Visible light communications(VLC) have recently attracted a growing interest and can be a potential solution to realize indoor positioning,however,the performance of existing indoor positioning system is limited by mu...Visible light communications(VLC) have recently attracted a growing interest and can be a potential solution to realize indoor positioning,however,the performance of existing indoor positioning system is limited by multipath distortion inside a room.In order to combat the effect of multipath distortion,this paper proposes an LED-based indoor positioning algorithm combined with hybrid OFDM(HOFDM),in which asymmetrically clipped optical OFDM(ACOOFDM) is transmitted on the odd subcarriers while using pulse amplitude modulated discrete multitone(PAM-DMT) to modulate the imaginary part of each even subcarrier.In this scheme,we take a combined approach where a received-signal-strength(RSS) technique is employed to determine the location of the receiver and realize the 3-D positioning by Trust-region-based positioning.Moreover,a particle filter is used to further improve the positioning accuracy.Results confirm that this proposed positioning algorithm can achieve high accuracy even with multipath distortion,and the algorithm has better performance when combined with particle filter.展开更多
由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具...由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具有较高计算复杂度。针对以上的缺点,提出了一种改进的双标签LANDMARC定位算法,通过定义双标签,即一个有源标签和一个无源标签,来定位目标标签的定位模型,该算法命名为DLANDMARC。由于无源标签被感应的距离有限,只能被处在它附近的待定位标签感应到,从而大大降低选错参考标签的概率并减小了计算开销。实验表明,DLANDMARC算法在定位精度、定位时间以及算法的稳定性比文献中已有的几种算法有明显改善。展开更多
室内区域定位在医疗养老、智慧大楼等领域有着广泛的应用.室内区域定位中最突出的问题是无线电信道效应的动态和不可预测性(如多径传播、信道衰落等)对接收信号强度(received signal strength, RSS)的干扰影响.为了降低无线电的干扰,提...室内区域定位在医疗养老、智慧大楼等领域有着广泛的应用.室内区域定位中最突出的问题是无线电信道效应的动态和不可预测性(如多径传播、信道衰落等)对接收信号强度(received signal strength, RSS)的干扰影响.为了降低无线电的干扰,提出了一种新的基于注意力机制的CNN-BiLSTM的室内区域定位模型,该模型通过捕获粗细粒度特征与定位区域的对应关系来减弱RSS序列对信道变化的依赖.首先,利用卷积神经网络(convolutional neural network, CNN)学习捕捉RSS序列的特征来抽取区域中心点的细粒度特征.然后,利用双向长短时记忆(bidirectional long short-term memory, BiLSTM)网络的存储记忆特性,学习当前与过去RSS序列中隐含区域范围的粗粒度特征.最后,利用注意力机制,通过融合粗细粒度特征,建立RSS序列特征与区域位置的映射关系,获取区域位置信息.真实室内环境下区域定位的实验结果表明,与目前定位效果最好的网格区域综合概率定位模型相比,提出的方法在降低计算复杂度的同时提高了区域定位的准确度和对环境的适应能力.展开更多
基金supported by the Doctoral Scientific Fund of the Ministry of Education of the People’s Republic of China(20120145120011)
文摘Visible light communications(VLC) have recently attracted a growing interest and can be a potential solution to realize indoor positioning,however,the performance of existing indoor positioning system is limited by multipath distortion inside a room.In order to combat the effect of multipath distortion,this paper proposes an LED-based indoor positioning algorithm combined with hybrid OFDM(HOFDM),in which asymmetrically clipped optical OFDM(ACOOFDM) is transmitted on the odd subcarriers while using pulse amplitude modulated discrete multitone(PAM-DMT) to modulate the imaginary part of each even subcarrier.In this scheme,we take a combined approach where a received-signal-strength(RSS) technique is employed to determine the location of the receiver and realize the 3-D positioning by Trust-region-based positioning.Moreover,a particle filter is used to further improve the positioning accuracy.Results confirm that this proposed positioning algorithm can achieve high accuracy even with multipath distortion,and the algorithm has better performance when combined with particle filter.
文摘由于室内环境存在严重干扰,导致经典室内定位算法LANDMARC(location identification based on dynamic active RFID calibration)在定位目标时出现选错参考标签的概率增大;此外,还需计算待定位标签和每个参考标签之间的欧氏距离,因而具有较高计算复杂度。针对以上的缺点,提出了一种改进的双标签LANDMARC定位算法,通过定义双标签,即一个有源标签和一个无源标签,来定位目标标签的定位模型,该算法命名为DLANDMARC。由于无源标签被感应的距离有限,只能被处在它附近的待定位标签感应到,从而大大降低选错参考标签的概率并减小了计算开销。实验表明,DLANDMARC算法在定位精度、定位时间以及算法的稳定性比文献中已有的几种算法有明显改善。
