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    • 4. 发明授权
    • Target detection improvements using temporal integrations and spatial fusion
    • 使用时间整合和空间融合的目标检测改进
    • US07742620B2
    • 2010-06-22
    • US10555104
    • 2004-03-18
    • Hai-Wen ChenTeresa L. OlsonSurachai Sutha
    • Hai-Wen ChenTeresa L. OlsonSurachai Sutha
    • G06K9/00
    • G06K9/3241G06K9/6289G06K9/6293
    • A method for identifying potential targets as far away as possible is disclosed. In a simple background scene such as a blue sky, a target may be recognized from a relatively long distance, but for some high clutter situations such as mountains and cities, the detection range is severely reduced. The background clutter may also be non-stationary further complicating the detection of a target. To solve these problems, target detection (recognition) of the present invention is based upon temporal fusion (integration) of sensor data using pre-detection or post-detection integration techniques, instead of using the prior art technique of fusing data from only a single time frame. Also disclosed are double-thresholding and reversed-thresholding techniques which further enhance target detection and avoid the shortcomings of the traditional constant false alarm rate (CFAR) thresholding technique. The present invention further discloses improved spatial fusion techniques for target detection (recognition) employing multiple sensors instead of employing the more conventional single sensor techniques. If spatial fusion is implemented with more than three sensors, then target detection can be enhanced by also using post-detection techniques. Moreover, since the pre-detection and the post-detection technique are complementary to each other, a combination of these two integration techniques will further improve target detection (recognition) performance.
    • 公开了一种尽可能远地识别潜在目标的方法。 在诸如蓝天的简单的背景场景中,可以从较长的距离识别目标,但是对于诸如山脉和城市的一些高杂波情况,检测范围被严重降低。 背景杂波也可能是非平稳的,进一步使目标的检测复杂化。 为了解决这些问题,本发明的目标检测(识别)基于使用预检测或检测后集成技术的传感器数据的时间融合(集成),而不是使用现有技术来仅将来自单个 大体时间。 还公开了双阈值和反向阈值技术,其进一步增强目标检测并避免传统的恒定误报率(CFAR)阈值技术的缺点。 本发明还公开了使用多个传感器的目标检测(识别)的改进的空间融合技术,而不是采用更传统的单传感器技术。 如果使用三个以上的传感器实现空间融合,则可以通过使用后检测技术来增强目标检测。 此外,由于预检测和后检测技术彼此互补,这两种集成技术的组合将进一步改善目标检测(识别)性能。
    • 8. 发明授权
    • Method and system for data fusion using spatial and temporal diversity between sensors
    • 使用传感器之间的空间和时间分集的数据融合方法和系统
    • US06909997B2
    • 2005-06-21
    • US10395269
    • 2003-03-25
    • Hai-Wen ChenTeresa L. Olson
    • Hai-Wen ChenTeresa L. Olson
    • G01S13/86G06F17/40G06F19/00G06K9/32G06K9/62G06T1/00G06T5/40H04B17/00H04J3/00
    • G06K9/3241G06K9/6288G06K9/6289G06K9/6293G06T5/009G06T5/40G06T2207/10048
    • A method and system provide a multi-sensor data fusion system capable of adaptively weighting the contributions from each one of a plurality of sensors using a plurality of data fusion methods. During a predetermined tracking period, the system receives data from each individual sensor and each data fusion method is performed to determine a plurality of reliability functions for the system based on combining each sensor reliability function which are individually weighted based on the S/N (signal-to-noise) ratio for the received data from each sensor, and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. The system may dynamically select to use one or a predetermined combination of the generated reliability functions as the current (best) reliability function which provides a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.
    • 一种方法和系统提供一种多传感器数据融合系统,其能够使用多种数据融合方法自适应地对来自多个传感器中的每一个的贡献进行加权。 在预定的跟踪期间,系统从每个单独的传感器接收数据,并且执行每个数据融合方法,以便基于基于S / N(信号)单独加权的每个传感器可靠性函数来组合系统的多个可靠性函数 对于每个传感器的接收数据的比较,以及每个传感器和最佳性能(最可靠)传感器的预定传感器操作特性的比较。 该系统可以动态地选择使用所生成的可靠性函数中的一个或预定组合作为当前(最佳)可靠性函数,该功能为与针对和诱饵的正确分类(识别)相关的多传感器系统提供置信度。
    • 10. 发明授权
    • Multi-resolution object classification method employing kinematic features and system therefor
    • 使用运动特征的多分辨率对象分类方法及其系统
    • US06393137B1
    • 2002-05-21
    • US09335427
    • 1999-06-17
    • Hai-Wen ChenHarry A. SchmittJack G. Riddle
    • Hai-Wen ChenHarry A. SchmittJack G. Riddle
    • G06K900
    • G06K9/522
    • A multi-resolution feature extraction method and apparatus. In the illustrative embodiment, the feature extractor includes circuitry for receiving and transforming a time variant data signal into a multi-resolution data signal. The multi-resolution data signal is compared to each of a plurality of object templates. The system then generates a feature vector based on a correlation of the multi-resolution data signal to one of the object templates. The multi-resolution feature extraction method employs object templates formed by transforming time variant image data for each of a plurality of objects into a respective multi-resolution template and averaging all templates for each respective object. The method includes steps for transforming an incoming time variant data signal into a multi-resolution data signal, comparing the multi-resolution data signal to each of the object templates, and generating a feature vector when the multi-resolution data signal correlates to one of the object templates. In a more specific implementation, the method further includes the steps of calculating a confusion matrix (CM), classifying the feature vectors as one of the objects to thereby produce classified objects responsive to the CM, and selecting a target from the classified objects. A multi-resolution feature extractor according to the present invention employs object templates formed by transforming time variant image data for each of a plurality of objects into a respective multi-resolution template and averaging all templates for each respective object to thereby generate object templates.
    • 一种多分辨率特征提取方法和装置。 在说明性实施例中,特征提取器包括用于接收时变数据信号并将其变换为多分辨率数据信号的电路。 将多分辨率数据信号与多个对象模板中的每一个进行比较。 然后,系统基于多分辨率数据信号与对象模板之一的相关性生成特征向量。 多分辨率特征提取方法使用通过将多个对象中的每一个的时间变量图像数据变换为相应的多分辨率模板而形成的对象模板,并对每个相应对象的所有模板进行平均。 该方法包括以下步骤:将输入时变数据信号变换为多分辨率数据信号,将多分辨率数据信号与每个对象模板进行比较,以及当多分辨率数据信号与 对象模板。 在更具体的实现中,该方法还包括以下步骤:计算混淆矩阵(CM),将特征向量分类为对象之一,从而响应于CM产生分类对象,并从分类对象中选择目标。 根据本发明的多分辨率特征提取器采用通过将多个对象中的每一个的时间变量图像数据变换为相应的多分辨率模板而形成的对象模板,并对每个相应对象的所有模板进行平均,从而生成对象模板。