Navigation System Stochastic Model Parameter Adaptation
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Solution Overview
Problem
Existing navigation systems fail to optimally adapt to varying acquisition conditions and ambient influences, leading to non-optimum results due to the lack of consideration for sensor availability and system errors, particularly in the automotive sector.
Innovation Solution
A method and system that utilize a base system and at least one correction system to recognize and correct error values by considering the availability of the correction system, adapting the stochastic system model's parameters to weight measured values accordingly, thereby accounting for ambient conditions and intrinsic system errors without modifying the system matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion methods are used to correct measured data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameters of the stochastic system model (error covariance matrices, process noise, measurement noise) to reflect the actual availability and quality of correction systems. By dynamically adjusting these parameters based on sensor availability rather than changing the fundamental system structure, the patent achieves adaptive correction while avoiding excessive complexity.
Solution Approach 2:
The patent introduces dynamic adaptation of the stochastic system model parameters in response to changing conditions (sensor availability, ambient conditions). The error covariance matrices and noise parameters are updated dynamically to reflect current system state, allowing the navigation system to adapt to varying conditions without requiring a completely different system architecture.
2Reliability
If multiple correction systems are used to account for ambient conditions, then reliability is improved, but computational burden increases
Solution Approach 1:
The patent extracts and separates the adaptation of stochastic model parameters from the core navigation calculation. By isolating the parameter adaptation step and making it modular, the system can efficiently update only the necessary parameters (error covariance, noise levels) without recomputing the entire navigation solution, thus reducing computational burden while maintaining reliability.
Solution Approach 2:
The patent efficiently handles multiple correction systems by changing the parameters of the stochastic model to reflect their combined effect. Rather than implementing complex multi-system fusion algorithms, the patent adapts the error covariance matrices and noise parameters to encapsulate the influence of multiple correction systems, simplifying the computational process while maintaining reliability.
3Adaptability or versatility
If the stochastic system model is adapted to consider sensor availability, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent achieves adaptability by changing the parameters of the existing stochastic system model (error covariance matrices, process noise, measurement noise) to reflect sensor availability and ambient conditions. This approach maintains the fundamental structure of the model while allowing it to adapt to different conditions through parameter adjustment, avoiding the need for multiple complex models.
Solution Approach 2:
The patent makes the stochastic system model universal by designing it to handle multiple scenarios (different sensor availabilities, ambient conditions) through a single unified framework. The same model structure is used across all conditions, with only the parameters being adapted, making the system versatile without increasing structural complexity.
Data Source
AI summary
A navigation system comprises a base system and at least one correction system. The base system and the at least one correction system each capture measured values. The measured values describe navigation data, and are each burdened with error values. The error values describe discrepancies in the measured values from the described navigation data. At least the error values of the measured values of the base system are recognized by the measured values of the at least one correction system and wherein the recognition is effected by considering an availability of the at least one correction system. The consideration represents adaptation of parameters of a stochastic system model. The stochastic system model prescribes a weighting for measured values of the at least one correction system with respect to measured values of the base system in accordance with the parameters.