Reconstructing Spatial Localization Error Maps via Physics-Informed Tensor Completion for Passive Sensor Systems.

Area of Science:

• Sensor systems engineering
• Data science and machine learning
• Signal processing

Background:

• Accurate localization error mapping is crucial for passive sensor systems and placement.
• Conventional methods like Geometrical Dilution of Precision (GDOP) use idealized models, failing in real-world heterogeneous environments.

Purpose of the Study:

• To develop a novel data-driven framework for reconstructing high-fidelity localization error maps from sparse observations.
• To address the limitations of traditional analytical methods in capturing complex error distributions.

Main Methods:

• Modeling the error distribution as a tensor and employing tensor completion for reconstruction.
• Utilizing a physics-informed regularization strategy incorporating analytical error covariance knowledge.
• Applying tensor factorization for robust error map recovery from incomplete data.

Main Results:

• The proposed framework reconstructs high-fidelity localization error maps from sparse Time Difference of Arrival (TDOA) data.
• Physics-informed regularization enables robust recovery of complete error maps even with highly incomplete data.
• Experiments demonstrated at least 27.96% accuracy improvement over state-of-the-art methods on a real-world dataset.

Conclusions:

• The novel data-driven framework offers superior performance for localization error mapping compared to conventional methods.
• This approach enhances the assessment of passive sensor systems and guides sensor placement strategies.
• The physics-informed tensor completion method provides a robust solution for complex, real-world error distributions.