AUV Localization via L1-Norm Tensor DoA Estimation
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Solution Overview
Problem
Current underwater and indoor GPS systems face challenges in localization and tracking due to limited power, computation, and communication resources, especially in GPS-denied environments, where existing methods are not suitable for low-bandwidth and high-latency underwater acoustic channels, leading to significant errors and outliers.
Innovation Solution
A beacon-assisted localization system using L1-norm space-time tensor subspaces for Direction-of-Arrival (DoA) estimation, which leverages a triangular hydrophone array to jointly estimate angles-of-arrival and identify codes of beacon signals, resistant to Doppler and multipath fading noise, enabling robust 3D self-localization of Autonomous Underwater Vehicles (AUVs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If acoustic-based localization techniques are used in GPS-denied environments, then localization capability is achieved, but measurement precision deteriorates due to multipath propagation and long delays
Solution Approach 1:
The patent introduces an intermediary processing layer (tensor decomposition and L1-norm filtering) between the raw acoustic measurements and the final localization result. This intermediary processing separates the direct path signal from multipath reflections and filters out outliers, thereby maintaining measurement precision while preserving GPS-denied localization capability
Solution Approach 2:
The patent changes the parameter of norm calculation from traditional L2-norm to L1-norm in the tensor decomposition process. This parameter change makes the algorithm more robust to outliers and multipath effects, improving measurement precision without sacrificing localization capability in challenging acoustic environments
2Measurement precision
If intensive message exchanges are used for GPS-free localization, then localization accuracy is improved, but communication resource consumption increases
Solution Approach 1:
The patent extracts only the essential information (acoustic signal arrivals) needed for localization from the complex communication environment, eliminating the need for intensive message exchanges. By using passive acoustic signal processing rather than active communication protocols, the system achieves accurate localization while consuming minimal communication resources
Solution Approach 2:
The system uses existing acoustic beacon signals for self-localization without requiring additional communication overhead or intensive message exchanges. The localization process is self-sufficient, utilizing the acoustic channel itself as both the propagation medium and the measurement source, thereby avoiding extra communication resource consumption
3Device complexity
If L2-norm based tensor and matrix DoA estimation methods are used, then computation is simplified, but positioning accuracy deteriorates in noisy environments
Solution Approach 1:
The patent changes the fundamental parameter of the norm calculation from L2-norm to L1-norm in the tensor decomposition process. This parameter change provides robustness against outliers and multipath interference while maintaining computational feasibility through efficient alternating least squares algorithms, thereby improving positioning accuracy without excessive computational complexity
Solution Approach 2:
The patent combines tensor decomposition with L1-norm optimization to create a composite estimation method that leverages the strengths of both approaches. This composite methodology achieves high positioning accuracy in noisy environments while maintaining reasonable computational complexity through structured algorithm design
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 approach provides superior positioning accuracy compared to state-of-the-art methods, overcoming the limitations of existing technologies by offering robust and precise localization in challenging environments with reduced errors and improved precision.
Implementation Method 1
collecting sound wave data snapshots over time from beacon signals transmitted from two known location beacons
Implementation Method 2
estimating azimuth and elevation angles-of-arrival of the transmitted beacon signals via Doppler-, multipath-, and impulsive noise-resistant tensor subspaces
Implementation Method 3
severely affected by time-varying multipath propagation (due to bottom/surface signal reflections)
Data Source
AI summary
A practically implementable robust direction-of-arrival (DoA) estimation approach that is resistant to localization errors due to mobility, multipath reflections, impulsive noise, and multiple-access interference. As part of the disclosed invention the inventors consider infrastructure-less 3D localization of autonomous underwater vehicles (AUVs) with no GPS assistance and no availability of global clock synchronization. The proposed method can be extended to challenging communication environments and applied for the localization of assets/objects in space, underground, intrabody, underwater and other complex, challenging, congested and sometimes contested environments. Each AUV leverages known-location beacon signals to self-localize and can simultaneously report its sensor data and measurement location. The approach uses two known location beacon nodes, where the beacons are single-hydrophone acoustic nodes that are deployed at known locations and transmit time-domain coded signals in a spread-spectrum fashion.