DUE TO its potential and promising commercial and military applications, wireless localization technique has drawn extensive attention in recent years. Most of traditional wireless localization techniques, such as sensor networks localization , Lightweight Robust Device-Free Localization in Wireless Networks RFID localization , robot and pedestrian localization , equip the target with a wireless device which emits signals that can be detected by some anchor nodes whose locations are known a prior, and localization is realized in a cooperative way by utilizing the wireless measurements between the target and anchor nodes. However, in some applications, such as battlefield surveillance, security safeguard, and emergency rescue, the target is uncooperative, and thus it is impractical to equip the target with a wireless device. Lightweight Robust Device-Free Localization in Wireless Networks How to achieve device-free localization (DFL) without the need of equipping the target with a wireless device becomes a hallenging problem in such a scenario. Within the deployment area of the wireless networks (WNs), communications between pairs of nodes construct lots of wireless links which travel through the space. When a target moves into the area, it may shadow some of the wireless links and absorb, diffract, reflect or scatter some of the transmitted power. The shadowed links will be different when the target locates at different locations Lightweight Robust Device-Free Localization in Wireless Networks, which makes it possible to realize DFL based on the link measurements. TheDFL techniquewas originally proposed independently by Youssef et al. and Zhang et al.. Youssef et al. modeled the problem as a machine learning problem and realized DFL with a fingerprint matching method. Zhang et al. presented a signal dynamic model, and adopted the geometric method as well as the dynamic cluster based probabilistic cover algorithm to solve the DFL problem. These works make valuable exploration on the DFL problem, and prove the feasibility of making use of the shadowing effect of the Lightweight Robust Device-Free Localization in Wireless Networks wireless links to realize DFL. However, the machine learning method requires an off-line training process which is laborious and time-consuming, while the geometric method is sensitive to noises since it uses only the current observation to realize location estimation. More recently, Savazzi et al. evaluated DFL technique with plenty of experiments. Wilson et al. and Zhao et al Lightweight Robust Device-Free Localization in Wireless Networks.