The research on indoor localization has received great interest in recent years. This has been fuelled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system which adopts Channel- state Information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for MIMO-OFDM PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of non-equipped individuals.