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The classification of ship targets using low resolution down-range radar profiles together with preprocessing and neural networks is investigated. An implementation of the Fourier-modified discrete Mellin transform is used as a means for extracting features which are insensitive to the aspect angle of the radar. Kohonen's self-organizing map with learning vector quantization (LVQ) is used for the classification of these feature vectors. The use of a feedforward network trained with the backpropagation algorithm is also investigated. The classification system is applied to both simulated and real data sets. Classification accuracies of up to 90% are reported for the real data, provided target aspect angle information is available to within an error not exceeding 30 deg  相似文献   
2.
Since many of the MMI systems designed for Tactical Command and Control operating environments are similar in nature, there is a need to standardise certain elements of the systems. This paper gives a summary of the investigation into standardising certain graphical MMI elements, and operator input device elements for Tactical Command and Control systems. This standardisation allows for rapid prototyping on low-cost platforms, as well as enabling the code to be transferred easily between different operating platforms  相似文献   
3.
Since many of the MMI systems designed for tactical command and control operating environments are similar in nature, there is a need to standardise certain elements of the systems. This paper gives a summary of the investigation into standardising certain graphical MMI elements, and operator input device elements for tactical command and control systems. This standardisation allows for rapid prototyping on low-cost platforms, as well as enabling the code to be transferred easily between different operating platforms  相似文献   
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