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首页> 外文会议>Conference on sensor fusion and aerospace applications >Comparison of polynomial network and model-based target recognition
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Comparison of polynomial network and model-based target recognition

机译：多项式网络与基于模型的目标识别的比较

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摘要

Model-based and data-driven approaches to automatic target recognition each provide a methodology to determine the class of an unknown target. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of unknown targets. A comparator algorithm determines a degree of `match' to each reference target. Data-driven approaches use a numeric algorithm to process a set of characterization features to produce a class likelihood estimate. Each approach has advantages and limitations that should be considered for a specific implementation. This research compares a specific implementation of each of these approaches developed for an automatic target recognition system that processes multi- spectral imagery representing military targets. To provide a valid baseline to compare the performance of each approach, a common target set, characterization feature set, and performance metrics are considered.
机译：基于模型和数据驱动的自动目标识别方法各自提供了一种确定未知目标的类的方法。基于模型的识别是一种目标驱动方法，该方法将未知目标的表示与未知目标的参考库进行比较。比较器算法确定每个参考目标的“匹配”的程度。数据驱动方法使用数字算法来处理一组特征功能以产生类似然估计。每个方法都具有应考虑特定实施的优点和限制。该研究比较了为自动目标识别系统开发的这些方法中的每一种方法的具体实施方式，该方法处理代表军事目标的多频率图像。为了提供比较每个方法的性能的有效基线，考虑公共目标集合，表征特征集和性能度量。

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