Survey on the performance of source localization algorithms

José Manuel Fresno, Guillermo Robles, Juan Manuel Martínez-Tarifa, Brian G. Stewart

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques to source localization which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on Newton-Raphson to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS is further proposed in this paper. The performance of all algorithms is analyzed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect in the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time is the combined MLE-HLS algorithm.
LanguageEnglish
Article number2666
Number of pages25
JournalSensors
Volume17
Issue number11
DOIs
Publication statusPublished - 18 Nov 2017

Fingerprint

Least-Squares Analysis
estimators
Maximum likelihood
sensors
Sensors
emitters
Surveys and Questionnaires
optimization
Nonlinear equations
layouts
Particle swarm optimization (PSO)
newton
positioning
nonlinear equations
arrivals
proximity
antennas
sampling
Antennas
Sampling

Keywords

  • source Localization
  • emitter localization
  • standard least squares
  • hyperbolic least squares
  • hyperbolic positioning
  • maximum likelihood estimator
  • Bancroft
  • particle swarm optimization
  • combined algorithm

Cite this

Fresno, J. M., Robles, G., Martínez-Tarifa, J. M., & Stewart, B. G. (2017). Survey on the performance of source localization algorithms. Sensors, 17(11), [2666]. https://doi.org/10.3390/s17112666
Fresno, José Manuel ; Robles, Guillermo ; Martínez-Tarifa, Juan Manuel ; Stewart, Brian G. / Survey on the performance of source localization algorithms. In: Sensors. 2017 ; Vol. 17, No. 11.
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Fresno, JM, Robles, G, Martínez-Tarifa, JM & Stewart, BG 2017, 'Survey on the performance of source localization algorithms' Sensors, vol. 17, no. 11, 2666. https://doi.org/10.3390/s17112666

Survey on the performance of source localization algorithms. / Fresno, José Manuel; Robles, Guillermo; Martínez-Tarifa, Juan Manuel; Stewart, Brian G.

In: Sensors, Vol. 17, No. 11, 2666, 18.11.2017.

Research output: Contribution to journalArticle

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AB - The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques to source localization which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on Newton-Raphson to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS is further proposed in this paper. The performance of all algorithms is analyzed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect in the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time is the combined MLE-HLS algorithm.

KW - source Localization

KW - emitter localization

KW - standard least squares

KW - hyperbolic least squares

KW - hyperbolic positioning

KW - maximum likelihood estimator

KW - Bancroft

KW - particle swarm optimization

KW - combined algorithm

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