Vehicle Navigation Filtering Across Frames With Large Error Correction

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Solution Overview

Problem

Conventional extended Kalman filters struggle with correcting large estimation errors and require a prior alignment phase, while invariant Kalman filters face consistency loss and numerical instabilities when estimating values in different frames of reference, leading to inconsistent and unstable navigation systems.

Innovation Solution

A navigation method that uses two distinct linearization points for error variable changes, transforming variables into a common frame of reference and applying invariant error variables, followed by a reverse change to improve filter performance and stability, particularly in the presence of significant sensor biases.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional extended Kalman filter is used, then the navigation system can operate without prior alignment phase, but it cannot correct large estimation errors

Engineering Contradiction:

Improveability to correct large estimation errorsVSAvoidrequirement of prior alignment phase

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent segments the error variable transformation into two distinct linearization points: one for the uncertainty matrix transformation and another for the state variable correction. This segmentation allows the system to handle large estimation errors by using appropriate linearization points for different transformation stages, eliminating the need for prior alignment while maintaining correction capability.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If invariant Kalman filter is used to estimate values in different frames of reference, then the filter can handle multi-frame variables, but it loses consistency and suffers from numerical instabilities

Engineering Contradiction:

Improveability to estimate values in different frames of referenceVSAvoidconsistency of estimation

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces an intermediary transformation step using a first linearization point for the uncertainty matrix, which acts as a mediator between different frame of reference representations. This intermediary transformation preserves consistency by providing a stable reference point for transforming variables between frames, preventing the consistency loss that occurs in traditional invariant Kalman filters.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the parameters used in error variable transformation by employing two distinct linearization points instead of a single fixed point. This parameter change allows the system to adapt to different frame of reference requirements while maintaining numerical stability through optimized transformation parameters at each stage.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If single linearization point is used for error variable transformation, then the computation is simpler, but the filter performance degrades with significant sensor biases

Engineering Contradiction:

Improvefilter performance with sensor biasesVSAvoidnumber of linearization points

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the error variable transformation process into two distinct linearization points: one specifically for transforming the uncertainty matrix and another for state variable corrections. This segmentation improves filter performance with sensor biases by using optimized linearization points for each transformation stage, while keeping the overall structure manageable through clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12523473B2Method for assisting with the navigation of a vehicle

Publication Date: 2026.01.13 SAFRAN SA

AI summary

The invention relates to a method, navigation device and computer program product for assisting with the navigation of a vehicle provided with a navigation device. The method comprises the following steps: acquiring a priori values of variables of a navigation device of the vehicle; determining current values of the variables and a current uncertainty matrix from previous values of the variables and a previous uncertainty matrix; and determining a correction from the current values of the variables, the current uncertainty matrix and a measurement.