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11. 发明申请

US20130041640A1 CLASSIFICATION OF RANGE PROFILES 有权
标题翻译：范围分类
公开(公告)号：US20130041640A1
公开(公告)日：2013-02-14
申请号：US13642386
申请日：2011-04-18
申请人： Robert James Miller
发明人： Robert James Miller
IPC分类号： G06F7/60
CPC分类号： G06K9/00543 , G01S7/41 , G06K9/3241
摘要： A method and apparatus are provided for classifying range profiles, generated for example by a radar, lidar or sonar. In the method, each in a set of objects of interest is modelled with a probabilistic model. The probabilistic model represents the probabilities of occurrence of different possible sequences of distances between selected features of the object, in different orientations, that are likely to result in peaks of backscatter in a range profile of the object. The probabilistic model is derived from a first probabilistic representation of each selected feature, generated to represent the uncertainty in locating the feature and the uncertainty in observing the feature in a range profile. Classification is achieved by calculating, for each probabilistic model, the probability that the model would generate a given sequence of distances between observed backscatter events in a given range profile. The model generating the given sequence with the greatest probability identifies the object likely to have produced the given range profile. Preferably, the probabilistic models comprise Hidden Markov Models (HMMs).
摘要翻译：提供了一种用于对由雷达，激光雷达或声纳产生的范围分布进行分类的方法和装置。在该方法中，利用概率模型对一组感兴趣对象中的每一个进行建模。概率模型表示在不同取向的物体的选定特征之间的不同可能的距离序列的出现概率，其可能导致对象的范围轮廓中的后向散射的峰值。概率模型从每个所选特征的第一概率表示中导出，用于表示定位特征的不确定性以及观察范围分布中的特征的不确定性。通过对于每个概率模型计算模型将在给定范围轮廓中产生观测到的反向散射事件之间的给定给定序列的距离的概率来实现分类。以最大概率生成给定序列的模型识别可能产生给定范围轮廓的对象。优选地，概率模型包括隐马尔可夫模型（HMM）。

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