Inertial Sensor Environment Detection for Gyro-Compassing
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Solution Overview
Problem
Gyro-compassing devices require manual configuration to select the appropriate leveling algorithm based on the deployment environment, leading to potential delays if an incorrect algorithm is used, especially in dynamic conditions like airborne or at sea.
Innovation Solution
An automatic environment detection system using multiple environment models and propagator-estimator algorithms, such as Kalman filters, to predict angular oscillations and determine the correct environment, allowing the device to self-select the optimized leveling algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration is used to select leveling algorithm, then device can be configured for specific environment, but deployment time increases and system reliability decreases due to potential incorrect selection
Solution Approach 1:
The system performs self-diagnosis by automatically detecting the deployment environment using inertial sensor data and propagator-estimator algorithms, eliminating the need for manual configuration. The device independently determines whether it is airborne, at-sea, or on-ground and selects the appropriate leveling algorithm without human intervention.
Solution Approach 2:
The system continuously monitors inertial sensor measurements and uses propagator-estimator algorithms to compare actual measurements against predicted measurements from different environment models. Based on the comparison results and calculated probabilities, the system dynamically selects the most appropriate leveling algorithm, creating a closed-loop feedback system that adapts to changing conditions.
2Measurement precision
If multiple environment models are processed simultaneously, then environment detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system divides the environment detection task into separate parallel processing paths, with each path dedicated to a specific environment model (airborne, at-sea, or on-ground). Each path independently processes inertial measurements using its specialized propagator-estimator algorithm, allowing simultaneous evaluation of multiple hypotheses without sequential overhead.
Solution Approach 2:
The system implements three distinct environment detection paths with different levels of computational effort corresponding to different environment models. Each path performs partial processing tailored to its specific environment type, and the system selects the path with the highest probability rather than fully processing all paths to completion, reducing overall computational burden.
Data Source
AI summary
Systems and methods for providing automatic detection of inertial sensor deployment environments are provided. In one embodiment, an environment detection system for a device having an inertial measurement unit that outputs a sequence of angular rate measurements comprises: an algorithm selector; and a plurality of environment detection paths each receiving the sequence of angular rate measurements, and each generating angular oscillation predictions using an environment model optimized for a specific operating environment. The environment model for each of the environment detection paths is optimized for a different operating environment. Each of the environment detection paths outputs a weighting factor that is a function of a probability that its environment model is a true model of a current operating environment given the sequence of angular rate measurements; and wherein the algorithm selector generates an output based on a function of the weighting factor from each of the environment detection paths.