Robust SINS/DVL Filtering via Statistical Similarity Measure
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Solution Overview
Problem
Current robust filters in integrated navigation systems, such as SINS/DVL, uniformly adjust measurement information utilization, leading to underutilization of normal beam data when a single beam has a large measurement error, resulting in loss of useful information.
Innovation Solution
A robust filtering method based on statistical similarity measure (SSM) is introduced, which decomposes multi-dimensional measurement equations and adaptively updates measurement noise variance for each beam, allowing independent processing of measurement information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robust filters uniformly adjust the utilization rate of all measurement information, then the influence of beam information with large measurement error is reduced, but other normal beam measurement information is underutilized, resulting in loss of useful information
Solution Approach 1:
The patent segments the measurement information processing by decomposing the multi-dimensional measurement equations into independent beam-level processing units. Each beam's measurement information is processed separately with individual utilization rate adjustments, allowing normal beams to be fully utilized while erroneous beams are downweighted. This segmentation enables selective robust filtering without uniform penalization of all measurements.
Solution Approach 2:
The patent applies local quality by implementing beam-specific utilization rate adjustment rather than uniform adjustment across all beams. The filtering algorithm dynamically determines the utilization rate for each beam based on its measurement quality, applying different filtering strengths locally to each beam. This ensures that high-quality beams maintain full utilization while low-quality beams receive appropriate robust filtering.
2Reliability
If the utilization rate of beam information with large measurement error is reduced, then the navigation system's reliability is improved, but the utilization of normal beam information is affected
Solution Approach 1:
The patent implements dynamic adjustment of utilization rates for each beam based on real-time measurement quality assessment. The filtering algorithm continuously evaluates the consistency of each beam's measurement and dynamically modifies its utilization rate accordingly. This dynamic approach ensures that reliable beams are fully utilized for high productivity while unreliable beams are downweighted to maintain navigation reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the filtering algorithm monitors measurement consistency and uses this information to adjust utilization rates. The system feeds back the quality assessment of each beam to the filtering process, creating a closed-loop system that automatically optimizes the balance between reliability and information utilization efficiency based on actual measurement conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method ensures the effective utilization of normal beam data while reducing the impact of large measurement errors, enhancing navigation accuracy and efficiency in underwater vehicles under non-ideal conditions.
Implementation Method 1
With the Doppler frequency shift principle, a DVL can measure the speed of a carrier along the direction of a sound wave beam by calculating the difference between the frequency of a sound wave emitted to the bottom and the frequency of the reflected sound wave received.
Data Source
AI summary
The disclosure belongs to the technical field of integrated navigation under non-ideal conditions, and in particular relates to a robust filtering method for integrated navigation based on a statistical similarity measure (SSM). In view of the situation that there are normal beam measurement information of the DVL and beam measurement information with a large error simultaneously in a SINS/DVL tightly integrated system, and aiming at the problem that the existing robust filters of an integrated navigation system process the measurement information in a rough manner and are likely to lead to loss of normal measurement information, the disclosure proposes a novel robust filtering method based on decomposition of multi-dimensional measurement equations and the SSM. The disclosure introduces the SSM theory while decomposing the multi-dimensional measurement equations of the SINS/DVL tightly integrated navigation system, and assists the measurement noise variance of each beam to complete respective adaptive update in case of a large measurement error, finally ensuring independence of processing of the measurement information of each beam. The disclosure can be used in the field of integrated navigation of underwater vehicles under non-ideal conditions.