基于超宽带雷达的室内定位关键技术的研究

发布时间：2018-11-01 10:32

【摘要】：超宽带雷达具有大宽带,抗干扰性强,低截获概率等优势,因此近年来受到了研究者们的广泛关注。超宽带雷达在定位领域,尤其是室内定位领域的研究也越来越多。本文针对冲击体制下的超宽带雷达,分别研究了在室内一维距离目标定位与追踪和室内二维平面目标定位与追踪下,从原始雷达信号中对目标回波信号的提取与分离,并进行了详细的算法研究和实验探究,同时利用上位机对算法进行了实现。论文首先介绍了超宽带雷达在定位领域的研究现状,并与其他体制下的定位系统进行了对比。其次介绍了本文所采用的雷达系统,并对该系统各模块的性能进行了分析,同时探讨了冲击体制下超宽带雷达目标探测的模型。本文随后研究了在室内一维距离定位情境下的高分辨目标定位与追踪算法。算法首先利用动目标检测算法和峰值检测算法,将轨迹信号初步提取出来,然后通过本文提出的基于熵值变化准则的经验模态分解算法对初步提取出来的含噪信号进行去噪重构,并与经过基于连续均方误差准则的经验模态分解去噪重构算法后得到的信号进行对比,通过实测数据验证,基于熵值变化准则的经验模态分解算法能有效地降低误差,得到更精确的运动轨迹;然后利用卡尔曼滤波追踪算法对实测信号进行目标追踪。本文随后研究了室内二维距离定位情境下的室内定位模型,其中包括方位到达角定位模型(AOA)、圆周定位模型(TOA)、双曲线定位模型(TDOA)等,并对双曲线定位模型下的Chan算法和Fang算法进行了深入研究。随后,本文提出了基于交互多模型的扩展卡尔曼滤波(IMM-EKF)追踪算法,并通过仿真实验证明该算法与经典扩展卡尔曼滤波追踪算法相比,IMM-EKF追踪算法具有更强的机动性、能有效降低突变噪声干扰的特性。最后,本文利用MFC平台,对该算法进行了上位机实现,并利用实测数据验证其算法和系统的可靠性与实时性。
[Abstract]:Ultra-wideband radar (UWB) has many advantages, such as large wideband, strong anti-jamming, low probability of interception and so on, so it has been paid more and more attention by researchers in recent years. UWB radar in the field of positioning, especially in the field of indoor positioning more and more research. In this paper, the extraction and separation of the target echo signal from the original radar signal under the indoor one-dimensional range target location and tracking and the indoor two-dimensional plane target location and tracking are studied respectively for the UWB radar under the impact system. At the same time, the algorithm is realized by the upper computer. This paper first introduces the research status of UWB radar in the field of positioning, and compares it with other positioning systems. Secondly, the radar system used in this paper is introduced, and the performance of each module of the system is analyzed. At the same time, the model of UWB radar target detection under impact system is discussed. Then, the high resolution target location and tracking algorithm in indoor one-dimensional range location is studied. The algorithm first uses the moving target detection algorithm and the peak detection algorithm to extract the trajectory signal. Then, the proposed empirical mode decomposition algorithm based on entropy change criterion is used to reconstruct the noise signal. Compared with the signal obtained by the empirical mode decomposition and reconstruction algorithm based on the continuous mean square error criterion, the empirical mode decomposition algorithm based on entropy change criterion can effectively reduce the error. Obtain more accurate motion trajectory; Then the Kalman filter tracking algorithm is used to track the measured signal. In this paper, the indoor positioning model under the condition of indoor two-dimensional distance positioning is studied, including the azimuth arrival angle location model, (AOA), circumferential positioning model, (TOA), hyperbolic positioning model, (TDOA), and so on. The Chan algorithm and Fang algorithm based on hyperbolic localization model are studied. Then, an extended Kalman filter (IMM-EKF) tracking algorithm based on interactive multiple models is proposed, and the simulation results show that the algorithm is compared with the classical extended Kalman filter tracking algorithm. The IMM-EKF tracking algorithm is more mobile and can effectively reduce the abrupt noise interference. Finally, using MFC platform, the algorithm is implemented on the host computer, and the reliability and real-time performance of the algorithm and the system are verified by the measured data.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958

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