无设备定位的建模与优化方法研究

发布时间：2018-03-07 02:36

本文选题：无线定位　切入点：无设备定位　出处：《北京科技大学》2017年博士论文　论文类型：学位论文

【摘要】：无设备定位(Device-Free Localization,DFL)是一种目标无需携带任何电子设备的无线定位技术,已被广泛地应用于各个领域,如入侵检测、紧急救援、老人看护等。该无线定位技术通过分析目标所带来的监测区域内无线射频信号的变化得以实现。但由于受环境时变、非视距、多路径信号传播等不确定性因素的影响,实现高精度、抗干扰能力强的DFL仍是亟待解决的、挑战性的问题。构建合理的无线信号传播模型是提高DFL精度的有效途径,为此本文对该模型及相应的DFL优化求解方法进行了深入的研究。论文主要工作和研究成果包括以下几个方面:(1)通过分析影响链路信号强度波动的因素,提出联合链路检测方法,实现监测区域内受影响链路的联合选取及异常链路的剔除。(2)基于链路的几何检测模型,对DFL进行优化方法研究。采用链路直线检测模型,提出了 DFL的非线性目标函数优化方法,并利用凸优化理论进行求解;为了进一步提高定位精度,采用链路椭圆检测模型,提出了基于凸可行并行投影的DFL方法;通过实验验证了所提方法具有较好的定位效果,并优于已有定位方法。(3)为解决在复杂环境下DFL精度面临恶化的问题,通过分析目标对链路信号强度的影响,构建了基于高斯过程的无线射频信号传播模型,并通过实验测试该模型的性能。(4)基于高斯过程无线射频信号传播模型,结合联合链路检测方法,将DFL问题转化为极大似然概率优化问题,并提出了相应的改进果蝇优化求解方法;针对目标跟踪问题,提出了基于高斯过程无线射频信号传播模型的粒子滤波方法,结合目标运动模型对粒子进行预测和更新,实现对目标的跟踪;分别在空旷的室外场景、及复杂的室内场景搭建实验平台对所提方法进行测试,验证了所提方法的定位与跟踪效果。
[Abstract]:Device-Free Localization #en0# (DFL) is a wireless location technology with no need to carry any electronic devices. It has been widely used in various fields, such as intrusion detection, emergency rescue. The wireless location technology is realized by analyzing the changes of radio frequency signals in the monitoring area brought about by the target. However, due to the influence of uncertain factors such as environmental time-varying, non-line-of-sight, multipath signal propagation, etc., this wireless location technology is realized by analyzing the changes of radio frequency signals in the monitoring area brought about by the target. It is still an urgent and challenging problem to realize DFL with high precision and strong anti-jamming ability. It is an effective way to improve the accuracy of DFL to build a reasonable wireless signal propagation model. In this paper, the model and the corresponding DFL optimization method are studied in depth. The main work and research results include the following aspects: 1) by analyzing the factors affecting the fluctuation of link signal intensity, a joint link detection method is proposed. To realize the joint selection of the affected links in the monitoring area and the elimination of the abnormal links. (2) based on the geometric detection model of the link, the optimization method of DFL is studied, and the link line detection model is adopted. The nonlinear objective function optimization method of DFL is proposed, and the convex optimization theory is used to solve the problem. In order to further improve the positioning accuracy, a DFL method based on convex feasible parallel projection is proposed by using the link ellipse detection model. The experimental results show that the proposed method has good localization effect, and is superior to the existing localization method. In order to solve the problem of deterioration of DFL precision in complex environment, the effect of target on link signal strength is analyzed. A radio frequency signal propagation model based on Gao Si process is constructed, and the performance of the model is tested experimentally. The DFL problem is transformed into the maximum likelihood probability optimization problem, and the corresponding improved optimization method for fruit fly is proposed, and a particle filter method based on Gao Si process radio frequency signal propagation model is proposed for target tracking. Combined with the target motion model to predict and update the particles to achieve the target tracking, respectively in the open outdoor scene, and complex indoor scene to build an experimental platform to test the proposed method, The localization and tracking effect of the proposed method is verified.
【学位授予单位】：北京科技大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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