We evaluate our system in a complex environment along three different moving paths. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Zan Li, Danilo Burbano Acu na, Luis Carrera, Torsten Braun INF-15-004: Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. Passive positioning systems produce user location information for third-party providers of positioning services. In this hands-on way of looking at the hyperimage, the students investigated how the existence and characteristics of an actual person become irrelevant (or can even disappear) under the gigantic visual assemblage that represents her.INF-15-005: Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging Under the guidance of Offshore Studio (Isabel Seiffert and Christoph Miler) the students deconstructed one of the iconic – and essentially (hyper-)visual – media personas of our time: the media celebrity Kim Kardashian. One of these latter digital polyptychs was also chosen as subject for a case study. Just like in an oversaturated heraclitian reality of ever-transforming visual content. Some are more static and linear, but some are in a permanent dynamic of ungraspable change. We observed how some of such image groupings exist in the same digital plane (like the carefully curated Instagram feeds), but some have their elements distributed across planes and dimensions, like the visual identities and personas emanating from intricate hyperlinking across platforms and channels. Throughout the discussions a special focus crystallized on the phenomenon of the image assemblages within the cyberspace. Through discussions, readings, museum visits and visual analysis around the idea of the hyperimages, we aimed essentially at touching some of the following questions: can we see something anew about the relationship between us and the visual, between the visual and reality, between reality and us? During the “Visual Culture” seminar the MA Visual Communication students explored the concept of hyper in relation to current visual phenomena: the excessive, heightened, multidimensional nature of how images are often produced, consumed or displayed today.
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