Threshold selection method for UWB TOA estimation based on wavelet decomposition and kurtosis analysis
Threshold selection method for UWB TOA estimation based on wavelet decomposition and kurtosis analysis
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基于小波分解和峰度分析的UWB TOA估计阈值选择方法
DOI：
10.1186/s13638-017-0990-4
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发表时间：
2017-11
影响因子：
2.6
通讯作者：
Jianhang Liu
中科院分区：
文献类型：
--
作者：
Juan Li;Xuerong Cui;Houbing Song;Zhongwei Li;Jianhang Liu
在无线传感器网络中，通过超宽带（UWB）进行测距或定位引起了广泛的研究兴趣，其中低采样率和低复杂度的非相干能量检测（ED）方法被广泛研究。然而传统的能量检测方法仅分析时域信号能量，误差较大。本文的仿真结果表明，大部分信号能量集中在低频段，因此提出了一种新的到达时间（TOA）估计阈值选择方法，该方法在时域和频域上分析信号。该方法对接收信号进行“db6”小波分解，并分析低频小波系数(Kc)能量块的峰度。最后，利用3次多项式拟合建立Kc与TOA估计归一化阈值之间的映射关系。仿真结果表明，该方法的TOA估计误差明显小于未进行小波分解的方法。
In wireless sensor networks, ranging or positioning via ultra-wideband (UWB) has caused widespread research interests where the non-coherent energy detection (ED) method with low sampling rate and low complexity is widely studied. However, the traditional energy detection methods only analyze the signal energy in the time domain, so their error is relatively large. In this paper, the simulation results show that most of the signal energy concentrates in the low-frequency band, so a novel threshold selection method for time of arrival (TOA) estimation is proposed that analyzes the signals in both time domain and frequency domain. In this method, the received signal is decomposed by “db6” wavelet and the kurtosis of energy blocks of the low-frequency wavelet coefficients (Kc) is analyzed. At last, the mapping relationship betweenKcand the normalized threshold for TOA estimation is created using polynomial fitting with degree 3. The simulation results show that the TOA estimation error of the proposed method is significantly less than the method without wavelet decomposition.
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发表时间：
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期刊：
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