Survey on the performance of source localization algorithms

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Fresno, José Manuel and Robles, Guillermo and Martínez-Tarifa, Juan Manuel and Stewart, Brian G. (2017) Survey on the performance of source localization algorithms. Sensors, 17 (11). 2666. ISSN 1424-8220 (https://doi.org/10.3390/s17112666)

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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.

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