Technology for the United States Navy and Marine Corps, 2000-2035: Becoming a 21st-Century Force Volume 3: Information in Warfare (1997) / Chapter Skim
Currently Skimming:
3 INFORMATION CONTENT
3 INFORMATION CONTENT
Pages 31-49
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From page 31... ...
Automatic target recognition (ATR) requires advanced algorithmic techniques and is an example of a class of applications where current limitations on processing capability represent a significant impediment to implementation.
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From page 32... ...
These systems can be classified into two categories: commercial systems that will be developed in order to sell information for profit, and sensors used in conjunction with information systems for the benefit of the user. The first category includes commercial satellite imagery, databases and mailing lists available for purchase, and commercially operated data mining sources.
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From page 33... ...
Such enhanced capability could be important not only for target recognition using sensors designed to acquire battlefield information, but also for reasoning about disparate information sources on a longer time scale, to provide deep understanding and facilitate planning for potential military operations. Traditionally, sensor information is fed to a processor that performs pattern recognition functions in order to detect targets.
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From page 34... ...
Finally, it is important to be able to perform inferencing, to adapt information to representations that are useful for military needs, and to fuse information from multiple sources. Naval forces have a particular need for automated inferencing for many applications due to the comparatively limited bandwidth that will connect naval platforms to the information sources.
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From page 35... ...
PROCESSING OF INFORMATION The growth of the information infrastructure and the proliferation of new information sources will enable new ways of exploiting information for military purposes. As discussed above, some applications require rapid decisions and are thus tightly coupled to local sensor streams, whereas other applications can make use of massive databases and can perform automated intelligence operations through processing occupying longer periods of time.
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From page 36... ...
Because of the critical importance of information to military systems, however, the Defense Department should strive to remain in the forefront of ongoing developments in this area Yet, before recognition capabilities can be developed that extend target recognition in localized sensor data, generalized capabilities for automatic target recognition need to be successfully demonstrated and refined. The Department of the Navy should develop capabilities in information understanding by identifying database mining methods that are applicable to defense needs, and by funding research in the broad area of recognition theory.
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From page 37... ...
Progress in processors, memory, and sensors, as well as improved algorithmic techniques for processing signal data, image formation, and coherent combination of information (such as from moving targets) , all point to major advances in a very few years.
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From page 38... ...
Synthetic aperture radar (SAR) is currently the principal means for acquiring sensor data for target recognition.
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From page 39... ...
More important to ATR development is the increasing capacity for dense memory storage, and high-bandwidth data transfer within a processor. Since ATR involves comparing relatively small amounts of sensor data with large databases of model data, the ability to index into that model data and to rapidly access the relevant objects largely dominates the processing time.
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From page 40... ...
, whereas rotation invariance has to be built in using multiple templates. Pattern recognition is based on segmenting the signal data, to extract the target region, and then making measurements of that segment.
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From page 41... ...
Apart from providing more compact representations, the grouped information provides opportunities for more accurate reasoning about the components of the image, and the likelihood that the independent groups form an instance of a target. Methods for grouping raw sensor data into clusters of independent meaningful localized features will be dependent on the sensor type, the kinds of target models, and the ingenuity of the researchers.
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From page 42... ...
Instead, it becomes necessary to reason about subparts of a model, each of which might be recognized only indistinctly, but which, in conjunction, might form strong evidence for the presence of a potentially deformed or modified target. In order for ATR systems to operate effectively in the complex combat environments that are likely to characterize future naval engagements, they must be capable of analysis and recognition in the face of uncertainty and partial evidence.
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From page 43... ...
Such highly specialized signal processing methods require nurturing and development, and considerable experimental validation, and tend to require nontraditional thinking about the image formation and signal processing theory. It is expected, however, that imaging of moving targets will become a viable technology.
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From page 44... ...
Generally, a certain number of independent features are required to enable discrimination between targets and background. In one kind of sample analysis applied to a simple image processing example, a 90 percent detection rate with a reasonable false alarm rate (one per kilometer)
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From page 45... ...
Data mining, which has potential DOD applications, currently focuses on the need of credit card companies to automatically recognize spending patterns that indicate probable fraud, based not only on current purchases, but also on the extent to which the current pattern is unusual for the card in question. Other business uses of data mining and collaborative filtering include profiling of potential customers based on their spending patterns, so as to target marketing efforts to the most likely consumers of products and services.
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From page 46... ...
Most ATR development is currently limited to the pattern recognition subset of recognition theory, being based on analysis of single image frames and segmented target regions. However, more generalized ATR processing would take advan
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From page 47... ...
Currently, most recognition systems work in a bottom-up fashion, first extracting features from the given sensor data, and then looking for patterns among the features that support a model hypothesis. Although hypotheses are formed in the course of executing pattern recognition, it is the sensory data that largely dictates the flow of processing, and bottom-up processing is the more appropriate description for the information flow.
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From page 48... ...
ADVANCES NEEDED TO SUPPORT INFORMATION UNDERSTANDING While much of the research that is required for the development of technologies to support information understanding is currently ongoing, in the view of the panel, it is not sufficiently focused on developing information understanding applications for Navy and Marine Corps needs. The panel has identified the following six technology areas as meriting special attention in order to realize the information understanding capabilities that will be required to analyze and exploit the sea of information that will characterize the future information environment: 1.
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From page 49... ...
Technology development is required to develop the applications that will effect the transformation of raw data to higher levels of information understanding, which will include not only the extraction of fixed patterns from sensor data, but also the analysis and reasoning about correlations and co-occurrence of relevant observations. Current applications typically perform pattern recognition on real-time data collected by dedicated sensors; future information understanding systems will need to perform higher-order reasoning about information from the full range of available information sources.
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