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Survey on the Performance of Source Localization Algorithms.

José Manuel Fresno1, Guillermo Robles2, Juan Manuel Martínez-Tarifa3

  • 1Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda, Universidad, 30, Leganés, 28911 Madrid, Spain. jfresno@ing.uc3m.es.

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Summary
This summary is machine-generated.

Accurate emitter localization is crucial. This study compares various multilateration algorithms, finding the combined Maximum Likelihood Estimator-Hyperbolic Least Squares (MLE-HLS) algorithm offers the best balance of accuracy and speed for source localization.

Keywords:
Bancroftcombined algorithmemitter localizationhyperbolic least squareshyperbolic positioningmaximum likelihood estimatorparticle swarm optimizationsource localizationstandard least squares

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Localization Algorithms

Background:

  • Emitter localization is vital across numerous applications.
  • Multilateration techniques, relying on time variables like Time of Flight (ToF), Time Differences of Arrival (TDoA), and pseudo-Time of Flight (pToF), are key methods.
  • Existing algorithms include iterative (Standard Least Squares, Hyperbolic Least Squares, Particle Swarm Optimization) and non-iterative (Hyperbolic Positioning Algorithms, Maximum Likelihood Estimator, Bancroft) approaches.

Purpose of the Study:

  • To comprehensively analyze and compare the performance of various source localization algorithms.
  • To introduce and evaluate a novel non-iterative combined algorithm, MLE-HLS.
  • To assess algorithm performance considering different sensor layouts and source positions, including digital sampling effects.

Main Methods:

  • Comparison of iterative algorithms: Standard Least Squares (SLS), Hyperbolic Least Squares (HLS), and Particle Swarm Optimization (PSO).
  • Evaluation of non-iterative algorithms: Hyperbolic Positioning Algorithms (HPA), Maximum Likelihood Estimator (MLE), and Bancroft algorithm.
  • Development and analysis of a new non-iterative combined algorithm: MLE-HLS.

Main Results:

  • The combined MLE-HLS algorithm demonstrated superior performance compared to other evaluated methods.
  • MLE-HLS achieved a better balance between localization accuracy and computational time.
  • Algorithm performance was robust across different sensor configurations and source locations, even with digital sampling errors.

Conclusions:

  • The MLE-HLS algorithm presents a highly effective solution for emitter localization.
  • This combined approach offers improved accuracy and efficiency over traditional iterative and non-iterative methods.
  • The findings provide valuable insights for selecting optimal localization algorithms in practical applications.