Marc-Alain Simard

Position and attribute fusion of radar, ESM, IFF and Datalink for AAW missions of the Canadian Patrol Frigate

The R&D group at Lockheed Martin Electronic Systems Canada (LMESC) has now implemented the second version (v2) of its Data Fusion Demonstration Model (DFDM) for a naval anti-air warfare platform. This project has been designed to read data passively on the Canadian Patrol Frigate (CPF) bus without any modification to the CPF software. DFDM v2 has the capability to fuse data from the following CPF sensors: 2 surveillance radar, 2 slaved identification friend or foe, an electronics support measure, the communication intercept operator and a tactical data link (Link-II). The fusion of data from non-organic sensors with the tactical Link-II data has produced spatial alignment problems which have been overcome by the use of a geodetic referencing coordinate system. A new Kalman filter with adaptive process noise provides significantly improved tracking capabilities. Two enhancements have been implemented into a Dempster-Shafer evidential reasoning over attribute data: the addition of pruning rules to reduce the set of identity propositions, and the use of fuzzy logic for confidence level distribution.

Managing evidential reasoning for identity information fusion

A case study in which the Dempster-Shafer evidential reasoning theory is applied to a naval identity and attribute fusion problem is described. Two identity information fusion architectures are discussed: the first concerns the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration, and the second architecture addresses the fusion of incomplete attribute information coming from various sources. The emphasis is put on the second architecture. The use of fuzzy logic for proposition generation is described as well as techniques for managing the number of propositions generated.

Multisource information fusion applied to ship identification for the recognized maritime picture

The Recognized Maritime Picture (RMP) is defined as a composite picture of activity over a maritime area of interest. In simplistic terms, building an RAMP comes down to finding if an object of interest, a ship in our case, is there or not, determining what it is, determining what it is doing and determining if some type of follow-on action is required. The Canadian Department of National Defence currently has access to or may, in the near future, have access to a number of civilians, military and allied information or sensor systems to accomplish these purposes. These systems include automatic self-reporting positional systems, air patrol surveillance systems, high frequency surface radars, electronic intelligence systems, radar space systems and high frequency direction finding sensors. The ability to make full use of these systems is limited by the existing capability to fuse data from all sources in a timely, accurate and complete manner. This paper presents an information fusion systems under development that correlates and fuses these information and sensor data sources. This fusion system, named Adaptive Fuzzy Logic Correlator, correlates the information in batch but fuses and constructs ship tracks sequentially. It applies standard Kalman filter techniques and fuzzy logic correlation techniques. We propose a set of recommendations that should improve the ship identification process. Particularly it is proposed to utilize as many non-redundant sources of information as possible that address specific vessel attributes. Another important recommendation states that the information fusion and data association techniques should be capable of dealing with incomplete and imprecise information. Some fuzzy logic techniques capable of tolerating imprecise and dissimilar data are proposed.

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.

Data fusion of multiple-sensors attribute information for target-identity estimation using a Dempster-Shafer evidential combination algorithm

The research and development group at Loral Canada is in the second phase in the development of a data fusion demonstration model (DFDM) for a naval anti-air warfare platform to be used as a workbench tool to perform exploratory research. The software has been designed to be implemented within the software environment of the Canadian Patrol Frigate (CPF). The second version of DFDM has the capability to fuse data from the following CPF sensors: surveillance radars, electronics support measure, identification friend or foe, communication intercept operator and a tactical data link. During the first phase, the project has demonstrated the feasibility of fusing the sensor attribute information using a modified version of the Dempster-Shafer evidential combination algorithm. A significant enhancement has been the addition of pruning rules to reduce the set of identity propositions which otherwise would be too large to comply with the DFDM real- time requirements. Another improvement has been the use of fuzzy logic to make possible the fusion of apparently incomplete attribute information coming from different sensors. This paper describes the main features of the evidential combination algorithm that we have implemented in the DFDM system. A benchmark scenario has been selected to quantitatively demonstrate the capability of the attribute fusion algorithm.

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.

ID box: a multisource attribute data fusion function for target identification

The R&D group at Lockheed-Martin Canada has developed a target identifier function called ID Box. This computer program performs five main functions: first it transforms the sensor attribute input into a few contact ID declarations, second, it evaluates the association score between the contact declarations and the ID propositions of a current target track, third it performs attribute contact to track fusion using a modification of the Dempster-Shafer evidential theory, fourth the ID Box, using a platform library, produces a translator that unifies the information within track identity and the attribute input, and fifth, it manages the distribution of results to a system human computer interface. Our exhaustive platform library enables the ID Box to fuse attribute data from almost all kinds of sensor or information sources that may be found on large warships or patrol aircraft. These attributes are the radar cross section and the moving parts from surveillance radars, allegiance from interrogator systems, emitter composition from electronics support measure systems, spoken language from communication intercept systems, acoustical signature from sonar systems, propulsion types from IR detectors, dimensional data from imaging systems and other classification attributes from various systems or operators including dynamical parameters from positional trackers. This paper presents and describes the ID Box.

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.

Identity and attribute information fusion using evidential reasoning

The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology that can enhance his/her decision making process regarding the identity of detected objects. Two identity information fusion architectures are discussed here. The first is concerned with the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration. The second is concerned with the fusion of attribute information using a modified version of the Dempster-Shafer evidential combination algorithm.