【信息融合】信息融合的概念和理论

Concepts of Data Fusion

  Data fusion (DF) or multisensor data fusion (MSDF) is the process of combining or integrating measured or preprocessed data or information originating from different active or passive sensors or sources to produce a more specific, comprehensive, and unified dataset or world model about an entity or event of interest that has been observed.

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Sensor-fusion networks

1. Complementary type

  In this type of sensor confi guration, the sensors do not depend on each other directly. One sensor views one part of the region, and another views a different part of the region, thereby giving a complete picture of the entire region.

2. Competitive type

  In this type of configuration, each sensor delivers independent measurements of the same attribute or feature.This configuration would provide robustness and fault-tolerance because comparison with another competitive sensor can be used to detect faults.

3. Cooperative type

  In this type of configuration, data provided by two independent sensors are used to derive information that would not be available from a single sensor, as in a stereoscopic vision system. Cooperative sensor fusion is difficult to design, and the resulting data will be sensitive to the inaccuracies in all the individual sensors.

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Data Fusion Models

1. Joint Directors of Laboratories Model

  The JDL model has five levels of data processing and a database.The JDL model has five levels of data processing and a database.These levels are interconnected by a common bus and need not be processed in a sequential order; nevertheless, they can be executed concurrently.

  The JDL model is an information-centered, abstract model, with specific characteristics as explained below: