公开(公告)号：US20140309967A1

公开(公告)日：2014-10-16

申请号：US13862311

申请日：2013-04-12

申请人： Thomas Eugene Old ,  Neal Jay Carron ,  Scott Mark Frasier ,  John Christopher Baker

发明人： Thomas Eugene Old ,  Neal Jay Carron ,  Scott Mark Frasier ,  John Christopher Baker

IPC分类号： G06F17/18

CPC分类号： G06K9/6244

摘要： The present invention relates to the method to identify the source of a signature signal by processing sparse digital data collected by a sensor system in a laboratory, field, or other application. The invention specifically addresses weak, obscured, or partially sampled signatures collected by a sensor system. The method takes advantage of all sources of data using an innovative method that uses Bayes Theorem for performing probability arithmetic and statistical inference. The method requires an exclusive and exhaustive library of candidate signatures. The method finds the most likely signature candidate from the library that has the highest likelihood of being responsible for the measured signal. In addition, the method can work with mixtures of library candidates to find the most likely mixture that explain the data. The method is applicable to a variety of sensor systems that collect and digitize data as signal strength (ordinate) versus measurement attribute (abscissa).

摘要翻译： 本发明涉及通过处理由实验室，场或其他应用中的传感器系统收集的稀疏数字数据来识别签名信号的源的方法。 本发明专门涉及由传感器系统收集的弱，模糊或部分采样的签名。 该方法利用所有数据来源，利用贝叶斯定理进行概率算法和统计推理的创新方法。 该方法需要排他性和详尽的候选签名库。 该方法从图书馆中找到最有可能对测量信号负责的最可能的签名候选者。 此外，该方法可以与库候选者的混合使用，以找到解释数据的最可能的混合物。 该方法适用于以信号强度（纵坐标）与测量属性（横坐标）收集和数字化数据的各种传感器系统。