Abstract

Time difference of arrival (TDoA) based on a group of sensor nodes with known locations has been widely used to locate targets. Two-step weighted least squares (TSWLS), constrained weighted least squares (CWLS), and Newton–Raphson (NR) iteration are commonly used passive location methods, among which the initial position is needed and the complexity is high. This paper proposes a hybrid firefly algorithm (hybrid-FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search region for the FA method. Simulations showed that the hybrid-FA method required far fewer iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments were conducted to compare the results of hybrid-FA with other methods. The findings indicated that the root-mean-square error (RMSE) and mean distance error of the hybrid-FA method were lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid-FA outperformed the NR, TSWLS, and GA for TDoA measurement.

Highlights

  • Target localization based on a group of sensor nodes whose positions are known has been extensively studied in research on signal processing [1,2,3]

  • The results of the proposed method were compared with the constrained weighted least squares (CWLS), NR, Two-step weighted least squares (TSWLS), and genetic algorithm (GA)

  • The hybrid-firefly algorithm (FA) method could cut down the computation of the algorithm with high accuracy compared with using the FA only

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Summary

Introduction

Target localization based on a group of sensor nodes whose positions are known has been extensively studied in research on signal processing [1,2,3]. It has been applied widely in military and civil fields, including sensor networks [4], wireless communication [2], radar [5], navigation, and so forth [6,7,8]. For a passive location system based on TDoA, once the measured data are obtained, the range difference between the target and two different sensor nodes can be calculated. The solving algorithms commonly adopted include iterative, analytical, and search methods

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