UHF频段窄带微波室内定位技术研究

发布时间：2018-05-17 00:34

本文选题：室内定位 + m序列　；参考：《广州大学》2017年硕士论文

【摘要】：过去的几年里,在许多公共服务中,室内高精度定位的需求迅速增加,特别是基于微波传输的厘米级精度的定位技术的需求越来越大。然而,目前的WLAN以及ZigBee定位技术只能提供米级的定位精度;而超宽带技术虽然可以提供厘米级的精度,但由于使用超高频60 GHz的通信技术,使得其成本非常高,无法广泛应用。定位技术通常通过计算发射机和基站的距离,然后根据三角测量算法来确定发射机的位置。而在微波传输中,可根据传输时间来得到距离,因而其定位的精度又取决于时间的测量。扩频技术由于具有良好的自相关特性以及抗干扰能力强等特点,能够很好的获取到精准时间测量,因此可以用扩频技术进行高精度的定位,且时间检测的精度与码元宽度有关,码元宽度越窄,精度越高。但码元宽度越窄也会导致采样率以及硬件成本的提高,并加大了系统的实现难度。由于相位检测比时间检测更容易实现,室内定位系统可以通过利用扩频信号良好的自相关特性来测量相干相位,在得到较为准确的到达时间的同时也降低了采样率以及硬件成本。因此论文提出基于1 GHz频率下的扩频信号小波自相关相位测量的室内定位算法,实现低成本高精度的定位目的。然而,通过实验发现,噪声对精度有很大的影响,因此有必要对接收到的信号进行去噪后再进行自相关运算。而目前的时域或频域滤波方法只能对高频带噪声进行降噪,这使得测量精度较低。因此,论文提出了一种基于矩形窗分段阈值的低频带降噪算法。该方法基于时频小波域进行阈值降噪,利用本地扩频信号同一子带的方差作为参考,根据方差之间的大小关系对带噪声的扩频信号进行小波分段阈值降噪,并在降噪后进行小波低频带自相关。仿真结果表明,与其他滤波方法相比,本文提出的小波自相关和低频带分段阈值去噪算法可以消除噪声的影响,并提高定位精度。
[Abstract]:In the past few years, the demand for indoor high-precision positioning has increased rapidly in many public services, especially the demand for centimeter-precision positioning technology based on microwave transmission. However, the current WLAN and ZigBee positioning technology can only provide meter level positioning accuracy, while UWB technology can provide centimeter level accuracy, but because of the use of UHF 60 GHz communication technology, its cost is very high, so it can not be widely used. The location technique usually calculates the distance between the transmitter and the base station, and then determines the location of the transmitter according to the triangulation algorithm. In microwave transmission, the distance can be obtained according to the transmission time, so the positioning accuracy depends on the measurement of time. Because of its good autocorrelation characteristics and strong anti-jamming ability, spread spectrum technology can obtain accurate time measurement, so it can be used to locate accurately, and the accuracy of time detection is related to symbol width. The narrower the symbol width, the higher the precision. However, the narrower the symbol width, the higher the sampling rate and hardware cost, and the more difficult the system is to realize. Because phase detection is easier than time detection, the indoor positioning system can measure coherent phase by using the good autocorrelation characteristic of spread spectrum signal. At the same time, the sampling rate and hardware cost are reduced. Therefore, an indoor localization algorithm based on wavelet autocorrelation phase measurement of spread spectrum signals at 1 GHz frequency is proposed to achieve the purpose of low cost and high precision. However, it is found that the noise has a great influence on the precision, so it is necessary to Denoise the received signal and then carry out autocorrelation operation. However, the current filtering methods in time domain or frequency domain can only reduce the noise in high frequency band, which makes the measurement accuracy low. Therefore, a low frequency band denoising algorithm based on the segmented threshold of rectangular window is proposed in this paper. This method is based on time-frequency wavelet domain for threshold denoising, using the variance of the same sub-band of local spread spectrum signal as a reference, according to the relationship between the variance of the spread spectrum signal with noise wavelet segmentation threshold noise reduction. After noise reduction, wavelet low frequency band autocorrelation is carried out. The simulation results show that compared with other filtering methods, the proposed wavelet autocorrelation and low-frequency segmented threshold denoising algorithm can eliminate the noise and improve the positioning accuracy.
【学位授予单位】：广州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN914.42

【参考文献】