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Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm

机译:加权最小二乘和萤火虫算法相结合的到达时间差(TDoA)定位

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摘要

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.
机译:基于具有已知位置的一组传感器节点的到达时间差(TDoA)已广泛用于定位目标。两步加权最小二乘(TSWLS),约束加权最小二乘(CWLS)和牛顿-拉夫森(Newton-Raphson)迭代是常用的被动定位方法,其中需要初始位置并且复杂度很高。提出了一种结合萤火虫算法(hybrid-FA)的方法,将加权最小二乘(WLS)算法和FA相结合,可以减少计算量并达到较高的精度。首先执行WLS算法,其结果用于限制FA方法的搜索区域。仿真表明,混合FA方法所需的迭代次数比单独的FA方法要少得多,以实现相同的精度。另外,进行了两个实验以比​​较杂交FA与其他方法的结果。结果表明,混合FA方法的均方根误差(RMSE)和平均距离误差均低于NR,TSWLS和遗传算法(GA)。总体而言,混合FA在TDoA测量方面的性能优于NR,TSWLS和GA。

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