Autonomous Merit-Based Heading Alignment for Inertial Navigation
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Solution Overview
Problem
Accurate heading alignment and initialization in inertial navigation systems are challenging, especially in environments without GPS signals, such as underwater navigation, due to the reliance on single sensors like magnetic compasses which are inaccurate and operationally restrictive, and the need for stable systems that are often size, weight, and cost-prohibitive.
Innovation Solution
An autonomous merit-based inertial navigation system that utilizes a plurality of sensors to generate genericized sensor data, calculates a figure of merit for each heading observation, and selects the most accurate alignment based on these merits to update the heading state, allowing for continuous improvement and reinitialization of the heading estimate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single sensor (e.g., magnetic compass) is used for heading alignment, then the system size, weight, and cost are reduced, but the accuracy and operational reliability of heading estimation deteriorate
Solution Approach 1:
The patent combines multiple sensors (magnetic compass, GPS, accelerometers, gyroscopes) into a unified inertial navigation system that processes inputs from all sensors through a figure-of-merit evaluation framework. This merging allows the system to achieve high heading alignment accuracy by selecting and combining data from multiple sensor sources rather than relying on a single sensor.
Solution Approach 2:
The patent creates a universal sensor-agnostic framework that can process data from multiple types of sensors (magnetic compass, GPS, accelerometers, gyroscopes) through a common figure-of-merit evaluation system. This multi-functional approach allows the same processing architecture to handle different sensor types and configurations, achieving high accuracy without proportionally increasing system complexity.
2Measurement precision
If multiple sensors are used to improve heading alignment accuracy, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements autonomous alignment capability where the system automatically evaluates multiple sensor inputs, computes figures of merit for each sensor, and selects the best alignment source without human intervention. The system self-manages the complex process of multi-sensor fusion and automatic realignment, reducing the need for manual operation while maintaining high accuracy.
Solution Approach 2:
The patent employs continuous feedback mechanisms where the system monitors alignment quality metrics, automatically triggers realignment when degradation is detected, and adjusts sensor weighting based on performance. This feedback loop enables the system to maintain high accuracy autonomously by continuously adapting to changing operational conditions and sensor performance.
3Adaptability or versatility
If traditional alignment methods are used, then the system is simpler to implement, but the ability to operate accurately in GPS-denied environments deteriorates
Solution Approach 1:
The patent implements dynamic sensor selection and weighting that adapts to operational conditions. The system can dynamically switch between different sensor combinations and alignment methods based on environmental context (e.g., GPS available vs. GPS denied, magnetic interference levels). This dynamic adaptability allows accurate operation across diverse environments without sacrificing precision.
Solution Approach 2:
The patent changes operational parameters (sensor weighting factors, fusion algorithms, alignment thresholds) based on environmental conditions. In GPS-denied environments, the system automatically adjusts to rely more heavily on inertial sensors and magnetic compass data, modifying processing parameters to maintain accuracy despite the absence of GPS signals.
Data Source
AI summary
Autonomous merit-based heading alignment and initialization methods for inertial navigation systems that update heading alignment in parallel with a heading estimation process. In some embodiments, such methods calculate figures of merit (FOMs) for multiple heading observations and estimate heading based on the calculate FOMs. In some embodiments, sensor data are genericized and published to a generic network, and multiple heading channels listen to the generic network for the published sensor data to make the heading estimation process sensor-agnostic. Such methods can be performed by software and/or incorporated into hardware and/or software based autonomous merit-based inertial navigation systems, which in turn can be deployed in vehicles to effect autonomous navigation solutions.