Data and information fusion are a multi-disciplinary subject. Data could come from diverse sources where information of the data could be ambiguous, noisy and the quantity could be substantial. Hence, it is not a trivial matter to solve data and information fusion problems.
Listed below are some of the common issues one will face in fusing multiple sources of data:
Data dimensionality and alignment. Different sensors have different measure- ment data and hence different dimensionalities and features. Data alignment and transformation would be needed to achieve common format and standards. Note that the registration process is considered as part of data alignment. For example, different sensor systems may have different:
– Coordinate systems such as Cartesian, spherical and polar coordinates,
latitude-longitude-altitude (or LGZ), North-East-Down (or North-East- Up) and Military Grid Reference System (MGRS) representation.
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– Size of data (object state observable) such as different dimensional space. – Features such as frequency, amplitude, imagery and non imagery. – Data resolution and accuracy.
– Common reference. Sensors in same or different platforms need to be
aligned into a common spatial reference.
Hence techniques are required to perform data transformation and normaliza- tion to a common format and standard that are appropriate for data fusion processes.
Data alignment is required to transform observed data from many different sources to a common format. Data alignment is part of the registration process. Data incest and reliability. Distributed and decentralized data fusion need to handle the problem of data incest and reliability issues. Figure 5.5 illustrates the problem of data incest. Ideally each fused data should contain information on whom is contributing it (this is related to indicating the data dependency and independency factors) and its source’s reliability. However, tagging such information across many nodes is a complicated task and may not be easy to implement in practice.
Time alignments. The synchronization of the data time is important. This is because only data that are ‘close’ in time can be integrated, if meaningful results are desired. This is particularly important for competitive data. The closeness of time or the window period where the data can be fused depends on the types of fusion system. Time-critical fusion systems such as fusion system on-board air platform, the time window is typically in micro or mini second; whereas fusion systems for non-real time analysis that depend on re- port sources that may take minutes or hours to arrive, the time window of closeness could be in the order of minutes or hours.
However, for complementary data, the closeness of time may be relaxed. This is because complementary data may still provide useful information to the fusion system even if the data sources are not sufficiently close in time. For more discussion on competitive and complementary data, please see section 5.6.
Time alignments are complicated by:
– Sensors located at geographically different areas and at different plat-
forms.
– Time difference of arrival due to different signal propagation speeds e.g.
acoustic signal vs electromagnetic signal vs seismic signal.
– Data source update rate.
– Sampling rate. Sampling rate of the sensor measurement data for most
88 Node 2 Node 1 Node 1 Node 2 Node 3 Node 3
No target detected but if Node 1 detects a target, then there probably is a target.
Probably a target
over there. detects a target, then thereprobably is a target. No target detected but if Node 2
Probably a target
over there. Node 3 detects a target Target detected (50% confidence)
must be a target. (90% confidence) as well. Therefore, there
Figure 5.5: Example of data incest. Node 1 communicates its detection to
node 2. Node 2 passes the message to node 3. Node 3 broadcasts the message and node 1 receives. Node 1 assumes that node 3 has the same detection and hence increases its confidence of the detected target.
Time stamping and a common clock for synchronization will have to be es- tablished. Techniques are required to resolve the time delay due to signal propagation and sensor detection. Alignment to ensure common time frame may be needed.
Sensor operation platforms.
– Single or multiple platforms. Sensors in a single or multiple platform
have their own unique time synchronization problems.
– Static or moving platforms. Sensors are in moving or stationary plat-
forms. It is certainly not a trivial problem when the sensors are in differ- ent moving platforms e.g. it may not be feasible to achieve any meaning- ful results having acoustic sensor on a continuous fast moving platform.
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Speed of the moving platform is a factor that needs to be considered. Quantities of sensors used. The increase in the number of sensors available
may result in an exponential increase of:
– Fusion engine design complexity. – Data storage structural complexity. – Communication costs [109].
It is common belief that with more sensors, hence more data, is never theoreti- cally bad for decision making. However, as pointed out by Treece [221], more data or information is damaging to the final answer under certain conditions. Some of these conditions are related to data reliability and the environment the sources are operating on. More studies and investigations are needed to identify the conditions that could be damaging for adding more data.
Sensor selection strategies. Luo and Kay [146] pointed out that the sensor selection is one of the integration functions that can enable a multi-sensor system to select the most appropriate configuration of sensors from among the sensors available. Therefore, proper sensor selection strategies need to be studied. Factors affecting the sensor selection include:
– Target dynamics.
– Target densities (e.g. closely-spaced targets).
– Background noise sources (e.g. high clutter background due to multipath
reflection).
– Sensor performance (e.g. probability of detection).
Concepts of operation. The high-level fusion process would need to take into consideration the concepts of operation, particularly in a military con- text. With increasing complexity of the battlefield, meaningful integration and fusion can only be established if the operational doctrine is well understood and the goals are clear.
Organizational structure and workflow. In order to provide meaningful fu- sion results, some understanding of the organizational structure and workflow would be useful. Different groups of people in the organization look for dif- ferent information, hence fusion system would need to fuse the correct set of data and information that the people are looking for.
Physical and operational constraints. The choice of fusion architecture and techniques are sometime affected by the physical and operational constraints. The solutions to the above issues are non-trivial. The degrees of difficulty also depend on the level of the fusion process and fusion architecture used. To derive
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higher intelligence inference, such as the human brain intelligence, the cognitive intelligence process will need to be studied. The next section will cover a brief de- scription of the cognitive intelligence. And since the fusion architecture will also affect the fusion system design (system engineering design perspective) and the fu- sion algorithms (in term of efficiency and effectiveness), the section after cognitive intelligence will discuss the fusion architecture.