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3D TDOA Emitter Localization Using Conic Approximation.

Kutluyil Dogancay1, Hatem Hmam2

  • 1UniSA STEM, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces a novel 3D emitter localization algorithm using time difference of arrival (TDOA) conic approximations. The method achieves high accuracy, even in challenging conditions, outperforming existing techniques.

Keywords:
iterative weighted least squaresmaximum likelihood estimatortime difference of arrival localization

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Area of Science:

  • Signal Processing
  • Estimation Theory
  • Geospatial Analysis

Background:

  • Emitter localization is crucial for various applications, including surveillance and navigation.
  • Traditional methods using Time Difference of Arrival (TDOA) often struggle with accuracy in 3D space and noisy environments.
  • Existing algorithms may require complex computations or perform suboptimally under certain geometric conditions.

Purpose of the Study:

  • To develop a novel and computationally efficient algorithm for 3D emitter localization using TDOA measurements.
  • To improve localization accuracy, particularly in scenarios with poor sensor-emitter geometries and high noise levels.
  • To analyze the impact of sensor-emitter and sensor array geometries on localization performance.

Main Methods:

  • The proposed algorithm converts TDOA measurements into 1D Angle of Arrival (1D-AOA) measurements, defining TDOA cones.
  • Emitter location is determined via triangulation of 1D-AOAs, formulated as a nonlinear system of equations.
  • A two-stage estimation approach is employed, combining an Iterative Weighted Least Squares (IWLS) estimator with a Taylor series refinement for improved accuracy and low complexity.

Main Results:

  • Numerical simulations demonstrate the algorithm's effectiveness in 3D emitter localization.
  • The two-stage estimator achieves performance close to the Cramer-Rao Lower Bound (CRLB), even in challenging geometries and high noise.
  • The algorithm outperforms the Maximum Likelihood Estimator (MLE) under adverse conditions.
  • The approximate efficiency of the IWLS estimator is established under mild conditions.

Conclusions:

  • The developed TDOA-based algorithm offers a robust and accurate solution for 3D emitter localization.
  • The conic approximation approach effectively handles TDOA measurements for improved spatial estimation.
  • The two-stage estimator provides a computationally efficient method that maintains high accuracy, making it suitable for real-world applications.