing systems, significantly improving their measurement precision when used as primary navigation sources in GPS-denied environments
Solution Approach 2:
The patent employs feedback mechanisms where the navigation system continuously monitors the performance and consistency of motion sensor and optical flow sensor data. When drift or accuracy degradation is detected through internal consistency checks or when GPS signals become temporarily available, the system adjusts sensor fusion weights, recalibrates sensor parameters, or corrects accumulated errors. This feedback loop maintains measurement precision by dynamically adapting to sensor performance variations and correcting deviations from accurate position estimates
Data Source
AI summary
In an embodiment, an apparatus may include a circuit that has a first input to receive motion data from at least one of a motion sensor and an optical sensor and that has a second input to receive directional data corresponding to the motion data. The circuit may further include an input/output interface configured to provide data to a display and a control circuit coupled to the first input, the second input, and the input/output interface. The control circuit may be configured to determine a current position based at least in part on the motion data and the directional data and update an indicator on a digital map based on the current position determined from the motion data.