现有非绑定式定位方法面临信号多径效应导致定位精度低、通用设备定位鲁棒性差的挑战和实施代价大的问题。项目旨在借助信号幅度与相位信息构建监测区域多径信号空间谱，利用目标对信号空间谱干扰的分布规律实现高精度定位；利用相位差建立非线性方程，用优化方法实现相位校准与稳定，提升定位鲁棒性；采用合成孔径雷达(SAR)稀疏布阵思想，研究天线拓扑对多径信号空间谱估计精度的影响，在保证信号空间谱估计精度前提下减少阵列天线数量，降低定位成本。建立无需学习的非绑定式定位模型，将定位问题转化为非线性优化求解，解决学习方法勘测成本高及场景变化致模型失用问题。针对典型应用场景，设计非绑定式定位原型系统，验证所提方法有效性。其科学实质是通过目标对多径信号空间谱干扰规律研究，寻求无线信号与目标的时空关联规律，探索定位精度、鲁棒性、可用性和环境时变性间的本质联系，研究成果有望为无线网络行为监测应用提供有价

Prior works in device-free localization (DfL) typically face two key challenges: 1) poor positioning accuracy under multipath effects, and 2）poor robustness using general equipment, and 3) high hardware and deployment cost. In this project, we build a multipath signal spectrum based on amplitude-phase information of the single, and use the statistical characteristics of signals, which caused by the moving target, for fine-grained target localization. Then a phase calibration technique based on nonlinear optimization is proposed to effectively eliminate the instability of the phase of wireless signals, which significantly improves the positioning robustness. In addition, based on our analysis on the relationship between antenna topology and estimation performance of the multipath signal spectrum, we minimize the antenna numbers while satisfying the desired accuracy by using synthetic aperture radar (SAR). By modelling the device-free localization problem as a set of over-determined equations, we can determine locations of the target by solving the nonlinear optimization problem, which helps to reduce the survey cost and avoid model mismatch problem. At last, we implement our system for several representative application scenarios and evaluate its effectiveness under different conditions..In conclusion, the main idea behind our project is to explore the relationship between the signal change and the target location by analyzing how target affects the wireless signal. In addition, we explore the inherent relationship among localization accuracy, usability, robustness and the unstability caused by changing environment. The research findings of this project will provide theoretical support and reference for the behavior monitoring applications in wireless network.