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公开(公告)号：US09880257B2
公开(公告)日：2018-01-30
申请号：US14843961
申请日：2015-09-02
申请人： Google Inc.
CPC分类号： G01S5/0278 , G01S5/0036 , G01S5/0252
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes modeling signals of wireless signal emitters. A computing device can determine first and second trained Gaussian processes. The respective first and second Gaussian processes can be based on first and second hyperparameter values related to first and second wireless signal emitters. The computing device can determine first and second sets of comparison hyperparameter values of the respective first and second hyperparameter values, and then determine whether the first and second sets of comparison hyperparameter values are within one or more threshold values. After determining that the first and second sets of comparison hyperparameter values are within the threshold(s), the computing device can determine the first and second Gaussian processes are dependent and then provide an estimated-location output based on a representative Gaussian process based on the first and the second Gaussian processes.
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公开(公告)号：US20160066156A1
公开(公告)日：2016-03-03
申请号：US14621991
申请日：2015-02-13
申请人： Google Inc.
CPC分类号： H04W4/029 , G01S5/0036 , G01S5/0205 , G01S5/0263
摘要： An indication that a wireless computing device (WCD) is moving toward a physical setting may be received. The physical setting may include a particular topography. There may be at least (i) location-determination information of a first type and (ii) location-determination information of a second type. The location-determination information of the first type may facilitate low-resolution location determinations in the physical setting and the location-determination information of the second type may facilitate high-resolution location determinations in the physical setting. Based on the physical setting and the particular topography, location-determination information may be selected from at least (i) the location-determination information of the first type or (ii) the location-determination information of the second type. At least some of the selected location-determination information may be used to estimate a location of the WCD.
摘要翻译： 可以接收到无线计算设备（WCD）朝向物理设置移动的指示。 物理设置可以包括特定的形貌。 可以存在至少（i）第一类型的位置确定信息和（ii）第二类型的位置确定信息。 第一类型的位置确定信息可以促进物理设置中的低分辨率位置确定，并且第二类型的位置确定信息可以促进物理设置中的高分辨率位置确定。 基于物理设置和特定地形，可以从至少（i）第一类型的位置确定信息或（ii）第二类型的位置确定信息中选择位置确定信息。 所选择的位置确定信息中的至少一些可以用于估计WCD的位置。
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3.发明申请Computational Complexity Reduction of Training Wireless Strength-Based Probabilistic Models from Big Data 有权计算复杂度减少训练基于无线强度的概率模型从大数据公开(公告)号：US20160080905A1
公开(公告)日：2016-03-17
申请号：US14843935
申请日：2015-09-02
申请人： Google Inc.
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on signal strength measurements. A computing device can receive a particular signal strength measurement, which can include a wireless-signal-emitter (WSE) identifier and a signal strength value and can be associated with a measurement location. The computing device can determine one or more bins; each bin including statistics for WSEs and associated with a bin location. The statistics can include mean and standard deviation values. The computing device can: determine a particular bin whose bin location is associated with the measurement location for the particular signal strength measurement, determine particular statistics of the particular bin associated with a wireless signal emitter identified by the WSE identifier of the particular signal strength measurement, and update the particular statistics based on the signal strength value. The computing device can provide an estimated location output based on the bins.
摘要翻译： 公开了用于提供输出的装置和方法; 例如，基于信号强度测量的位置估计。 计算设备可以接收特定的信号强度测量，其可以包括无线信号 - 发射器（WSE）标识符和信号强度值，并且可以与测量位置相关联。 计算设备可以确定一个或多个箱; 每个bin包括WSE的统计信息并与bin位置相关联。 统计数据可以包括平均值和标准偏差值。 计算设备可以：确定其仓位置与特定信号强度测量的测量位置相关联的特定仓，确定与由特定信号强度测量的WSE标识符识别的无线信号发射器相关联的特定仓的特定统计， 并根据信号强度值更新特定统计信息。 计算设备可以基于箱提供估计的位置输出。
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4.发明申请Gaussian Process-Based Approach for Identifying Correlation Between Wireless Signals 有权基于高斯过程的识别无线信号之间相关性的方法公开(公告)号：US20160077191A1
公开(公告)日：2016-03-17
申请号：US14843961
申请日：2015-09-02
申请人： Google Inc.
CPC分类号： G01S5/0278 , G01S5/0036 , G01S5/0252
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes modeling signals of wireless signal emitters. A computing device can determine first and second trained Gaussian processes. The respective first and second Gaussian processes can be based on first and second hyperparameter values related to first and second wireless signal emitters. The computing device can determine first and second sets of comparison hyperparameter values of the respective first and second hyperparameter values, and then determine whether the first and second sets of comparison hyperparameter values are within one or more threshold values. After determining that the first and second sets of comparison hyperparameter values are within the threshold(s), the computing device can determine the first and second Gaussian processes are dependent and then provide an estimated-location output based on a representative Gaussian process based on the first and the second Gaussian processes.
摘要翻译： 公开了用于提供输出的装置和方法; 例如，基于经训练的高斯过程建模无线信号发射器的信号的位置估计。 计算设备可以确定第一和第二训练高斯过程。 相应的第一和第二高斯过程可以基于与第一和第二无线信号发射器相关的第一和第二超参数值。 计算设备可以确定相应的第一和第二超参数值的第一和第二组比较超参数值，然后确定第一组和第二组比较超参数值是否在一个或多个阈值内。 在确定第一和第二组比较超参数值在阈值之内时，计算设备可以确定第一和第二高斯过程是相关的，然后基于代表性的高斯过程提供基于 第一和第二高斯过程。
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公开(公告)号：US09810762B2
公开(公告)日：2017-11-07
申请号：US14843941
申请日：2015-09-02
申请人： Google Inc.
发明人： Brian John Julian
IPC分类号： G01S5/02
CPC分类号： G01S5/0278
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on measurement bins (MBs) determined by a computing device. An MB can be associated with a wireless signal emitter (WSE), and can include a mean signal strength value (SSV) and a standard deviation of SSVs for each WSE associated with the MB. The computing device can designate a WSE. The computing device can determine a collection of the MBs associated with the designated WSE. The computing device can train a mean Gaussian process for the designated WSE based on the mean SSV and the standard deviation of SSVs of the collection of MBs. The mean Gaussian process can be associated with a covariance matrix having a diagonal entry based on a standard deviation of SSVs of an MB in the collection of MBs. The computing device can provide an estimated location based on the trained mean Gaussian process.
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6.发明申请Data Driven Evaluation and Rejection of Trained Gaussian Process-Based Wireless Mean and Standard Deviation Models 有权基于训练高斯过程的无线平均和标准偏差模型的数据驱动评估和拒绝公开(公告)号：US20160080908A1
公开(公告)日：2016-03-17
申请号：US14843955
申请日：2015-09-02
申请人： Google Inc.
CPC分类号： H04W4/029 , H04L41/142 , H04L43/08 , H04W4/023
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes. A computing device can determine trained Gaussian processes related to wireless network signal strengths, where a particular trained Gaussian process is associated with one or more hyperparameters. The computing device can designate one or more hyperparameters. The computing device can determine a hyperparameter histogram for values of the designated hyperparameters of the trained Gaussian processes. The computing device can determine a candidate Gaussian process associated with one or more candidate hyperparameter value for the designated hyperparameters. The computing device can determine whether the candidate hyperparameter values are valid based on the hyperparameter histogram. The computing device can, after determining that the candidate hyperparameter values are valid, add the candidate Gaussian process to the trained Gaussian processes. The computing device can provide an estimated location output based on the trained Gaussian processes.
摘要翻译： 公开了用于提供输出的装置和方法; 例如，基于经训练的高斯过程的位置估计。 计算设备可以确定与无线网络信号强度相关的经训练的高斯过程，其中特定训练高斯过程与一个或多个超参数相关联。 计算设备可以指定一个或多个超参数。 计算设备可以确定训练高斯过程的指定超参数的值的超参数直方图。 计算设备可以确定与指定的超参数的一个或多个候选超参数值相关联的候选高斯过程。 计算设备可以基于超参数直方图来确定候选超参数值是否有效。 在确定候选超参数值有效之后，计算设备可以将候选高斯过程加到经过训练的高斯过程中。 计算设备可以基于经过训练的高斯过程来提供估计的位置输出。
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公开(公告)号：US09838847B2
公开(公告)日：2017-12-05
申请号：US14843955
申请日：2015-09-02
申请人： Google Inc.
CPC分类号： H04W4/029 , H04L41/142 , H04L43/08 , H04W4/023
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes. A computing device can determine trained Gaussian processes related to wireless network signal strengths, where a particular trained Gaussian process is associated with one or more hyperparameters. The computing device can designate one or more hyperparameters. The computing device can determine a hyperparameter histogram for values of the designated hyperparameters of the trained Gaussian processes. The computing device can determine a candidate Gaussian process associated with one or more candidate hyperparameter value for the designated hyperparameters. The computing device can determine whether the candidate hyperparameter values are valid based on the hyperparameter histogram. The computing device can, after determining that the candidate hyperparameter values are valid, add the candidate Gaussian process to the trained Gaussian processes. The computing device can provide an estimated location output based on the trained Gaussian processes.
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8.发明授权Computational complexity reduction of training wireless strength-based probabilistic models from big data 有权从大数据中训练基于无线强度的概率模型的计算复杂度降低公开(公告)号：US09591454B2
公开(公告)日：2017-03-07
申请号：US14843935
申请日：2015-09-02
申请人： Google Inc.
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on signal strength measurements. A computing device can receive a particular signal strength measurement, which can include a wireless-signal-emitter (WSE) identifier and a signal strength value and can be associated with a measurement location. The computing device can determine one or more bins; each bin including statistics for WSEs and associated with a bin location. The statistics can include mean and standard deviation values. The computing device can: determine a particular bin whose bin location is associated with the measurement location for the particular signal strength measurement, determine particular statistics of the particular bin associated with a wireless signal emitter identified by the WSE identifier of the particular signal strength measurement, and update the particular statistics based on the signal strength value. The computing device can provide an estimated location output based on the bins.
摘要翻译： 公开了用于提供输出的装置和方法; 例如，基于信号强度测量的位置估计。 计算设备可以接收特定的信号强度测量，其可以包括无线信号 - 发射器（WSE）标识符和信号强度值，并且可以与测量位置相关联。 计算设备可以确定一个或多个箱; 每个bin包括WSE的统计信息并与bin位置相关联。 统计数据可以包括平均值和标准偏差值。 计算设备可以：确定其仓位置与特定信号强度测量的测量位置相关联的特定仓，确定与由特定信号强度测量的WSE标识符识别的无线信号发射器相关联的特定仓的特定统计， 并基于信号强度值来更新特定统计信息。 计算设备可以基于箱提供估计的位置输出。
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9.发明申请公开(公告)号：US20160077190A1
公开(公告)日：2016-03-17
申请号：US14843941
申请日：2015-09-02
申请人： Google Inc.
发明人： Brian John Julian
IPC分类号： G01S5/02
CPC分类号： G01S5/0278
摘要： Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on measurement bins (MBs) determined by a computing device. An MB can be associated with a wireless signal emitter (WSE), and can include a mean signal strength value (SSV) and a standard deviation of SSVs for each WSE associated with the MB. The computing device can designate a WSE. The computing device can determine a collection of the MBs associated with the designated WSE. The computing device can train a mean Gaussian process for the designated WSE based on the mean SSV and the standard deviation of SSVs of the collection of MBs. The mean Gaussian process can be associated with a covariance matrix having a diagonal entry based on a standard deviation of SSVs of an MB in the collection of MBs. The computing device can provide an estimated location based on the trained mean Gaussian process.
摘要翻译： 公开了用于提供输出的装置和方法; 例如，基于由计算设备确定的测量箱（MB）的位置估计。 MB可以与无线信号发射器（WSE）相关联，并且可以包括与MB相关联的每个WSE的平均信号强度值（SSV）和SSV的标准偏差。 计算设备可以指定一个WSE。 计算设备可以确定与指定的WSE相关联的MB的集合。 计算设备可以基于平均SSV和MB集合的SSV的标准偏差来训练用于指定的WSE的均值高斯过程。 平均高斯过程可以与具有基于MB集合中的MB的SSV的标准偏差的对角条目的协方差矩阵相关联。 计算设备可以基于经训练的平均高斯过程来提供估计位置。
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