Tightly-Coupled Integration Filter for GNSS Step Detection
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Solution Overview
Problem
GNSS receivers experience performance degradation in harsh environments due to satellite signal blockage, attenuation, and multipath issues, and low-cost inertial sensors struggle to provide reliable navigation data, especially in indoor and urban canyon scenarios.
Innovation Solution
A low-complexity tightly-coupled integration filter using an extended Kalman filter (EKF) is implemented to optimally integrate inertial measurement unit (IMU) navigation data with satellite measurements, minimizing modifications to the GNSS receiver's position engine and allowing smooth transitions between GNSS-only, GNSS/IMU, and IMU-only configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If low-cost inertial sensors are integrated into GNSS receivers to improve performance in harsh environments, then navigation reliability is improved, but sensor data quality and measurement precision deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the GNSS receiver continuously monitors satellite signal quality and adjusts the weight given to IMU sensor data accordingly. When satellite signals are strong, the system relies more on GNSS data; when signals are weak or unavailable, it increases reliance on IMU data. This dynamic feedback adjustment allows the system to maintain navigation reliability while compensating for the lower precision of low-cost sensors through contextual awareness and adaptive weighting.
Solution Approach 2:
The patent changes the operational parameters of the integration filter based on environmental conditions and signal availability. The system adjusts fusion weights, update rates, and threshold values dynamically. For example, when entering an indoor environment with poor satellite coverage, the system automatically increases the weight of IMU measurements and adjusts the prediction horizon parameters to better accommodate the lower precision of inertial sensors while maintaining acceptable navigation accuracy.
2Reliability
If existing GNSS receiver architecture is modified to integrate IMU data, then navigation performance in harsh environments is improved, but device complexity increases
Solution Approach 1:
The patent designs the integration filter to serve multiple functions within a single architectural framework. The same filter structure handles both GNSS data fusion and IMU data fusion, as well as transition between different operational modes (GNSS-only, IMU-only, hybrid). This multi-functional design avoids the need for separate processing paths and reduces overall system complexity compared to implementing dedicated integration modules for each sensor type.
Solution Approach 2:
The patent implements a dynamic architecture where the integration filter can adapt its behavior based on operational conditions. The system transitions smoothly between GNSS-only mode, IMU-only mode, and hybrid mode depending on signal availability and quality. This dynamic adaptability allows the system to maintain low complexity in normal operation while providing enhanced navigation performance only when and where needed, avoiding the permanent complexity burden of always-active multi-mode processing.
3Ease of manufacture
If low-cost sensors are used instead of expensive inertial sensors, then device cost is reduced, but measurement precision and data reliability deteriorate
Solution Approach 1:
The patent merges the processing of low-cost IMU sensors with the GNSS receiver architecture through a unified integration filter. By combining the strengths of both sensor systems and leveraging the complementary nature of their measurements, the system achieves navigation precision comparable to expensive sensor setups. The fusion process combines the global positioning capability of GNSS with the short-term accuracy of inertial navigation, creating a synergistic effect that compensates for the limitations of low-cost individual sensors.
Solution Approach 2:
The integration filter acts as an intermediary between the low-cost IMU sensors and the final navigation output. This intermediary component processes and contextualizes the imperfect sensor data, using GNSS information to calibrate and correct IMU measurements. The filter serves as a mediator that transforms the raw, low-precision sensor data into accurate navigation information by continuously referencing and correcting against available GNSS data, thereby masking the underlying sensor limitations.
Data Source
AI summary
Embodiments of the invention provide a step detection. An accelerometer measurement in the form of a multi-dimensional acceleration vector is obtained. The magnitude of the accelerometer measurement is filtered using a low pass filter. A threshold for a down-crossing is provided as is a threshold for an up-crossing. A step detection is triggered if the magnitude of the accelerometer measurement is greater than or equal to the threshold for an up-crossing.