One of the results of the JDL/DFS efforts was the development of a data fusion process model. It provides a high-level functional view of the data fusion process. The JDL Functional Data Fusion Process Model, depicted in Figure 3.1, consists of eight components [WL90, Hal92, Ant95, HL97]:
• Sources provide input to the data fusion system. Possible sources are local sensors, distributed sensors, human input and a priori informa- tion from databases. Multiple sensors that are from the same type are called commensurate sensors, as opposed to noncommensurate sen- sors that are of different type.
• Source Pre-Processing is sometimes referred to as ‘Level 0 Processing’ or ‘Process Assignment’. It covers initial signal processing and allo- cates data to appropriate processes. It enables the data fusion process to focus on data that applies most to the current situation and reduces the data fusion system load.
• Level 1 Processing - Object Refinement fuses sensor information to achieve a refined representation of an individual entity. It usually consists of four functions:
3.2. THE JDL FUNCTIONAL DATA FUSION PROCESS MODEL 21
Figure 3.1: JDL Functional Data Fusion Process Model
– Data Alignment aligns data received from multiple sensors to a
common reference frame;
– Association combines, sorts or correlates observations from mul-
tiple sensors that relate to a single entity;
– Tracking involves the combination of multiple observations of po-
sitional data to estimate the position and velocity of an entity;
– Identification combines data related to identity to refine the esti-
mation of an entity’s identity or classification.
Level 1 fusion benefits from the use of heterogeneous sensors, the em- ployment of spatially distributed sensors and the application of non- sensor derived information.
• Level 2 Processing - Situation Refinement develops a contextual de- scription of relations between entities. It focusses on relational in- formation to determine the meaning of a group of entities. It consists of object aggregation, event and activity interpretation and eventually contextual interpretation. Its results are indicative of hostile behavior patterns. It effectively extends and enhances the completeness, con- sistency, and level of abstraction of the situation description produced by Object Refinement.
• Level 3 Processing - Threat Refinement analyzes the current situation and projects it into the future to draw inferences about possible out- comes. It identifies potential enemy intent and friendly force vulnera- bilities. Threat refinement focusses on intent, lethality, and opportu- nity.
optimize the overall performance of the fusion system. It consists of four key functions:
– Performance evaluation provides information about real-time con-
trol and long-term performance;
– Process control identifies the information needed to improve the
multilevel fusion product;
– Source requirements determination determines the source spe-
cific requirements to collect relevant information;
– Mission management allocates and directs sources to achieve mis-
sion goals.
Part of the process refinement, in particular mission management, may be outside the domain of specific data fusion functions. It is there- fore partially placed outside the fusion process in Figure 3.1.
• The Database Management System provides access to, and manage- ment of data fusion databases. It is the most extensive support func- tion for data fusion processing. Its functions include data retrieval, storage, archiving, compression, relational queries, and data protec- tion.
• The Human-Computer Interface allows human input into the data fu- sion process. It is also a means of communicating data fusion results to a human operator.
Antony [Ant95] presents three interpretations of levels 1, 2, and 3 in the JDL model. These interpretations are depicted in Figure 3.2. The first in- terpretation associates the three levels with input information classes. The second interpretation associates them with the answers to the questions where, when, what, why, how and so what. The third one associates the levels with the information product classes. These interpretations resem- ble the multiple levels of abstraction of the input, the fusion process, and its products.
Antony compares the multiple level-of-abstraction situation description that results from data fusion with a jigsaw puzzle [Ant95]. Individual puz- zle pieces represent low level-of-abstraction information, such as color or texture. When several pieces are combined they might represent an object, such as a tree. The whole puzzle contains several aggregate objects, for in- stance a forest. When one starts with solving the puzzle, there is very little context, since there are no pieces in place yet. This means that a single piece can be interpreted in multiple ways. A blue piece, for example, could represent sky as well as water. Hence placing the pieces is quite difficult. At this stage, solving the puzzle is analogous to level 1 fusion. With the placement of more and more pieces, objects (‘level 1’) and groups of objects (‘level 2’) emerge. This makes placement of new pieces considerably easier.
3.2. THE JDL FUNCTIONAL DATA FUSION PROCESS MODEL 23
One might even start to look for specific missing pieces (‘level 4’)1. Eventu-
ally, although some pieces might be missing, the whole scene (situation) is adequately described by the pieces that are in place.