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首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2002, Apr 2-4, 2002, Orlando, USA >Feature Aided JBPDAF Group Tracking and Classification Using an IFFN Sensor
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Feature Aided JBPDAF Group Tracking and Classification Using an IFFN Sensor

机译：使用IFFN传感器的功能辅助JBPDAF组跟踪和分类

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摘要

Recent work has been conducted to develop group tracking algorithms that identify and track multiple targets. One of the characteristics of the group tracking algorithms is the ability to correctly identify the target. If enough evidence has been accumulated to identify the target, the algorithms perform well. However, in the case of spurious measurements and obscured targets, the target identity may not be completely realizable. For the case in which the target identity is not discerned, it is important to classify the target based on some methodology to aid the user. Such a classification could be an allegiance so that when the algorithm groups targets, the information is useful to the human. One sensor that is ideal for the scenario is an Identify Friend Foe Neutral (IFFN) sensor which can classify the target allegiance. By incorporating an IFFN sensor in the GRoup IMM-JBPDAF Tracker (GRIT) algorithm, results show that when identity information is not available, target classification is realizable with allegiance features. Results are simulated for a high-range resolution radar (HRR) and an IFFN sensor and a 29% reduction in the computational classification due to the presence of clutter.
机译：已经进行了最近的工作来开发识别和跟踪多个目标的组跟踪算法。组跟踪算法的特征之一是能够正确识别目标。如果已经积累了足够的证据来确定目标，则该算法将表现良好。但是，在虚假测量和目标模糊的情况下，目标身份可能无法完全实现。对于无法识别目标身份的情况，重要的是根据某种方法对目标进行分类以帮助用户。这种分类可能是效忠的，因此当算法将目标分组时，该信息对人类有用。一种最适合该场景的传感器是“识别敌我中立（IFFN）”传感器，该传感器可以对目标忠诚度进行分类。通过将IFFN传感器集成到GRoup IMM-JBPDAF跟踪器（GRIT）算法中，结果表明，当没有身份信息时，可以使用忠诚度功能实现目标分类。仿真结果适用于高分辨雷达（HRR）和IFFN传感器，由于杂波的存在，计算分类减少了29％。

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