Threshold Selection Algorithm Based on Skewness and Standard Deviation Using Back Propagation Artificial Neural Networks in the 60GHz Wireless Communication Systems
Threshold Selection Algorithm Based on Skewness and Standard Deviation Using Back Propagation Artificial Neural Networks in the 60GHz Wireless Communication Systems
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DOI：
10.14257/ijsh.2016.10.2.23
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Xiaolin Liang;Hao Zhang;Tingting Lu;Xue-rong Cui;T. Gulliver
中科院分区：
文献类型：
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作者：
Xiaolin Liang;Hao Zhang;Tingting Lu;Xue-rong Cui;T. Gulliver
精确定位是传感器网络领域的研究热点，60 GHz脉冲无线电信号具有低成本、低复杂度等优点，尤其是具有较高的时间分辨率和多径分辨率等特点，在测距、定位和跟踪系统中更具有实用价值，因此准确估计60 GHz脉冲无线电信号的到达时间（TOA）是非常重要的。为了提高TOA估计的精度，提出了一种基于能量检测后偏度和标准差联合度量的BP神经网络阈值选择算法。基于信噪比（SNR）的最佳阈值的研究和积分周期和信道模型的影响进行了检查。仿真结果表明，对于IEEE802.15.3c信道模型CM1.1和CM2.1，所提出的BP-ANN算法提供了更好的精度和鲁棒性在高和低信噪比环境比其他ED为基础的算法。
Accurate localization has gained significant interest in the field of sensor networks, impulse radio 60GHz signals which is low cost, low complexity are even much more practical for ranging, localization and tracking systems because of the high time and multipath resolution and so on. Typically, accurate Time of Arrival (TOA) estimation of the 60GHz signals is very important. In order to improve the precision of the TOA estimation, a new threshold selection algorithm using Back Propagation Artificial Neural Networks (BP-ANN) is proposed which is based on a joint metric of Skewness and Standard Deviation after Energy Detection. The best threshold based on the signal-to-noise ratio (SNR) is investigated and the effects of the integration period and channel model are examined. Simulation results are presented which show that for the IEEE802.15.3c channel models CM1.1 and CM2.1, the proposed BP-ANN algorithm provides better precision and robustness in both high and low SNR environments than other ED-based algorithms.