Multisensor fusion with the ramification algorithm

机译：多传感器融合与分枝算法

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摘要

The present approach combines data fusion from several sensor types to enhance the overall detection and classification performance. The fusion of different sensors is implemented at data and feature levels that results in enhanced target identification by the means of spatial spectral analysis.

机译：本方法结合了来自几种传感器类型的数据融合，以增强整体检测和分类性能。在数据和特征级别实现不同传感器的融合，从而通过空间光谱分析增强目标识别。

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来源
《Automatic Target Recognition XXV》|2015年|94760X.1-94760X.12|共12页
会议地点 Baltimore MD(US)
作者
Andre Sokolnikov;
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作者单位

'Visual Solutions Applications', South Bend, IN;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Automatic Target Recognition; adaptive modeling; prediction modeling; tree-based algorithm; multisensor fusion; ramification algorithm;

机译：自动目标识别；自适应建模；预测建模；基于树的算法；多传感器融合分枝算法;

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1. NEW TRACK-TO-TRACK CORRELATION ALGORITHMS BASED ON BITHRESHOLD IN A DISTRIBUTED MULTISENSOR INFORMATION FUSION SYSTEM [J] . Liu Yu, Wang Haipeng, He You, Journal of computer sciences . 2013,第12期

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2. NEW TRACK-TO-TRACK CORRELATION ALGORITHMS BASED ON BITHRESHOLD IN A DISTRIBUTED MULTISENSOR INFORMATION FUSION SYSTEM | Science Publications [J] . Dong Kai, He You, Liu Yu, Journal of computer sciences . 2013,第12期

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3. Computationally Efficient Multisensor Fusion Estimation Algorithms [J] . Seokhyoung Lee and Vladimir Shin Journal of Dynamic Systems, Measurement, and Control . 2010,第2期

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4. Multisensor fusion with the ramification algorithm [C] . Andre Sokolnikov Automatic Target Recognition . 2015

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5. Multisensor fusion algorithms for object detection, using subsurface data. [D] . Tolstoy, Leonid. 2003

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Multisensor fusion with the ramification algorithm

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