Sensor Networks: Data Fusion and Tracking


Data fusion is one the fundamental elements of modern tracking techniques, utilising information from a variety of sources and combining the information in a way that meets the desired application constraints and objectives.

Data fusion is a framework describing the process of combining data originating from different sources. The objective of data fusion is maximization of useful information, such that the fused information provides a more detailed representation with less uncertainty than that obtained from individual sources. While producing more valuable information, the fusion process may also allow for a more efficient representation of the data. Another by-product of information fusion may be the observation of higher-order relationships between respective entities.

The selected method for performing the data combination will depend on the original data format produced by the various sensor types. Data fusion is, in general, conducted using one of the following frameworks:

Pixel level fusion describes the combination of multiple images into a single image, where raw data is robustly and redundantly merged. Each location in the resulting image is an algorithmic combination of the vector of measurements from each of the sensors.

Feature level fusion refers to the extraction of features from each of the sensor data. Registration of detected features is performed for regions of interest or image segments containing more than one pixel. A detection/classification algorithm can then be applied on the combined feature vector.

High-level data fusion or decision fusion occurs where sensor data, with or without pre-processing, is combined with other data or a priori knowledge. Each sensor makes an independent decision based on its own observations and passes these decisions to a central fusion module where a global decision is made. Alternatively, in a decentralized multi-sensor system each node functions performs data fusion based on local observations and the information communicated from neighbouring nodes.

Shown below (Figure 1) are some of the techniques applied to the various elements of the data fusion process.

Significance / Benefits



Practical applications of data fusion have necessarily been those areas in which the required output of an analysis may not be measured directly.


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