基于多维层析成像的被动无线定位跟踪方法研究

Device-free localization and tracking is a promising technique which could realize the target localization and tracking task without the need of equipping the target with a wireless device. It utilizes the shadowing effect and realizes location estimation based on the information of the shadowed wireless links. And with this technique, the wireless networks will have the ability of knowing the location of the target within its deployment area. This promising technique has potential application in the field where the target is non-cooperative, such as security, emergency rescue, and battlefield monitoring. As an emerging technique, the device-free localization and tracking technique has lots of fundamental problems to be solved, such as system architecture, link observation model, the localization and tracking algorithms, etc.. In particular, the location estimation performance will drop dramatically in the sparse networks where only modest set of wireless nodes are available. The objective of this proposal is to tackle the above problems. Firstly, the device-free localization and tracking scheme suitable for sparse networks is presented. We model the device-free localization and tracking problem as a multi-dimensional radio tomographic imaging question. A frequency-diversity and power-diversity based multi-dimensional scanning method is proposed to improve the utilization efficiency of wireless links and the performance of radio tomographic imaging. And then, the localization problem is modeled as a sequential sparse signal reconstruction question. We utilize the space domain sparse feature and time domain gradually change feature of the location information, and use the compressive sensing theory and Bayesian estimation theory to make full use of the above feature to realize location estimation. Secondly, we propose robust schemes to deal with the wireless link measurements and restrain the negative effect of the noise, and develop an effective statistical model to relate the variation of link measurements with the spatial location of target. The above works will improve the performance of the device-free localization system, and provide relevant theoretical foundation for lots of position- oriented applications.

被动无线定位跟踪技术是一种无需目标携带无线设备的新兴定位技术，它利用目标对无线链路的遮蔽效应实现对目标位置的估计，从而使无线网络具备感知网络覆盖范围内目标位置的能力。该技术在安防、救援、战场监测等目标不配合的领域有着广泛的应用前景。但是，其在模型及算法等方面存在诸多亟待解决的问题，尤其在无线链路较稀疏的条件下定位性能较差，本课题拟对此展开研究。首先，研究稀疏无线网络中提高定位跟踪性能的方法。提出将定位系统建模为多维层析成像问题，采用频率及功率分集方法实现多维扫描提高链路利用效率；提出将位置估计问题建模为连续渐变稀疏信号重构问题，充分利用位置信息的空间域稀疏及时间域连续渐变特性，采用压缩感知理论及贝叶斯估计方法实现定位跟踪。其次，研究无线链路观测信息的处理与建模方法，对噪声抑制方法、链路与目标关系模型等进行研究。课题成果将显著改善被动定位跟踪性能，为其在无线网络中的应用提供理论基础。

被动无线定位跟踪技术是一种可以在目标不携带任何设备的条件下实现对目标定位的新技术，该技术在安防、救援、战场监测等目标不配合的领域有着广泛的应用前景。本课题针对被动无线定位中的观测模型、位置估计算法等展开研究，实现了多维无线层析成像。在自然科学基金的资助下，课题组发表学术论文11篇，其中，SCI期刊长文论文10篇，所提出的具有轻计算复杂度的被动定位算法发表在期刊IEEE Trans. Industrial Electronic (IF 6.383 一区)，提出的多维无线层析方法发表在IEEE Trans. Vehicle Technology (IF 1.978 二区)，提出的具有高执行效率、低能耗的TOF成像方法发表在期刊IEEE Trans. Industrial Informatics (IF 4.708 一区)，提出的可以详细描述人体对无线链路遮蔽效应的马鞍面模型发表在IEEE Trans. Vehicle Technology (IF 1.978 二区)，课题组在观测模型、被动定位算法、被动定位系统设计方法、网络系统等方面取得系列研究成果，有多篇论文获得辽宁省自然科学优秀论文。项目成果为实现高性能的被动无线定位跟踪、无线层析成像提供了理论和方法保障，所提出的方法对于被动无线定位领域的研究具有普适意义和推动作用。