Towards Cognitive Accurate Real-time Device-Free Wireless Localization

With the popularization of the wireless environment, it has broad market prospects for wireless localization technology in home intelligent, medical treatment, national defense et al.. As a new type of localization technology, compared with traditional localization (e.g. RFID, video surveillance, infrared ray, ultrasonic et al.), device-free wireless localization (DFL) does not equip the target being tracked with any wireless device. In addition, the technology also shares the unique advantages of wireless signals (e.g. penetrating the wall and smoke, all day working et al.), which solves the problems of congenital defect of these traditional localization technologies. However, influenced by the time-varying environment, the shadowing effect of the barrier, weak anti-interference of the algorithms et al., the paper will solve these burning questions to realize a highly precise wireless DFL with the ability of strong anti-interference and adaptively to the environment. The following research works are carried out:Firstly, in order to improve the performance of DFL algorithm, the paper proposes a lightweight robust Bayesian graphical approach (BGA), which utilizes not only the observation information of the shadowed links, but also the prior information involved in the previous estimations and the constraint information involved in non-shadowed links to ensure its robust performance. The BGA can be carried out with a series of lightweight graphical multiplication and addition operations, which avoids the complex matrix inversion computation involved in the traditional algorithms.Secondly, to improve the performance of DFL in electromagnetic environment, a cognitive DFL (CDFL) is proposed. Different from traditional DFL, CDFL can evaluate and detect the channel with weak interference in real-time on the online stage. And then we make the CDFL system working on the clear channel, which solves the co-channel and adjacent channel interference problem very well.Finally, the problem of the shadowing effect of the target is researched deeply. With vast experimental demonstration and theoretical analysis, the paper discovers that a precise shadowing effect model is benefit of location precision. So the paper proposes a saddle surface (SaS) model to accurately simulate the effect of the target shadowing the wireless links to improve the location precision. Generally speaking, through the study of the above three contents, the paper explores some methods to improve the performance of DFL to realize cognitive accurate wireless device-free localization, which gathers the experiences for the development of DFL.