INS/GNSS Navigation Module Using Mixture Particle Filter
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Solution Overview
Problem
Current navigation systems using INS/GNSS integration suffer from errors that grow unbounded over time due to the use of low-cost MEMS-based sensors, particularly in environments with degraded or denied GNSS signals, where traditional Kalman Filter techniques fail to accurately model nonlinear sensor errors and drift, leading to inaccurate positioning.
Innovation Solution
A navigation module that integrates GNSS information with self-contained sensors like accelerometers, gyroscopes, and magnetometers, using a nonlinear filtering technique such as the Mixture Particle Filter to decouple actual platform motion from sensor readings, and optionally includes advanced modeling of inertial sensor stochastic drift and automatic switching between loosely and tightly coupled integration schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional Kalman Filter techniques are used to integrate INS/GNSS data, then the system is simpler to implement, but positioning accuracy deteriorates in degraded or denied GNSS environments due to inability to accurately model nonlinear sensor errors and drift
Solution Approach 1:
The patent transitions from linear filtering parameters (Kalman Filter) to nonlinear filtering parameters (Mixture Particle Filter), changing the mathematical model to better represent the actual nonlinear behavior of MEMS sensor errors and drift, thereby improving positioning accuracy in GNSS-denied environments
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the system to switch between loosely coupled and tightly coupled integration schemes based on operational conditions, and by using particle filters that dynamically adjust to nonlinear error characteristics, making the filtering approach adaptive rather than static
2Ease of manufacture
If low-cost MEMS-based sensors are used in INS, then the system cost is reduced, but positioning accuracy deteriorates over time due to unbounded error accumulation from integration operations
Solution Approach 1:
The patent implements feedback mechanisms where the Mixture Particle Filter continuously estimates sensor drift and error characteristics, then uses this feedback to correct and bound the error accumulation, preventing unbounded drift while maintaining the use of low-cost MEMS sensors
Solution Approach 2:
The patent introduces an intermediary filtering layer (Mixture Particle Filter) that mediates between the raw MEMS sensor data and the final positioning solution, separating the low-cost sensors from the high-accuracy output by processing intermediate measurements through sophisticated error modeling
3Device complexity
If loosely coupled integration scheme is used, then the system is simpler to implement, but navigation accuracy deteriorates during GNSS outages compared to tightly coupled schemes
Solution Approach 1:
The patent makes the integration scheme dynamic by enabling automatic switching between loosely coupled and tightly coupled modes based on GNSS signal availability and quality, allowing the system to optimize between simplicity and accuracy depending on operational conditions
Data Source
AI summary
A navigation module and method for providing an INS/GNSS navigation solution for a moving platform, comprising a receiver for receiving absolute navigational information from an external source (e.g., such as a satellite), means for obtaining speed or velocity information and an assembly of self-contained sensors capable of obtaining readings (e.g., such as relative or non-reference based navigational information) about the moving platform, and further comprising at least one processor, coupled to receive the output information from the receiver, sensor assembly and means for obtaining speed or velocity information, and operative to integrate the output information to produce a navigation solution. The at least one processor may operate to provide a navigation solution by using the speed or velocity information to decouple the actual motion of the platform from the readings of the sensor assembly.