Elisa Shahbazian

High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges

High-level information fusion (situation and threat assessment, process and user refinement) requires novel solutions for the operational transition of information fusion designs. Low-level (signal processing, object state estimation and characterization) is well-vetted in the community as compared to high-level information fusion (control and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), information management (ontologies, protocols) systems design (scenario-based, user-based, distributed-agent) and evaluation (measures of performance/effectiveness, and empirical case studies).

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary HLIF advances and developments to determine unsolved grand challenges and issues. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and high-level information fusion (control, situational understanding, and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), systems design (scenario-based, user-based, distributed-agent) and evaluation (measures of effectiveness and empirical case studies). The goal is to address the contemporary operational and strategic issues in information fusion system design.

Findings of the NATO workshop on data fusion technologies for harbour protection

The NATO Security Through Science Program and the Defence Investment Division requested and sponsored the organization of a NATO Advanced Research Workshop (ARW) on the topic of Data Fusion Technologies for Harbour Protection, which was held June 27-July 1, 2005 in Tallinn, Estonia. The goal of the workshop was to help knowledge exchange between the technology experts and the security policy makers for a better understanding of goals, functions and information requirements of the decision makers as well as the way the data fusion technology can help enhancing security of harbours. In addition to presentations by experts from the research community on detection and fusion technologies as well as in practice and policy the workshop program included daily breakout sessions, in which the participants were given an opportunity to brainstorm on the topics of the workshop in interdisciplinary smaller teams. The working groups: (i) chose a scenario, including threat stages, threat types, threat methods and ranges, and response constraints due to the particular harbour environment; then (ii) identified: (a) requirements (objectives, functions and essential elements of information); (b) technologies (available and future); (c) information available and necessary through sensors and other sources, as agencies and jurisdiction; (d) methods: detection, identification, situation assessment, prediction. This paper describes the main issues and proposed approaches that were identified by the working groups.

Comparison of various schema of filter adaptivity for the tracking of maneuvering targets

Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.

Fusion of imaging and nonimaging data for surveillance aircraft

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for airborne surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an identification friend or foe (IFF) system, an electronic support measures (ESM) system, a spotlight synthetic aperture radar (SSAR), a forward looking infra-red (FLIR) sensor and a link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (1) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (2) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (3) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as multi-sensor data fusion (MSDF), situation and threat assessment (STA) and resource management (RM).

Comparison of different parallel filtering techniques

Tracking algorithms commonly use practical models of target motion to estimate the targets kinematic quantities such as the position, the velocity and in certain cases, the acceleration. When there is a maneuver, the tracking algorithm should detect the error created by this change and correct the situation to adapt itself to this new change or new tracking model. There are different approaches in the literature for handling maneuver detection using different filtering techniques. A thorough literature survey about different types of filtering techniques used for maneuver detection has been performed. The focus of this study has been the parallel filtering techniques. Some of those techniques given by different authors are summarized in this paper. This paper presents a parallel filter design using three linear Kalman filters with a simple switching algorithm for maneuver detection selected for the Multi Sensor Data Fusion (MSDF) for an anti-air warfare (AAW) surveillance radar. This design is relatively simple compared to other parallel Kalman filter techniques and requires modest computer resources. The parallel filter design has been compared with a single Kalman filter design previously used. The simulation results have shown a great deal of improvement with parallel filtering, particularly in speed estimations and in filtering stability when a target is maneuvering.

Multisensor data fusion implementation within a distributed command and control system

The naval forces will encounter air, surface, underwater, electro-optic/infrared (EO/IR), communications, radar, electronic warfare, etc., threats. Technological advancements of future threats to the navy will place heavy demands (quicker reaction to faster, stealth threats) upon the ability to process and interpret tactical data provided by multiple and often dissimilar sensors. This emphasizes the need for a naval platform employing an automated distributed command and control system (CCS) which includes a multi-sensor data fusion (MSDF) function to increase probability of mission success facing the threats of the future. The main advantage of a distributed CCS is redundancy and reconfigurability resulting in a high degree of survivability and flexibility while accomplishing the mission. The MSDF function provides the combat system with a capability to analyze sensor data from multiple sensors and derive contact/track solutions, which would not be derived by the individual sensors. The command and control (C2) functions, including the MSDF function, operate within a number of general purpose C2 processors, communicating with each other and the sensor systems via a high speed data bus. Different sensors are more effective in different environmental conditions and for different geometrical parameters (elevation, distance, bearing, etc.). The MSDF function combines the capabilities of all the sensors providing the operators and other CCS functions with more accurate solutions faster than each sensor system operating alone. An architecture of a distributed CCS using an MSDF function to increase the probability of mission success of a naval platform is presented.

Data fusion applications for military and civilian purposes developed on DND/LM Canada decision support testbed

Since 1991, the research and development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating technologies which will provide observe-orient-decide-act (OODA) decision making capabilities/tools in naval and airborne command and control (C2) for application on Canadian Patrol Frigates (CPF) and Canadas CP-140 (Aurora) fixed wing aircraft. Over the last three years LM Canada has also established a generic expert system infrastructure and has demonstrated that it is suitable for integrating these decision making technologies into real-time command and control systems (CCSs). This infrastructure has proven to be very useful not only in the defence applications like CPF and Aurora, but also in LM Canada diversification efforts, specifically in intelligent transportation systems (ITS) and remote sensing. The paper describes the infrastructure, the generic testbed for decision support and the data fusion applications that have been implemented and are under development within this testbed for military and civilian purposes.

Multi-agent data fusion workstation (MADFW) architecture

This paper describes an on-going effort to build a Multi- Agent Data Fusion Workstation (MADFW) based on a Knowledge- Based System (KBS) BlackBoard (BB) architecture to offer a range of innovative techniques for Data Fusion (DF), applicable to various domains. The initial application to be demonstrated is in the area of airborne maritime surveillance where several multi-agent concepts and algorithms have already been studied and demonstrated. The end result will offer the user a flexible and modular environment providing capability for: (1) addition of user defined sensor simulation models and fusion algorithms; (2) integration with existing models and algorithms; and (3) evaluation of performance to derive requirement specifications and help in the design phase towards fielding a real DF system. The workstation is being designed to accommodate modular interchangeable algorithm implementation and performance evaluation of: (1) fusion of positional data from imaging and non-imaging sensors; (2) fusion of attribute information obtained from imaging and non-imaging sensors and other sources such as communication systems, satellites, etc.; and (3) Object Recognition in imaging data. The design allows algorithms for sensor simulators and measures of performance to reside ether on the KBS BB shell or be separate from it, thus facilitating integration with other testbed designs. This architecture also allows the future introduction of fusion management capabilities. The real-time KBS BB shell developed by Lockheed Martin Canada, in collaboration with DREV, is the basis of the MADFW infrastructure. This system is totally generic, and could be used to implement any system comprising of components which can be numeric or AI based. It has been implemented in C++ rather than in a higher-level language (such as LISP, Smalltalk, ...) to satisfy the real-time requirement.

Implementation strategies for the central-level multihypothesis tracking fusion with multiple dissimilar sensors

The processor resource requirements for a central-level multi-hypothesis tracking (MHT) fusion system have been estimated to be beyond most of the currently known general purpose processors for naval applications. A benchmark MHT fusion system has been selected for Command and Control System (CCS) for a frigate class naval platform of the year 2000 and beyond. The system parameters have been selected to support the Anti-Air Warfare (AAW) mission requirements of a frigate which has a long range radar (LRR), a medium range radar (MRR), an electronic support measure (ESM) sensor, and an infra-red search and track (IRST) sensor. Appropriate fusion parameters have been selected to support the frigate mission, and the real-time capability to run the algorithms, the time required to perform a cycle of the central-level MHT fusion system has been estimated for a general purpose processor. This paper presents a comparative analysis of the two implementation strategies for the two modes of operation of the central-level benchmark MHT fusion system, by analyzing the system and fusion parameters selected in this study, estimating peak and average processor resource requirements, and evaluating the timing delays between contact detection and fusion for the two approaches. Based on the estimated processor and timing requirements of these approaches, this paper also presents a concurrent computing implementation, that is expected to permit the real-time execution of the central-level MHT fusion system for the AAW frigate within currently available computer technology for naval applications.