Click here to download the call for special sessions.

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 Intelligent Information Fusion
• SS2 Dynamic Data Driven Application Systems for Sensor Data Problems
• SS3 Context-Based Information Fusion
• SS4 Homotopy Methods for Progressive Bayesian Estimation
• SS5 Data Fusion Methods for Indoor Localization of People and Objects
• SS6 Directional Estimation
• SS7 Space Object Detection, Tracking, Identification, and Classification
• SS8 Recent Advances in Estimation Performance Bounds and Applications
• SS9 Sequential Monte Carlo Methods for Complex Systems
• SS10 Multi-Level Fusion: Bridging the Gap between High and Low Level Fusion
• SS11 Kalman Filter Based Nonlinear Estimation
• SS12 Advances in Distributed Kalman Filtering
• SS13 Applications of Data Analytics and Information Fusion to Finance, Business, and Marketing
• SS14 Sensor, Resources, and Process Management for Information Fusion Systems
• SS15 Multistatic Tracking
• SS16 Multimodal Image Processing and Fusion
• SS17 Maritime Domain Awareness
• SS18 Positioning in Wide Area Networks
• SS19 Evaluation of Technologies for Uncertainty Reasoning
• SS20 Extended Object and Group Tracking
• SS21 Information Fusion in Multi-Biometric Systems
• SS22 Situational Understanding Through Equivocal Sources

Description: Research on Intelligent Systems for information fusion has matured during the last years and many effective applications of this technology are now deployed. The problem of Information Fusion has attracted significant attention in the artificial intelligence and machine learning community, trying to innovate in the techniques used for combining the data and to provide new models for estimations and predictions. The growing advances of Information Fusion accompanied with the advances of sensor technologies and distributed computing systems has led to new applications in different environments such as remote sensing, distributed surveillance, smart home care, network management etc. With the continuing expansion of the domain of interest and the increasing complexity of the collected information, intelligent techniques for fusion processing have become a crucial component in information fusion applications. In this sense, Intelligent systems can improve high level information fusion aimed at supporting decision making and/or intelligent information management.

Organizers: Juan Manuel Corchado, Javier Bajo, and Tiancheng Li

Description: The Dynamic Data-Driven Application Systems (DDDAS) paradigm shapes a symbiotic feedback ecosystem consisting of models of physical and engineered systems and application instrumentation. Precisely, DDDAS establishes new avenues for accurate analysis and robust prediction, and control in application systems using multi-modal fusion of sensory data. The ubiquitous Big Data problems place the DDDAS as a unifying framework among applications, mathematical and statistical modeling, as well as information systems. Such challenges make the DDDAS paradigm now more relevant than ever that integrate modeling, measurements, and software. The DDDAS Session invites papers that demonstrate advances in the DDDAS paradigm that combine real-world applications, contemporary mathematical approaches, real-time large scale measurements, with software solutions. Key applications requiring DDDAS high-end computing solutions include distributed wireless platforms, distributed processing, collection and processing of sensor data for situation awareness, and critical infrastructure systems.

Organizers: Erik Blasch, Frederica Darema, Vasileios Maroulas, and Ioannis D. Schizas

Description: The goal of the proposed session is discussing approaches to context-based information fusion. It will cover the design and development of information fusion solutions integrating sensory data with contextual knowledge. The context may be spread at different levels, with static or dynamic structure, and be represented in different ways, as maps, knowledge-bases, ontologies, etc. It can constitute a powerful tool to favour adaptability and systém performance. Therefore, the session covers both representation and exploitation mechanisms so that this knowledge can be efficiently integrated in the fusion process and enable adaptation mechanisms under different possible paradigms (intelligent systems, knowledge management, integration in fusion algorithms, etc). The applicability of advanced approaches can be illustrated with real-world applications of information fusion requiring a ontextualized approach.

Organizers: Jesus Garcia, Lauro Snidaro, José M. Molina, and Ingrid Visentini

Description: This session is concerned with homotopy methods for the efficient solution of Bayesian state estimation problems occurring in information fusion and filtering. For state estimation in the presence of stochastic uncertainties, the best current estimate is represented by a probability density function. For that purpose, different representations are used including continuous densities such as Gaussian mixtures or discrete densities on continuous domain such as particle sets. Given prior knowledge in form of such a density, the goal is to include new information by means of Bayes' theorem. Typically, the resulting posterior density is of higher complexity and difficult to compute. In the case of particle sets, additional problems such as particle degeneracy occur. Hence, an appropriate approximate posterior has to be found. For recursive applications, this approximate posterior should be of the same form as the given prior density (approximate closedness). To cope with this challenging approximation problem, a well-established technique is to gradually include the new information instead of using it in one shot, which is achieved by a homotopy. For this session, manuscripts are invited that cover any aspect of homotopy methods for state estimation. This includes both theoretically oriented work and applications of known methods.

Organizers: Uwe D. Hanebeck, and Fred Daum

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 satisfiable 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. This Special Session addresses fundamental techniques, recent developments, and future research directions to help clear the way toward robust, accurate, indoor localization.

Organizers: Antonio Zea, Florian Faion, and Uwe D. Hanebeck

Description: Many estimation problems of practical relevance include the problem of estimating directional quantities, for example angular values or orientations. However, conventional filters like the Kalman filter assume Gaussian distributions defined on Rn. This assumption neglects the inherent periodicity present in directional quantities. Consequently, more sophisticated approaches are required to accurately describe the circular setting. This Special Session addresses fundamental techniques, recent developments and future research directions in the field of estimation involving directional and periodic data. It is our goal to bridge the gap between theoreticians and practitioners. Thus, we welcome both applied and theoretic contributions on this topic.

Organizers: Gerhard Kurz, Igor Gilitschenski, and Uwe D. Hanebeck

Description: The operation of Earth-orbiting spacecraft has become increasingly difficult due to the proliferation of orbit debris and increased commercialization. This has been made evident by several collisions involving operational spacecraft in recent years. Maintaining sustainability of key orbit regimes, e.g., low-Earth, sun-synchronous, and geosynchronous orbits, requires improved tracking and prediction of up to hundreds of thousands of objects given sparse measurements in both space and time. Target identification and classification allows for better prediction and situational aware ness. Moreover, proper characterization of measurement assignments as well as the determination of measurement associations for maneuvering targets play a pivotal role in successful space situational awareness. Solutions to the problem will be interdisciplinary and require expertise in astrodynamics, computational sciences, information fusion, applied mathematics, and many other fields.

The primary goal of this session is to promote interaction between the astrodynamics and space situational awareness community with those conducting research in information fusion and multi-target tracking. The secondary goal is a gathering of the individuals performing research on the associated topics to present, discuss, and disseminate ideas related to solving the detection, tracking, identification, and classification problems in the context of space situational awareness.

Organizers: Kyle DeMars, and Brandon Jones

Back to top menu

Description: The field of estimation performance bounds has a long history. The perhaps most prominent example is the Cramer-Rao Lower bound (CRLB) which nowadays finds widespread use. Even though CRLB itself is established, there are many emerging areas, where it has not been evaluated. Besides the CRLB, there are other bounds that are often tighter, i.e. they better predict the estimation performance, such as the Barankin bound or Weiss-Weinstein bound, which are often more difficult to compute, but have recently attracted considerable interest in the research community.

This special session aims at bringing together different experts in the field of estimation performance bounds to discuss the newest research results in this area. Of particular interest are developments of novel bounds, such as e.g. Bayesian bounds, non-Bayesian bounds, hybrid bounds, misspecified bounds, as well as new results for the CRLB with application to for instance target tracking, sensor networks, aerospace, or localization.

Organizers: Carsten Fritsche, and Fredrik Gustafsson

Description: The aim of this special session is to address challenging problems such as estimation for high-dimensional systems and systems with complex dynamics (inter-relationships) with Sequential Monte Carlo (SMC) methods. This session will get together experts from different areas and is aimed at presenting novel techniques, algorithms, approaches especially based on sequential Monte Carlo methods. Both theoretically oriented and application related works are welcomed.

Organizers: Lyudmila Mihaylova, Hans Driessen, Martin Ulmke, Fredrik Gustafsson, Fredrik Gunnarsson, and Carsten Fritsche

Description: The exploitation of all relevant information originating from a growing mass of heterogeneous sources, both device-based (sensors, video, etc.) and human-generated (text, voice, etc.), is a key factor for the production of timely, comprehensive and most accurate description of a situation or phenomenon. There is a growing need to effectively identify relevant information from the mass available, and exploit it through automatic fusion for timely, comprehensive and accurate situation awareness. Even if exploiting multiple sources, most fusion systems are developed for combing just one type of data (e.g. positional data) in order to achieve a certain goal (e.g. accurate target tracking) without considering other relevant information that could be of different origin, type, and with possibly very different representation (e.g. a priori knowledge, contextual knowledge, mission orders, risk maps, availability and coverage of sensing resources, etc.) but still very significant to augment the knowledge about observed entities. Very likely, this latter type of information could be considered of different fusion levels that rarely end up being systematically exploited automatically. The result is often stove-piped systems dedicated to a single fusion task with limited robustness. This is caused by the lack of an integrative approach for processing sensor data (low-level fusion) and semantically rich information (high-level fusion) in a holistic manner thus effectively implementing a multi-level processing architecture and fusion process. The proposed special session will bring together researchers working on fusion techniques and algorithms often considered to be at different and disjoint, fostering thus the discussion on the commonalities and differences in their research methodologies, and proposing viable multi-level fusion solutions to address challenging problems or relevant applications.

Organizers: Lauro Snidaro, Jesus Garcia Herrero, and Wolfgang Koch

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, divided difference filter, cubature Kalman filter, or iterated Kalman filters. The proposed 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 performance evaluation and applications of nonlinear Kalman filter based estimators.

Organizers: Jannik Steinbring, Jindřich Duník, Uwe D. Hanebeck, and Ondřej Straka

Description: The rapid advances in sensor and communication technologies are accompanied by an increasing demand for distributed state estimation methods. Centralized implementations of Kalman filter algorithms are often too costly in terms of communication bandwidth or simply inapplicable - for instance when mobile ad-hoc networks of autonomously operating state estimation systems are considered. Compared to centralized approaches, distributed or decentralized Kalman filtering is considerably more elaborate. In particular, the treatment of dependent information shared by different state estimation systems is a central issue. This special session provides a platform to discuss recent developments and to share ideas on distributed Kalman filtering and related topics.

Organizers: Benjamin Noack, Felix Govaers, Uwe D. Hanebeck, and Wolfgang Koch

Description: This proposal is to propose a special session of applying data fusion and predictive analytics to finance, business, and marketing within the Fusion 2016 conference. Finance and business are critical application areas in information fusion and data analytics. Many of the techniques discussed in the information fusion community are directly applicable to this emerging and important application area. The goal of this proposed session is to open up a forum for data scientists and engineer to share their latest experience and insight on applying the predictive modeling and data analytics techniques to the applications in finance and business areas.

Organizers: KC Chang, and Zhi Tian

Description: A continuing increase of performance requirements sets up the need for optimal gain and exploitation of information. This gives rise to a broad field of optimization problems in the context of uncertainty. Advancements in communication, information and sensor technologies are driving a trend in the development of complex, adaptive and reconfigurable sensor systems. Such a sensor system can have a large scope for online reconfiguration, which typically exceeds the management capability of a human operator. In addition, the sensor system can face a variety of fundamental resource limitations, such as a limited power supply, a finite total time budget, a narrow field of sight, a limited on-board processing capability, or constraints on the communication channels between the sensor nodes. Consequently, effective sensor scheduling and resources management is a key factor for the performance of the emerging generation of adaptive and reconfigurable sensor systems.

In the case of stationary sensors, it is usually desirable to schedule measurements to maximize the benefit in respect to the objectives of the sensor system, whilst avoiding redundant measurements. This benefit can be quantified by an appropriate metric, for example, a task specific metric, information gain or utility. For mobile sensors it is also necessary to consider the sensor platform navigation (including its uncertainties), as the sensor-scenario geometry can significantly affect performance of the current and future information acquisition, e.g., for coordinated exploration in disaster areas. Additionally, the coupling of navigation and sensing allows for the exploitation of the dual effect and an improved control performance. Considering the uncertainty of the controlled variable, which can be done explicitly or implicitly, allows for the exploitation of this dual effect and an improved control performance.

Organizers: Christof Chlebek, Fotios Katsilieris, Maxim Dolgov, and Uwe D. Hanebeck

Description: This special session focuses on multistatic sonar and radar information fusion and target tracking algorithms. Recent years have seen increasing interest in fusion and tracking algorithms for multistatic systems. Challenges include the effective treatment of bistatic sensor nodes, non-linear measurements, and false alarm overloading. Recent progress in multistatic tracking has been facilitated by the Multistatic Tracking Working Group (MSTWG), an International Society of Information Fusion (ISIF) working group. The purpose of this working group is to evaluate a large variety of multistatic tracking algorithms available amongst group members on common data sets with common metrics. The reporting of these results and other related multistatic topics has been of great value to MSTWG and ISIF in the form of numerous papers and participation during similar special sessions in previous FUSION conferences since 2006. A special session on multistatic sonar/radar tracking at FUSION’16 will enable current MSTWG outputs and other contributions by others outside of this group to be documented.

Organizers: Garfield R. Mellema, and David W. Krout

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: Florian Faion, Antonio Zea, and Uwe D. Hanebeck

Description: According to the International Maritime Organization, Maritime Domain Awareness (MDA) is seen as the effective understanding of any activity associated with the maritime environment that could impact upon the security, safety, economy or environment. A particular interest in the field of MDA lies in the collection of essential information about individuals, groups of people or organizations acting in the maritime domain. This information is used to monitor activities in such a way that trends can be identified and anomalies differentiated. The goal is the protection of the territorial waters against threats such as military interventions or terrorist attacks and the sustainment of the global trade which depends on the safety of the oceans. Achieving this goal involves a number of different facilities, equipment and technologies. Most important are the sensor carriers which are necessary for information gathering. Among them are such diverse types as submarines, surface vessels, unmanned underwater or surface vehicles, autonomous underwater or surface vehicles, airplanes and even satellites, sharing information through a communication network. The sensors in use comprise sonar sensors, electro-optical and infrared cameras, ESM sensors, radars of various kinds, AIS transponders and receivers and many more. Data produced by these sensors alone are insufficient. In order to allow the operational decision makers to anticipate threats and take the initiative to defeat them, data must be collected and analyzed by the aid of computer analysis algorithms. A major part of these algorithms include modern sensor data and information fusion methods.

This Special Session addresses fundamental techniques, recent developments and future research directions in the field of MDA sensor data fusion. It brings together academic, industry and government experts working on topics related to the field of MDA.

Organizers: Thomas Kausch, and Kevin Brinkmann

Description: Positioning of devices in wireless networks is becoming ubiquitous and has many applications, such as surveillance, Internet of Things, health care, intelligent transportation systems, logistics, etc. The devices can be mobile, stationary or nomadic. Estimating the position of devices is subject to several challenges including deployment aspects, proper reception of sufficiently many radio signals, measurement errors modelling, motion models aspects, energy and resource efficiency aspects etc. Furthermore, analysis of large number of positioned devices and associated movements is highly relevant in some use cases.

This special session calls for both theoretical and practical oriented works in different domains of positioning for devices in indoor and/or outdoor environments.

Organizers: Fredrik Gunnarsson, Carsten Fritsche, Fredrik Gustafsson, Lyudmila Mihaylova, Martin Ulmke, Feng Yin, and Hans Driessen

Description: The session will focus three topics: (1) to summarize the state of the art in uncertainty analysis, representation, and evaluation, (2) discussion of metrics for uncertainty representation, and (3) survey uncertainty at all levels of fusion. The impact to the ISIF community would be an organized session with a series of methods in uncertainty representation as coordinated with evaluation. The techniques discussed and questions/answers would be important for the researchers in the ISIF community; however, the bigger impact would be for the customers of information fusion systems to determine how measure, evaluate, and approve systems that assess the situation beyond Level 1 fusion. The customers of information fusion products would have some guidelines to draft requirements documentation, the gain of fusion systems over current techniques, as well as issues that important in information fusion systems designs. One of the main goals of information fusion is uncertainty reduction, which is dependent on the representation chosen. Uncertainty representation differs across the various levels of Information Fusion (as defined by the JDL/DFIG models). Given the advances in information fusion systems, there is a need to determine how to represent and evaluate situational (Level 2 Fusion), impact (Level 3 Fusion) and process refinement (Level 5 Fusion), which is not well standardized for the information fusion community.

Organizers: Paulo Costa, Kathryn Laskey, Anne-Laure Jousselme, Erik Blasch, Jürgen Ziegler, Valentina Dragos, Pieter DeVilliers, and Gregor Pavlin D

Description: Typical object tracking algorithms assume that the object can be modeled as a single point without an extent. However, there are many scenarios in which this assumption is not reasonable. For example, when the resolution of the sensor device is higher than the spatial extent of the object, a varying number of measurements from spatially distributed reflection centers are received. Furthermore, a collectively moving group of point objects can be seen as a single extended object because of the interdependency of the group members.

This Special Session addresses fundamental techniques, recent developments and future research directions in the field of extended object and group tracking.

Organizers: Marcus Baum, Uwe D. Hanebeck, Peter Willett, and Wolfgang Koch

Description: This session will focus on the latest innovations and best practices in the emerging field of multi-biometric fusion. Biometrics tries to build an identity recognition decision based on the physical or behavioral characteristics of individuals. Multi-biometrics aims at outperforming the conventional biometric solutions by increasing accuracy, and robustness to intra-person variations and to noisy data. It also reduces the effect of the non-universality of biometric modalities and the vulnerability to spoof attacks. Fusion is performed to build a unified biometric decision based on the information collected from different biometric sources. This unified result must be constructed in a way that guarantees the best performance possible and take into account the efficiency of the solution.

The topic of this special session, Information Fusion in Multi-Biometrics, requires the development of innovative and diverse solutions. Those solutions must take into account the nature of biometric information sources as well as the level of fusion suitable for the application in hand. The fused information may include more general and non-biometric information such as the estimated age of the individual or the environment of the background.

This special session will be supported by the European Association for Biometrics (EAB). The EAB will provide technical support by addressing experts for reviews and will help with the dissemination and exploitation of the event.

Organizers: Naser Damer, and Raghavendra Ramachandra

Description: In contrast to traditional sensing sources, the proliferation of soft information sources—especially multimodal social media—has made them a viable medium to obtain insights about events and their evolutions in the environment. Fusing information from such information sources could improve the situational understanding of decision makers, thus enabling them to make informed decision in rapidly changing complex environments. However, the equivocal nature of such sources makes the decision-making challenging, especially in critical situations where information reliability plays a key role.

Thus, the aim of the special session is as follows: (a) discuss how different strains of information can be processed, analysed, and combined to model the equivocality in information; (b) investigate how such models can be exploited to improve the credibility and reliability of the fused information; and (c) frameworks to combine such information to assists decision makers—be they central or edge users.

Organizers: Geeth de Mel, Murat Sensoy, Lance Kaplan, and Tien Pham