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List of Special Sessions of MFI 2016 As additional special sessions are announced, the title of each confirmed special session will be added to the topic list above in the paper submission interface. Authors submitting to a special session need to delay their submission until the special sessions are available. SS1 Multi-Sensor Data Fusion for Autonomous Vehicles SS2 Kalman Filters in Nonlinear State Estimation SS3 Data Fusion Methods for Indoor Localization of People and Objects SS4 Multimodal Image Processing and Fusion SS5 Homotopy Methods in State Estimation

SS1 Multi-Sensor Data Fusion for Autonomous Vehicles Description: Organizers: Daniel Clarke, Michael Fiegert, Feihu Zhang, Dhiraj Gulati, Benjamin Noack, Florian Faion,

SS2 Kalman Filters in Nonlinear State Estimation Description: Nonlinear state estimation is an important component in navigation, robotics, object tracking, and many other current research fields. Besides popular but computational expensive particle filters, variants of nonlinear Kalman filters or LMMSE estimators are widely used methods for state estimation. Such filters include for example the Unscented Kalman Filter, the Divided Difference Filter, or iterated Kalman filters. This session aims to cover the recent advances in the area of nonlinear Kalman filters with an emphasis on sampling and sigma-point set design, linearization techniques, and applications of Kalman filters in nonlinear state estimation scenarios. Organizers: Jannik Steinbring, Uwe D. Hanebeck

SS3 Data Fusion Methods for Indoor Localization of People and Objects Description: Indoor positioning has gained great importance as technology allows for affordable realtime sensing and processing systems. Researchers and developers can take advantage of the pervasiveness of WSNs (e.g., in the form of WLAN) and mobile sensors (such as smartphones) to obtain more accurate results by exploiting already existing infrastructure. Applications for indoor positioning include pedestrian navigation in public buildings and shops, location based services, safety for the elderly and impaired, museum guides, surveillance tasks, and also tracking products in manufacturing, warehousing, etc. Unlike outdoor environments, which are covered by GNSS to a satisfying extent, indoor navigation faces additional challenges depending on the underlying measurement system such as occlusions, reflections and attenuation. While there are a great variety of sensors and measuring principles, in practice every single measuring technique suffers from deficits. While RF and (ultra-)sound are subject to multipath propagation, optical systems are intolerant to NLOS conditions. Some systems require setting up beacons, while others are self-calibrating and easy-to-install. Data fusion can overcome these limitations by combining complementary and redundant sensing techniques, with the application of algorithmic methods such as stochastic filtering. Organizers: Antonio Zea, Florian Faion, Uwe D. Hanebeck

SS4 Multimodal Image Processing and Fusion Description: Since the launch of the first version of the Microsoft Kinect in 2010, setting up networks based on multimodal image sensors has become extremely popular. The novelty of these devices includes the availability of not only color information, but also infrared and depth information of a scene, at a price affordable to laymen. The combination of multiple sensors and image modalities has many advantages, such as simultaneous coverage of large environments, increased resolution, redundancy, multimodal scene information, and robustness against occlusion. However, in order to exploit these benefits, multiple challenges also need to be addressed: synchronization, calibration, registration, multi-sensor fusion, large amounts of data, and last but not least, sensor-specific stochastic and set-valued uncertainties. This Special Session addresses fundamental techniques, recent developments and future research directions in the field of multimodal image processing and fusion. Organizers: Antonio Zea, Florian Faion, Uwe D. Hanebeck

SS5 Homotopy Methods in State Estimation Description: In Bayesian state estimation, the inherent uncertainties of the underlying system and measurements are represented as probability density functions. Given new measurements, the predicted system state is updated to incorporate the new information. Traditionally, this information is introduced directly, which can lead to problems in recursive applications. In the case of discrete particle representations of the densities for example, the problem of particle degeneration is well known and has to be corrected for. Another approach is to gradually incorporate the new information through homotopy methods, allowing for a smooth transition of the underlying densities. This special session is concerned with theoretical and practical aspects of homotopy methods in the context of state estimation and all works pertaining to fundamental techniques, recent developments and future research directions in this field are invited. Organizers: Martin Pander, Uwe D. Hanebeck