Inertial Navigation Alignment via Iterative Parameter Optimization
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Solution Overview
Problem
Existing inertial navigation systems face challenges in accurately estimating initial parameters such as orientation and speed, especially in complex scenarios where data is limited, incomplete, or imprecise, and they often require a priori knowledge of the carrier type.
Innovation Solution
An alignment process for inertial navigation systems on mobile devices, which includes receiving position and motion data, generating an initial set of parameters, and iteratively correcting these parameters using a non-linear square method to minimize an error function, allowing for accurate estimation regardless of the carrier type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory alignment methods are used to determine initial orientation, then the device must follow a straight trajectory at high speed with no lateral velocity, but this assumption fails for airplanes, boats, pedestrians, and other carriers subject to wind, current, or natural movement
Solution Approach 1:
The patent creates a universal alignment method that works across multiple carrier types (airplanes, boats, vehicles, pedestrians) by replacing carrier-specific assumptions with a mathematical optimization approach that adapts to any movement pattern. The method uses position data and motion data from any carrier to iteratively optimize initialization parameters, making the system multi-functional across different application domains.
Solution Approach 2:
The patent transforms the alignment problem from relying on fixed physical assumptions (straight trajectory, high speed, no lateral velocity) to using adjustable mathematical parameters (initialization parameters for position, velocity, orientation, accelerometer bias, gyroscope bias) that are optimized through iterative calculation. This allows the system to adapt to varying carrier dynamics without requiring specific movement constraints.
2Measurement precision
If magnetic alignment methods using a magnetic compass are used to provide initial orientation, then the method can fail when metallic objects are present in the system's environment
Solution Approach 1:
The patent introduces position data from a positioning system and motion data from sensors as intermediary elements that mediate between the carrier's physical movement and the determination of initialization parameters. Instead of directly measuring orientation with a magnetic compass that is susceptible to metallic interference, the system uses these intermediary data sources to indirectly determine orientation through mathematical optimization, bypassing the magnetic field measurement vulnerability.
Solution Approach 2:
The patent replaces the magnetic compass mechanism (which relies on magnetic field interactions) with a computational approach using position data and motion data processing. This substitution eliminates dependence on magnetic field measurements that are vulnerable to metallic objects, replacing them with a mathematical model that processes accelerometer and gyroscope data along with position information to determine initialization parameters.
3Measurement precision
If dual-antenna alignment methods are used to determine initial parameters, then two antennas must be installed on the device sufficiently far apart, but this constraint complicates configuration and increases system size
Solution Approach 1:
The patent extracts the alignment capability from hardware-dependent methods (dual-antenna geometric constraints) and relocates it to a software-based mathematical optimization approach. By removing the requirement for physically separated antennas and their associated geometric relationships, the system eliminates the complexity of antenna configuration and spacing requirements while maintaining the ability to determine initialization parameters.
Solution Approach 2:
The patent creates a virtual model of the carrier's movement trajectory by copying and processing position data and motion data through mathematical relationships. Instead of relying on physical antenna geometry to infer position and orientation, the system creates a computational representation of the movement path and uses this copied data to optimize initialization parameters, eliminating the need for physical antenna arrays.
4Measurement precision
If existing alignment methods are used, then prior knowledge of the carrier type is required, but this limits the system's ability to guarantee correct parameter estimation for unknown or varied carriers
Solution Approach 1:
The patent transforms the static, carrier-type-specific alignment methods into a dynamic, adaptive system. Instead of requiring pre-programmed knowledge of carrier type (car, airplane, boat, pedestrian), the system dynamically adjusts to any carrier by using iterative optimization that processes actual position and motion data from the specific carrier instance. The method adapts its calculations based on the observed movement characteristics rather than relying on predefined carrier categories.
Solution Approach 2:
The patent enables the system to self-determine its initialization parameters without external assistance or prior knowledge of the carrier type. The iterative optimization process uses the carrier's own position data and motion data to automatically calculate the correct initialization parameters, making the system self-sufficient and independent of external configuration or carrier type identification.
Data Source
Figure 1~2
Figure 3A~3C
AI summary
The invention relates to a method for aligning an inertial navigation system, comprising the steps: (E1, E2) Receiving first position data (GNSS) and second motion data (a,u); (E3) Generating an "a priori" set (x) of alignment initialization parameters (θ0, v0, bg, ba); (E4) Correcting the "a priori" set to obtain an optimal set by the following substeps: (E41) Estimating third position data (pt) from the first and second data and a corrected set of initialization parameters; (E42) Calculating correction values (Δx) of the set that minimize an error function determined from the first and second data; (E425) Correcting the set using the correction values; the first corrected game being determined from the "a priori" game and said correction step being iterated until a stopping condition is satisfied and/or a predetermined number of iterations is reached.