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Coordinated Positioning Method for Shortwave Anti-Multipath Based on Bayesian Estimation.

Tao Tang1, Linqiang Jiang1, Paihang Zhao1

  • 1Institute of Information Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for beyond-visual-range target positioning using direction of arrival (DOA) and time difference of arrival (TDOA) that enhances accuracy in multipath environments. The approach improves coordinated positioning by mitigating errors caused by signal reflections.

Keywords:
anti-multipathcoordinated positioningdeep learningprior informationshortwave

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Area of Science:

  • Electromagnetics and Signal Processing
  • Array Signal Processing
  • Target Localization

Background:

  • Coordinated positioning using Direction of Arrival (DOA) and Time Difference of Arrival (TDOA) is crucial for beyond-visual-range target localization with shortwave signals.
  • Existing methods suffer from reduced accuracy in multipath propagation, particularly in estimating the elevation angle, which degrades overall positioning precision.
  • Multipath propagation significantly challenges accurate target positioning by distorting signal paths and affecting angle estimations.

Purpose of the Study:

  • To propose a new coordinated positioning method combining DOA-TDOA and Bayesian estimation for enhanced performance in shortwave anti-multipath scenarios.
  • To address the degradation in elevation angle estimation accuracy caused by multipath propagation.
  • To improve the overall accuracy and robustness of beyond-visual-range target positioning.

Main Methods:

  • Modeling the elevation angle as a random variable with a probability distribution that captures multipath characteristics.
  • Utilizing a convolutional neural network to analyze 3D spatial spectrograms for path identification and elevation angle probability distribution estimation.
  • Modifying elevation angle estimates using their probability distributions and employing matrix QR decomposition for iterative position estimation in both DOA and TDOA stages.

Main Results:

  • The proposed method demonstrates superior anti-multipath capability compared to traditional techniques.
  • Significant improvements in the coordinated positioning accuracy for both DOA and TDOA were achieved.
  • Validation using measured data confirmed the effectiveness of the proposed anti-multipath positioning strategy.

Conclusions:

  • The developed Bayesian estimation-based method effectively mitigates multipath interference in DOA-TDOA coordinated positioning.
  • The integration of convolutional neural networks and probabilistic modeling enhances elevation angle estimation accuracy under multipath conditions.
  • The proposed technique offers a robust and accurate solution for beyond-visual-range target positioning in challenging shortwave environments.