WO2017049914A1 - 一种终端定位方法、装置及*** - Google Patents

一种终端定位方法、装置及*** Download PDF

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WO2017049914A1
WO2017049914A1 PCT/CN2016/081894 CN2016081894W WO2017049914A1 WO 2017049914 A1 WO2017049914 A1 WO 2017049914A1 CN 2016081894 W CN2016081894 W CN 2016081894W WO 2017049914 A1 WO2017049914 A1 WO 2017049914A1
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terminal
channel response
vector
location
feature vector
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PCT/CN2016/081894
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French (fr)
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黄河
张晓雷
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models

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  • This document relates to, but is not limited to, the field of positioning, and in particular, to a terminal positioning method, device and system.
  • the terminal positioning method based on the RSSI generally uses the channel energy between the AP (Access Point) and the terminal and the distance between the AP and the terminal to satisfy a certain statistical significance. Functional relationships, which in turn enable position estimation. Since the function relationship is unknown, this method needs to generate the terminal location and the receiving energy database of multiple APs in advance, and then learn or acquire the position-energy function relationship to realize wireless positioning. This method has a large database generating workload and a process. Computational complexity and other shortcomings.
  • the embodiment of the invention provides a terminal positioning method, device and system, which can be easily realized.
  • the embodiment of the invention provides a terminal positioning method, including:
  • Generating a kernel mapping matrix according to the terminal location-channel response database acquiring a main eigenvector according to the kernel mapping matrix; and generating a terminal position-row vector function according to the main eigenvector and the kernel mapping matrix;
  • the position information corresponding to the channel response vector of the current position of the terminal is calculated.
  • the method further includes: acquiring a location-channel response vector of the plurality of known locations of the terminal, and establishing the terminal location-channel response database according to the location-channel response vector of the multiple known locations of the terminal. .
  • generating the kernel mapping matrix according to the terminal location-channel response database comprises: calling a data matrix in the terminal location-channel response database, and calculating a kernel mapping matrix of the data matrix by using a kernel function.
  • invoking the data matrix in the terminal location-channel response database comprises: calculating an initial range of the terminal by using a ray tracing algorithm, and calling a position-channel response vector of the terminal location-channel response database in an initial range to form a data matrix.
  • the method further includes: correcting the initial range according to the location information, and continuing the subsequent steps.
  • obtaining the main eigenvector according to the kernel mapping matrix comprises: normalizing the kernel mapping matrix to obtain a standardized kernel mapping matrix; performing eigenvalue decomposition on the normalized kernel mapping matrix to obtain a diagonal matrix composed of eigenvalues; A feature value contribution rate and a cumulative contribution rate are selected according to the cumulative contribution rate and the threshold value, and the feature vectors corresponding to all the main feature values are used as the main feature vector.
  • generating the terminal position-row vector function according to the main feature vector and the kernel mapping matrix comprises: projecting the kernel mapping matrix to the main feature vector to obtain a new channel response feature vector matrix; and performing a new channel response feature vector matrix Dimensionality reduction processing; linear regression processing is performed on the new channel response feature vector matrix after dimensionality reduction according to the terminal position corresponding to the new channel response feature vector after each dimension reduction processing, and the terminal position-row vector function is obtained.
  • the location information corresponding to the channel response vector of the current location of the terminal is calculated according to the main feature vector and the terminal location-row vector function, including: projecting the channel response vector of the current location of the terminal to the main feature vector to obtain a new feature vector.
  • the new feature vector is substituted into the terminal position-row vector function to calculate the position information.
  • An embodiment of the present invention provides a terminal positioning apparatus, including:
  • a modeling module configured to generate a kernel mapping matrix according to the terminal location-channel response database; obtain a main eigenvector according to the kernel mapping matrix; and generate a terminal position-row vector function according to the main eigenvector and the kernel mapping matrix;
  • Obtaining a module configured to obtain a channel response vector of a current location of the terminal
  • the calculation module is configured to calculate position information corresponding to the channel response vector of the current position of the terminal according to the main feature vector and the terminal position-row vector function.
  • modeling module is also set to:
  • the modeling module is configured to generate a kernel mapping matrix according to the terminal location-channel response database in the following manner:
  • the data matrix in the terminal location-channel response database is called, and the kernel mapping matrix of the data matrix is calculated using the kernel function.
  • the modeling module is configured to implement the calling of the data matrix in the terminal location-channel response database in the following manner:
  • the ray tracing algorithm is used to calculate the initial range of the terminal to be located, and the position-channel response vector of the terminal position-channel response database in the initial range is called to form a data matrix.
  • the modeling module is further configured to: correct the initial range according to the location information.
  • the modeling module is configured to obtain the main feature vector according to the kernel mapping matrix in the following manner:
  • the main feature value is selected for the rate and the threshold value, and the feature vector corresponding to all the main feature values is used as the main feature vector.
  • the modeling module is configured to generate a terminal position-row vector function according to the main feature vector and the kernel mapping matrix in the following manner:
  • the kernel mapping matrix is projected onto the main eigenvector to obtain a new channel response eigenvector matrix; the new channel response eigenvector matrix is subjected to dimensionality reduction processing; according to the terminal corresponding to each new channel response eigenvector after dimensionality reduction processing The position is subjected to linear regression processing on the new channel response feature vector matrix after the dimension reduction processing, and the terminal position-row vector function is obtained.
  • calculation module is set to:
  • the channel response vector of the current position of the terminal is projected to the main feature vector to obtain a new feature vector; the new feature vector is substituted into the terminal position-row vector function, and the position information is calculated.
  • the embodiment of the invention provides a terminal positioning system, which comprises the terminal positioning device provided by the embodiment of the invention.
  • the embodiment of the invention provides a terminal positioning method, which generates a main feature vector according to a terminal position-channel response database, and then obtains a position-row vector function according to the data matrix and the main feature vector, and the channel response vector of the terminal to be tested is in the main feature.
  • Vector projection generating a new vector substituting position-row vector function to calculate the position; in this process, only the main feature vector needs to be generated according to the terminal position-channel response database, which has lower requirements on the terminal position-channel response database, only needs to be
  • the data can be completed, which solves the problem that the generation of the relevant terminal location-channel response database is large.
  • the channel response vector of the terminal to be tested is projected in the main feature vector to generate a new vector substitution position-row vector function calculation position.
  • the calculation process is simple and fast, and solves the complicated problem of the existing terminal positioning process.
  • FIG. 1 is a schematic structural diagram of a terminal positioning apparatus according to a first embodiment of the present invention
  • FIG. 2 is a flowchart of a terminal positioning method according to a second embodiment of the present invention.
  • FIG. 3 is a schematic diagram of networking of a terminal positioning system in a third embodiment of the present invention.
  • FIG. 4 is a flowchart of a terminal positioning method in a third embodiment of the present invention.
  • FIG. 1 is a schematic structural diagram of a terminal locating device according to a first embodiment of the present invention.
  • the terminal locating device 1 provided by the present invention includes:
  • the modeling module 11 is configured to generate a kernel mapping matrix according to the terminal location-channel response database; obtain a main feature vector according to the kernel mapping matrix; and generate a terminal position-row vector function according to the main feature vector and the kernel mapping matrix;
  • the obtaining module 12 is configured to obtain a channel response vector of a current location of the terminal
  • the calculating module 13 is configured to calculate position information corresponding to the channel response vector of the current position of the terminal according to the main feature vector and the terminal position-row vector function.
  • the modeling module 11 in the above embodiment is further configured to:
  • the modeling module 11 in the above embodiment is configured to generate a kernel mapping matrix according to the terminal location-channel response database in the following manner:
  • the data matrix in the terminal location-channel response database is called, and the kernel mapping matrix of the data matrix is calculated using the kernel function.
  • the modeling module 11 in the above embodiment is configured to implement a call to the data matrix in the terminal location-channel response database in the following manner:
  • the ray tracing algorithm is used to calculate the initial range of the terminal to be located, and the position-channel response vector of the terminal position-channel response database in the initial range is called to form a data matrix.
  • the modeling module 11 in the above embodiment is further configured to:
  • the initial range is corrected based on the location information.
  • the embodiment of the present invention can implement terminal positioning with different precisions.
  • the terminal positioning accuracy requirement is low, the positioning can be achieved according to the position-channel response vector in the initial range.
  • the terminal positioning accuracy requirement is high, it is required. Corrected according to the initial range of positioning results until the user's needs are met.
  • the modeling module 11 in the above embodiment is configured to implement obtaining a main feature vector according to the kernel mapping matrix in the following manner:
  • the main feature value is selected for the rate and the threshold value, and the feature vector corresponding to all the main feature values is used as the main feature vector.
  • the modeling module 11 in the above embodiment is configured to generate a terminal position-row vector function according to the main feature vector and the kernel mapping matrix in the following manner:
  • the kernel mapping matrix is projected onto the main eigenvector to obtain a new channel response eigenvector matrix; the new channel response eigenvector matrix is subjected to dimensionality reduction; according to each dimension reduction processing
  • the terminal position corresponding to the new channel response feature vector performs linear regression processing on the channel response feature vector matrix after the dimensionality reduction process, and obtains the terminal position-row vector function.
  • the computing module 13 in the above embodiment is configured to:
  • the channel response vector of the current position of the terminal is projected to the main feature vector to obtain a new feature vector; the new feature vector is substituted into the terminal position-row vector function, and the position information is calculated.
  • the embodiment of the present invention provides a terminal positioning system, which includes the terminal positioning device 1 provided by the embodiment of the present invention.
  • the method in the following embodiments may be implemented in a server, and the above terminal locating device may be disposed in a server.
  • the terminal locating method provided by the embodiment of the present invention includes:
  • S201 Generate a kernel mapping matrix according to the terminal location-channel response database; obtain a primary eigenvector according to the kernel mapping matrix; and generate a terminal location-row vector function according to the primary eigenvector and the kernel mapping matrix;
  • S203 Calculate location information corresponding to a channel response vector of a current location of the terminal according to the main feature vector and the terminal position-row vector function.
  • the method before the method S201 in the foregoing embodiment, further includes: acquiring a location-channel response vector of a plurality of known locations of the terminal, and establishing a terminal location according to the location-channel response vector of the plurality of known locations of the terminal- Channel response database.
  • generating the kernel mapping matrix according to the terminal location-channel response database in the foregoing embodiment includes: calling a data matrix in the terminal location-channel response database, and calculating a kernel mapping matrix of the data matrix by using a kernel function.
  • the data matrix in the call terminal location-channel response database in the above embodiment includes: calculating the initial range of the terminal by using a ray tracing algorithm, and calling the location-channel response of the terminal location-channel response database in the initial range.
  • Vectors form the data matrix.
  • the initial range of the terminal may also be calculated by using a wireless positioning method (for example, a fingerprint positioning method based on Really Simple Syndication (RSS)).
  • a wireless positioning method for example, a fingerprint positioning method based on Really Simple Syndication (RSS)
  • the method in the above embodiment further includes: correcting the initial range according to the location information, and continuing the subsequent process.
  • the location information is added to the initial range, or the location information is added to the initial range, and one or more location information in the initial range that is the largest distance from the location information is deleted.
  • obtaining the main feature vector according to the kernel mapping matrix in the foregoing embodiment includes: normalizing the kernel mapping matrix to obtain a normalized kernel mapping matrix; performing eigenvalue decomposition on the normalized kernel mapping matrix to obtain a feature value The diagonal matrix; calculating the contribution rate of each eigenvalue and the cumulative contribution rate, selecting the main eigenvalue according to the cumulative contribution rate and the threshold value, and using the feature vector corresponding to all the main eigenvalues as the main feature vector.
  • generating the terminal position-row vector function according to the main feature vector and the kernel mapping matrix in the foregoing embodiment includes: projecting the kernel mapping matrix to the main feature vector to obtain a new channel response feature vector matrix; The channel response feature vector matrix is subjected to dimensionality reduction processing; linear regression processing is performed on the new channel response feature vector matrix after dimensionality reduction according to the terminal position corresponding to each new channel response feature vector after the dimensionality reduction processing, and the terminal position is obtained.
  • - Line vector function
  • calculating the location information corresponding to the channel response vector of the current location of the terminal according to the primary feature vector and the terminal location-row vector function in the foregoing embodiment includes: performing channel response vector of the current location of the terminal to the primary feature vector. Projection, a new feature vector is obtained; the new feature vector is substituted into the terminal position-row vector function, and the position information is calculated.
  • This embodiment is described by taking an indoor positioning system as an example, and provides an algorithm that can effectively utilize multipath information to reduce positioning errors.
  • the energy of each radial component in the channel between the AP and the terminal also has a statistically significant functional relationship between the terminal locations. If the energy-terminal position relationship of each radial component can be fully utilized, not only Improve estimation accuracy while using KPCA (Kernel principal) Component analysis, kernel principal component analysis) can also reduce the database storage space between the terminal location and the AP receiving energy, and the corresponding workload required to establish the database.
  • KPCA Kernel principal Component analysis
  • kernel principal component analysis kernel principal component analysis
  • the eigenvectors suitable for the current terminal position estimation are extracted by gradually narrowing the geographical range and the kernel principal component analysis method, and the regression method is used to realize the terminal position estimation, which can fully utilize the energy-terminal position relationship of each radial component, which can not only improve the estimation. Accuracy, while also reducing the database storage space between the terminal location and the AP receiving energy, and the corresponding workload required to build the database.
  • the kernel principal component analysis method is adopted for the channel response database, and the nonlinear feature vector in the multipath component can be extracted on the one hand, and the dimension of the feature vector can also be reduced, and the feature of the extracted smaller dimension is further
  • the vector is subjected to regression analysis to obtain the position estimate. Firstly, all the data matrices H' in the given range ⁇ are taken out from the terminal position-channel response database, and then the kernel principal component analysis is used to reduce the dimensionality of H'. Then, according to the data matrix H' and the corresponding known position, linear regression is performed to obtain the position-row vector linear function. Finally, the channel response vector of the current position of the terminal is projected onto the main eigenvector to obtain a new eigenvector, which is substituted into the position-row vector linearity. Function to estimate the terminal location.
  • each AP can obtain the channel response between the AP and the terminal, wherein the discrete channel response between the mth AP and the terminal is L-dimensional
  • the row vector h m (x) [h m,1 (x) h m,2 (x) ...
  • the terminal location channel response "map" which may be expressed as a matrix form as follows:
  • H [h(x(1)) ... h(x(n)) ... h(x(N))] T , where x(n) represents the nth known terminal position.
  • H is a three-dimensional matrix.
  • the second step projecting the kernel mapping matrix K to the main eigenvector to obtain a new channel response eigenvector matrix
  • the third step performing a support vector machine regression to obtain a position-row vector linear function according to the new channel response feature vector matrix and the corresponding known position;
  • Wi is the weight coefficient of the regression
  • bx is the regression coefficient of the x-axis
  • by is the regression of the y-axis coefficient.
  • the fourth step projecting the channel response vector of the current position of the terminal to the main feature vector to obtain a new feature vector
  • the terminal position is estimated.
  • the terminal positioning method provided by the present invention includes the following steps:
  • the terminal positioning algorithm provided by the present invention includes a two-stage working mode: an offline phase, which is mainly used to establish a terminal location-channel response database, and an online phase, which is mainly used to implement Terminal positioning.
  • This step is offline, using the mobile device to collect the channel response information of each reference point and the AP, and correlating the location information at the time of acquisition to construct a location fingerprint database.
  • the location fingerprint database (LFDB) is constructed in the offline phase.
  • N r represents the number of APs in the range of device communication in the offline phase
  • r i is the channel response data between the i-th AP and the terminal received by the sampling device
  • id i is the ID of the i-th AP.
  • a terminal position-channel response database is determined using a ray tracing algorithm.
  • the ray in the ray tracing algorithm may be transmitted directly from the transmitter to the receiver, or it may be multiple reflections, diffraction, and transmission.
  • this embodiment only considers the reflection condition, and the maximum number of reflections of the signal is 3 (because the energy of the signal has been greatly lost after 3 times of reflection, the influence can be ignored).
  • Tracking calculates all losses in each ray propagation process. The tracking calculation is performed until the ray reaches the receiver, and the data of the reference point in the located area is counted to form a location fingerprint database, that is, a terminal location-channel response database.
  • the discrete channel response between the mth AP and the terminal can be expressed as Where 0 ⁇ ⁇ m T s ⁇ T G , t is time, T s is the sampling interval, and ⁇ m, l is the normalized delay corresponding to the lth radial component of the channel between the mth AP and the terminal, T G is the complex gain after the dispersion of the discrete channel response between the mth AP and the terminal in the sampling interval is expressed as:
  • the Fast Fourier Transformation has a size of N.
  • ⁇ m,l is an integer
  • the complex gain h' m,l does not diverge at other sampling times; but when ⁇ m,l is a non-integer, the complex gain h' m,l is dispersed at other sampling times.
  • the discrete channel response h m (x) [h m,1 (x) h m,2 (x) ... h m,L (x)] between the diffused m-th AP and the terminal
  • the dispersed discrete channel response h m (x) [h m,1 (x) h m,2 (x) ... h m,L (x)].
  • the position estimation range is gradually reduced (for example, the position range can be narrowed down according to the empirical value, and the updated position range can be centered on the previous estimated position, and the previous position range is multiplied by the reduction.
  • the scale factor is obtained from the current position range), and the kernel principal component analysis and regression analysis method are used in each estimation range to obtain a position estimate with a small error range.
  • I is an N ⁇ N unit matrix, and a kernel mapping matrix
  • S403 Projecting the data matrix to the main feature vector to obtain a new channel response feature vector matrix This step is mainly to achieve the dimensionality reduction processing of the data matrix.
  • N NSV is the number of standard support vectors, and ⁇ is the insensitivity function.
  • S405 Project a channel response vector of the current location of the terminal to the main feature vector to obtain a new feature vector.
  • the new feature vector obtained is:
  • S406 Calculate the terminal position according to the new feature vector and the position-row vector linear function.
  • step S405 the new feature vector calculated in step S405 is substituted into the position-row vector linear function obtained in step S404, and the terminal position (x 0 , y 0 ) is estimated.
  • the embodiment of the invention provides a terminal positioning method, which generates a main feature vector according to a terminal position-channel response database, and then obtains a position-row vector function according to the data matrix and the main feature vector, and the channel response vector of the current position of the terminal is in the main feature.
  • Vector projection generating a new eigenvector substituting position-row vector function to calculate the position; in this process, only the main eigenvector needs to be generated according to the terminal position-channel response database, which has lower requirements on the terminal position-channel response database, only It takes several data to complete, and the terminal positioning is simply realized, which solves the problem of large database generation workload in related technologies.
  • the channel response vector of the current position of the terminal is projected in the main feature vector to generate a new feature vector.
  • the position-row vector function to calculate the position, the calculation process is simple and fast, and solves the complicated problem of the related terminal positioning process.
  • Embodiments of the present invention also provide a computer readable storage medium storing computer executable instructions for performing any of the methods described above.

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Abstract

一种终端定位方法、装置及***，该方法包括：根据终端位置-信道响应数据库生成核映射矩阵；根据核映射矩阵获取主特征向量；根据主特征向量及核映射矩阵生成终端位置-行向量函数；获取终端当前位置的信道响应向量；根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息。

Description

一种终端定位方法、装置及*** 技术领域
本文涉及但不限于定位领域，尤其涉及一种终端定位方法、装置及***。
背景技术
基于RSSI(Received Signal Strength Indication，接收信号强度指示)的终端定位方法，通常利用AP(Access Point，接入点)和终端之间的信道能量与AP和终端之间的距离满足一定统计意义下的函数关系，进而实现位置估计。由于函数关系未知，因此这类方法需要提前生成终端位置和多个AP的接收能量数据库，再进行位置-能量函数关系的学习或获取，从而实现无线定位，该方法存在数据库生成工作量大、过程计算复杂等的缺点。
因此，如何提供一种可以简化定位计算过程的终端定位方法，是本领域技术人员亟待解决的技术问题。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本发明实施例提供了一种终端定位方法、装置及***，能够简单地实现定位。
本发明实施例提供了一种终端定位方法，包括：
根据终端位置-信道响应数据库生成核映射矩阵；根据核映射矩阵获取主特征向量；根据主特征向量及核映射矩阵生成终端位置-行向量函数；
获取终端当前位置的信道响应向量；
根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息。
可选的，该方法之前还包括：获取终端的多个已知位置的位置-信道响应向量，根据所述终端的多个已知位置的位置-信道响应向量建立所述终端位置-信道响应数据库。
可选的，根据终端位置-信道响应数据库生成核映射矩阵包括：调用终端位置-信道响应数据库内的数据矩阵，利用核函数，计算数据矩阵的核映射矩阵。
可选的，调用终端位置-信道响应数据库内的数据矩阵包括：利用射线追踪算法计算终端的初始范围，调用终端位置-信道响应数据库在初始范围内的位置-信道响应向量，形成数据矩阵。
可选的，还包括：根据位置信息对初始范围进行修正，并继续后续步骤。
可选的，根据核映射矩阵获取主特征向量包括：对核映射矩阵进行标准化处理，获得标准化核映射矩阵；对标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为主特征向量。
可选的，根据主特征向量及核映射矩阵生成终端位置-行向量函数包括：将核映射矩阵向主特征向量进行投影，获得新的信道响应特征向量矩阵；对新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的新的信道响应特征向量对应的终端位置对降维处理后的新的信道响应特征向量矩阵进行线性回归处理，获取终端位置-行向量函数。
可选的，根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息包括：将终端当前位置的信道响应向量向主特征向量进行投影，得到新的特征向量；将新的特征向量代入终端位置-行向量函数，计算得到位置信息。
本发明实施例提供了一种终端定位装置，包括：
建模模块，设置为根据终端位置-信道响应数据库生成核映射矩阵；根据核映射矩阵获取主特征向量；根据主特征向量及核映射矩阵生成终端位置-行向量函数；
获取模块，设置为获取终端当前位置的信道响应向量；
计算模块，设置为根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息。
可选的，建模模块还设置为：
获取终端的多个已知位置的位置-信道响应向量，根据所述终端的多个已知位置的位置-信道响应向量建立所述终端位置-信道响应数据库。
可选的，建模模块是设置为采用以下方式实现根据终端位置-信道响应数据库生成核映射矩阵：
调用终端位置-信道响应数据库内的数据矩阵，利用核函数，计算数据矩阵的核映射矩阵。
可选的，建模模块是设置为采用以下方式实现调用所述终端位置-信道响应数据库内的数据矩阵：
利用射线追踪算法计算待定位终端的初始范围，调用终端位置-信道响应数据库在初始范围内的位置-信道响应向量，形成数据矩阵。
可选的，建模模块还设置为：根据位置信息对初始范围进行修正。
可选的，建模模块是设置为采用以下方式实现根据所述核映射矩阵获取主特征向量：
对核映射矩阵进行标准化处理，获得标准化核映射矩阵；对标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为主特征向量。
可选的，建模模块是设置为采用以下方式实现根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数：
将核映射矩阵向主特征向量进行投影，获得新的信道响应特征向量矩阵；对新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的新的信道响应特征向量对应的终端位置对降维处理后的新的信道响应特征向量矩阵进行线性回归处理，获取终端位置-行向量函数。
可选的，计算模块是设置为：
将终端当前位置的信道响应向量向主特征向量进行投影，得到新的特征向量；将新的特征向量代入终端位置-行向量函数，计算得到位置信息。
本发明实施例提供了一种终端定位***，包括本发明实施例提供的终端定位装置。
本发明实施例的有益效果：
本发明实施例提供了一种终端定位方法，根据终端位置-信道响应数据库生成主特征向量，然后根据数据矩阵及主特征向量得到位置-行向量函数，将待测终端的信道响应向量在主特征向量投影，生成新向量代入位置-行向量函数计算位置；在该过程中，仅需要根据终端位置-信道响应数据库生成主特征向量，其对终端位置-信道响应数据库的要求较低，仅需要数个数据即可完成，解决了相关终端位置-信道响应数据库的生成工作量大的问题，同时，将待测终端的信道响应向量在主特征向量投影，生成新向量代入位置-行向量函数计算位置，计算过程简单、速度快，解决了现有终端定位过程繁杂的问题。
在阅读并理解了附图和详细描述后，可以明白其他方面。
附图概述
图1为本发明第一实施例提供的终端定位装置的结构示意图；
图2为本发明第二实施例提供的终端定位方法的流程图；
图3为本发明第三实施例中的终端定位***的组网示意图；
图4为本发明第三实施例中的终端定位方法的流程图。
本发明的实施方式
现通过具体实施方式结合附图的方式对本发明做出进一步的诠释说明。
第一实施例：
图1为本发明第一实施例提供的终端定位装置的结构示意图，由图1可知，在本实施例中，本发明提供的终端定位装置1包括：
建模模块11，设置为根据终端位置-信道响应数据库生成核映射矩阵；根据核映射矩阵获取主特征向量；根据主特征向量及核映射矩阵生成终端位置-行向量函数；
获取模块12，设置为获取终端当前位置的信道响应向量；
计算模块13，设置为根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息。
在一些实施例中，上述实施例中的建模模块11还设置为：
获取终端的多个已知位置的位置-信道响应向量，根据所述终端的多个已知位置的位置-信道响应向量建立所述终端位置-信道响应数据库。
在一些实施例中，上述实施例中的建模模块11是设置为采用以下方式实现根据终端位置-信道响应数据库生成核映射矩阵：
调用终端位置-信道响应数据库内的数据矩阵，利用核函数，计算数据矩阵的核映射矩阵。
在一些实施例中，上述实施例中的建模模块11是设置为采用以下方式实现调用所述终端位置-信道响应数据库内的数据矩阵：
利用射线追踪算法计算待定位终端的初始范围，调用终端位置-信道响应数据库在初始范围内的位置-信道响应向量，形成数据矩阵。
在一些实施例中，上述实施例中的建模模块11还设置为：
根据位置信息对初始范围进行修正。这样，本发明实施例就可以实现不同精度的终端定位，当终端定位精度要求较低时，根据初始范围内的位置-信道响应向量即可实现定位，当终端定位精度要求较高时，就需要根据初始范围的定位结果进行修正，直至满足用户需求。
在一些实施例中，上述实施例中的建模模块11是设置为采用以下方式实现根据所述核映射矩阵获取主特征向量：
对核映射矩阵进行标准化处理，获得标准化核映射矩阵；对标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为主特征向量。
在一些实施例中，上述实施例中的建模模块11是设置为采用以下方式实现根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数：
将核映射矩阵向主特征向量进行投影，获得新的信道响应特征向量矩阵；对新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的 新的信道响应特征向量对应的终端位置对降维处理后的信道响应特征向量矩阵进行线性回归处理，获取终端位置-行向量函数。
在一些实施例中，上述实施例中的计算模块13是设置为：
将终端当前位置的信道响应向量向主特征向量进行投影，得到新的特征向量；将新的特征向量代入终端位置-行向量函数，计算得到位置信息。
对应的，本发明实施例提供了一种终端定位***，其包括本发明实施例提供的终端定位装置1。
以下实施例中的方法可以在服务器中实现，上述终端定位装置可以设置在服务器中。
第二实施例：
图2为本发明第二实施例提供的终端定位方法的流程图，由图2可知，在本实施例中，本发明实施例提供的终端定位方法包括：
S201：根据终端位置-信道响应数据库生成核映射矩阵；根据核映射矩阵获取主特征向量；根据主特征向量及核映射矩阵生成终端位置-行向量函数；
S202：获取终端当前位置的信道响应向量；
S203：根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息。
在一些实施例中，上述实施例中的方法S201之前还包括：获取终端的多个已知位置的位置-信道响应向量，根据终端的多个已知位置的位置-信道响应向量建立终端位置-信道响应数据库。
在一些实施例中，上述实施例中的根据终端位置-信道响应数据库生成核映射矩阵包括：调用终端位置-信道响应数据库内的数据矩阵，利用核函数，计算数据矩阵的核映射矩阵。
在一些实施例中，上述实施例中的调用终端位置-信道响应数据库内的数据矩阵包括：利用射线追踪算法计算终端的初始范围，调用终端位置-信道响应数据库在初始范围内的位置-信道响应向量，形成数据矩阵。
其中，终端的初始范围也可以采用无线定位方法(例如基于简易信息聚合(RSS，Really Simple Syndication)的指纹定位方法)计算得到。
在一些实施例中，上述实施例中的方法还包括：根据位置信息对初始范围进行修正，并继续后续流程。
可选的，将位置信息增加到初始范围中，或者将位置信息增加到初始范围中，并将初始范围中与位置信息距离最大的一个或多个位置信息删除。
在一些实施例中，上述实施例中的根据核映射矩阵获取主特征向量包括：对核映射矩阵进行标准化处理，获得标准化核映射矩阵；对标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为主特征向量。
在一些实施例中，上述实施例中的根据主特征向量及核映射矩阵生成终端位置-行向量函数包括：将核映射矩阵向主特征向量进行投影，获得新的信道响应特征向量矩阵；对新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的新的信道响应特征向量对应的终端位置对降维处理后的新的信道响应特征向量矩阵进行线性回归处理，获取终端位置-行向量函数。
在一些实施例中，上述实施例中的根据主特征向量及终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息包括：将终端当前位置的信道响应向量向主特征向量进行投影，得到新的特征向量；将新的特征向量代入终端位置-行向量函数，计算得到位置信息。
现结合具体应用场景对本发明做进一步的诠释说明。
第三实施例：
本实施例以室内定位***为例进行说明，提供了一种可有效地利用多径信息来减少定位误差的算法。
在实际应用中，AP和终端之间的信道中每一个径分量的能量与终端位置之间也具有统计意义下的函数关系，如果能够充分利用每一个径分量的能量-终端位置关系，不仅可以提高估计精度，同时利用KPCA(Kernel principal  component analysis，核主成分分析)还可以降低终端位置与AP接收能量之间的数据库存储空间，以及相应的建立数据库所需的工作量。本实施例提出了一种基于终端-AP信道响应的核主成分分析的无线定位算法，因为信道响应中包含的多径能量-终端位置的函数关系具有非线性特点，同时具有地域局限性特点，采用逐步缩小地域范围和核主成分分析方法，提取适用于当前终端位置估计的特征向量，进一步采用回归方法实现终端位置估计，能够充分利用每一个径分量的能量-终端位置关系，不仅可以提高估计精度，同时还可以降低终端位置与AP接收能量之间的数据库存储空间，以及相应的建立数据库所需的工作量。
本实施例主要通过对信道响应数据库采用核主成分分析的方法，一方面可以提取多径分量中的非线性特征向量，同时也能够降低特征向量的维数，进一步针对提取的较小维度的特征向量进行回归分析，得到位置估计；首先从终端位置-信道响应数据库中取出所有在给定范围Φ内的数据矩阵H′，然后利用核主成分分析对H′进行降维处理得到
Figure PCTCN2016081894-appb-000001
然后根据数据矩阵H′以及相应的已知位置，进行线性回归获得位置-行向量线性函数，最后将终端当前位置的信道响应向量向主特征向量投影得到新的特征向量，代入位置-行向量线性函数，估计终端位置。
具体的，如图3所示，假定有一典型的无线局域网(WLAN，Wireless Local Area Networks)定位***，其中有一个位置待定的移动终端和M个AP(AP位置未知)，令移动终端的位置为二维向量x＝[xx,xy](对应二维平面)，每个AP都能获得AP与终端之间的信道响应，其中第m个AP与终端之间的离散信道响应为L维行向量hm(x)＝[hm,1(x) hm,2(x) ... hm,L(x)]，其中hm,l(x)为第l采样时刻第m个AP与终端之间的信道的复增益，M个AP与终端之间的离散信道响应的总向量可以表示为ML维矩阵h(x)＝[h1(x) h2(x) ... hM(x)]T。
假定该***提前已经在N个已知位置记录了终端位置与AP之间的信道响应向量，即终端位置信道响应“地图”，该“地图”可以表示为如下矩阵形式：
H＝[h(x(1)) ... h(x(n)) ... h(x(N))]T，其中x(n)表示第n个已知的终端位置。
H为三维矩阵。
无线定位问题就是给定当前终端的总信道向量h(x)＝[h1(x) h2(x) ... hM(x)]T，如何利用已有的终端位置信道响应“地图”估计得到终端位置。由于基于核主成分分析的定位方法在每次给定范围空间内的位置估计具有相同的算法步骤，下面仅以一次位置估计为例进行介绍：
第一步：从终端位置-信道响应数据库中取出所有在给定范围Φ内的数据矩阵如下：H'＝[... h(x(n)) ...]T；选取典型的核函数K(·,·)，可以得到对应核函数和数据矩阵的N'×N'维核映射矩阵K，N’为给定范围F中已知位置的个数，Ki,j＝K(h(x(i)),h(x(j)))，对该核映射矩阵进行特征值分解，其中特征值构成的对角矩阵为D＝Diag(λ1,λ2,...,λN')，第l个特征向量为al。选取其中的L'个主特征向量，则对应的主特征值和主特征向量分别为
Figure PCTCN2016081894-appb-000002
和
Figure PCTCN2016081894-appb-000003
其中，L'<<N'。
第二步：将核映射矩阵K向主特征向量投影得到新的信道响应特征向量矩阵
Figure PCTCN2016081894-appb-000004
Figure PCTCN2016081894-appb-000005
其中，
Figure PCTCN2016081894-appb-000006
其中，
Figure PCTCN2016081894-appb-000007
为第l个特征向量的第n个元素。
第三步：根据新的信道响应特征向量矩阵以及相应的已知位置，进行支持向量机回归获得位置-行向量线性函数；
Figure PCTCN2016081894-appb-000008
其中，Wi为回归的权重系数，bx为x轴的回归系数，by为y轴的回归 系数。
第四步：将终端当前位置的信道响应向量向主特征向量投影得到新的特征向量，
Figure PCTCN2016081894-appb-000009
代入第三步中的线性回归后拟合的位置-行向量线性函数，估计终端位置。
现结合图4对本发明做进一步的诠释说明，如图4所示，在本实施例中，本发明提供的终端定位方法包括以下步骤：
S401：建立终端位置-信道响应数据库。
***模型如图3所示，假定AP的数目为3，本发明提供的终端定位算法包括两阶段的工作模式：离线阶段，主要用于建立终端位置-信道响应数据库；在线阶段，主要用于实现终端定位。
本步骤为离线状态，使用移动设备采集每一个参考点与AP的信道响应信息，并关联上采集时的位置信息，以构建一个位置指纹数据库。位置指纹数据库(LFDB)的构建是在离线阶段完成，位置指纹数据是由众多数据库元素组成：DBE＝{L,R}，其中L是物理位置，R表示在该位置上采集的指纹，表示为：
Figure PCTCN2016081894-appb-000010
其中，Nr表示在离线阶段设备通信范围内AP的数目，ri是采样设备接收到的第i个AP与终端之间的信道响应数据，idi是第i个AP的ID。
利用射线追踪算法确定终端位置-信道响应数据库。射线追踪算法中的射线可能是从发射机直接传播到接收机，也可能是经多次反射、衍射、透射 等到达接收机，本实施例只考虑反射情况，并且信号最大反射次数为3(因为信号经过3次反射后能量已经损耗很大，可以忽略其影响)。跟踪计算每个射线传播过程中的所有损耗。一直跟踪计算直至射线到达接收机，统计所定位区域中参考点的数据，形成位置指纹数据库，即终端位置-信道响应数据库。
在实际应用中，每个AP都能获得AP与终端之间的信道响应，其中第m个AP与终端之间的离散信道响应为L维行向量h'm(x)＝[h'm,1(x) h'm,2(x) ... h'm,L(x)]，其中h'm,l(x)为第l采样时刻第m个AP与终端之间的信道的复增益。M个AP与终端之间的离散信道响应的总向量可以表示为ML维矩阵h'(x)＝[h'1(x) h'2(x) ... h'M(x)]。但是在实际的正交频分复用技术(OFDM，Orthogonal Frequency Division Multiplexing)***中，信道响应会在采样点上发生相应的弥散。其中第m个AP与终端之间的离散信道响应可以表示为
Figure PCTCN2016081894-appb-000011
其中，0≤τmTs≤TG，t为时间，Ts为采样间隔，τm，l为第m个AP与终端之间信道的第l个径分量对应的归一化时延，TG为采样时间间隔第m个AP与终端之间的离散信道响应弥散后的复增益表示为：
Figure PCTCN2016081894-appb-000012
，其中OFDM***中快速傅里叶变换(FFT，Fast Fourier Transformation)大小为N。当τm,l为整数时，复增益h'm,l不在其他采样时间发生弥散；但是当τm,l为非整数时，复增益h'm,l在其他采样时间发生弥散。那么弥散后的第m个AP与终端之间的离散信道响应hm(x)＝[hm,1(x) hm,2(x) ... hm,L(x)]那么信道响应数据库表示为：H＝[h(x(1)) ... h(x(n)) ... h(x(N))]T其中x(n)表示第n个已知的终端位置。
S402：根据终端位置-信道响应数据库计算主特征向量。
本步骤以及以下所有步骤均为在线阶段，主要是利用位置关系与指纹数据库信息的对应关系，通过测量到的指纹确定位置的算法过程，首先，用射 线追踪算法测量目标位置的信道响应h(x)，其中第m个AP与终端之间的离散信道响应为L维行向量h'm(x)＝[h'm,1(x) h'm,2(x) ... h'm,L(x)]，其中h'm,l(x)为第l采样时刻第m个AP到终端之间的信道的复增益。弥散后的离散信道响应hm(x)＝[hm,1(x) hm,2(x) ... hm,L(x)]。为了能够获得不同精度的位置估计，采用逐步缩小位置估计范围(例如，可以按照经验值选取位置范围缩小比例因子，更新后的位置范围可以是以前次估计位置为中心，前次位置范围乘以缩小比例因子得到本次位置范围)，并在每次估计范围内采用核主成分分析和回归分析的方法得到具有较小误差范围的位置估计。
本步骤首先数据矩阵H'＝[... h(x(n)) ...]T，选取多项式核函数K(xi,xj)＝(<xi,xj>+d)p,p∈N,d≥0,得到对应数据矩阵的N×N维核映射矩阵K，其中Ki,j＝K(h(x(i)),h(x(j)))，N为样本个数，即数据矩阵中已知位置的个数。其中，
Figure PCTCN2016081894-appb-000013
对上述K矩阵进行标准化处理得到：
Figure PCTCN2016081894-appb-000014
其中，
Figure PCTCN2016081894-appb-000015
I为N×N的单位矩阵，对核映射矩阵
Figure PCTCN2016081894-appb-000016
进行特征值分解得到核映射矩阵的特征值和特征向量，其中,由特征值构成的对角矩阵为D＝Diag(λ1,λ2,...,λN)，第l个特征向量为al；依次求出每一个特征值贡献率，和相应的特征值累计贡献率
Figure PCTCN2016081894-appb-000017
并且与累计门限值(如98％)比较，当特征值累计贡献率大于累计门限值时，停止计算，选出相应的特征值λ1，λ2...λL，按特征值大小选取其中的L个主特征向量，则对应的主特征值和主特征向量为
Figure PCTCN2016081894-appb-000018
和
Figure PCTCN2016081894-appb-000019
S403：将数据矩阵向主特征向量投影得到新的信道响应特征向量矩阵
Figure PCTCN2016081894-appb-000020
本步骤主要为了实现对数据矩阵的降维处理。
Figure PCTCN2016081894-appb-000021
其中；
Figure PCTCN2016081894-appb-000022
S404：根据相应的已知位置对新的信道响应特征向量矩阵
Figure PCTCN2016081894-appb-000023
进行支持向量机回归，获取位置-行向量线性函数。
具体的，
Figure PCTCN2016081894-appb-000024
Figure PCTCN2016081894-appb-000025
αi
Figure PCTCN2016081894-appb-000026
是SVR(支持向量机)求解出来的拉格朗日乘数；
Figure PCTCN2016081894-appb-000027
NNSV为标准支持向量的个数，ε为不敏感度函数。
S405：将终端当前位置的信道响应向量向主特征向量投影得到新的特征向量。
得到的新的特征向量为：
Figure PCTCN2016081894-appb-000028
S406：根据新的特征向量及位置-行向量线性函数计算终端位置。
本步骤将步骤S405中计算得到的新的特征向量代入步骤S404得到的位置-行向量线性函数，估计终端位置(x0,y0)。
S407：修正地域范围，获得最精确定位。
根据初始估计终端位置(x0,y0)，逐步缩小地域范围，即缩小数据库的范围；确定坐标调整精度为m，横坐标x调整为x∈[x0-m,x0+m]，纵坐标y调整为y∈[y0-m,y0+m]，x,y∈N*；从终端位置-信道响应数据库中取出所有在给定范围Φ内的数据矩阵如下：H'＝[h(x(1)) ... h(x(n)) ...]T,n∈Φ,且有N'＝|Φ|，然后根据上述步骤S402-S406，重新估算终端位置(x1，y1)。迭代上述步骤，获得终端位置(x2，y2)，(x3，y3)，根据经验值，将(x3，y3)作为估算终端位置的最终坐标。
综上可知，通过本发明的实施，至少存在以下有益效果：
本发明实施例提供了一种终端定位方法，根据终端位置-信道响应数据库生成主特征向量，然后根据数据矩阵及主特征向量得到位置-行向量函数，将终端当前位置的信道响应向量在主特征向量投影，生成新的特征向量代入位置-行向量函数计算位置；在该过程中，仅需要根据终端位置-信道响应数据库生成主特征向量，其对终端位置-信道响应数据库的要求较低，仅需要数个数据即可完成，简单的实现了终端定位，解决了相关技术中数据库的生成工作量大的问题，同时，将终端当前位置的信道响应向量在主特征向量投影，生成新的特征向量代入位置-行向量函数计算位置，计算过程简单、速度快，解决了相关终端定位过程繁杂的问题。
本发明实施例还提出了一种计算机可读存储介质，存储有计算机可执行指令，计算机可执行指令用于执行上述描述的任意一个方法。
本领域普通技术人员可以理解上述方法中的全部或部分步骤可通过程序来指令相关硬件(例如处理器)完成，所述程序可以存储于计算机可读存储介质中，如只读存储器、磁盘或光盘等。可选地，上述实施例的全部或部分步骤也可以使用一个或多个集成电路来实现。相应地，上述实施例中的各模 块/单元可以采用硬件的形式实现，例如通过集成电路来实现其相应功能，也可以采用软件功能模块的形式实现，例如通过处理器执行存储与存储器中的程序/指令来实现其相应功能。本发明不限于任何特定形式的硬件和软件的结合。
以上仅是本发明的具体实施方式而已，并非对本发明做任何形式上的限制，凡是依据本发明的技术实质对以上实施方式所做的任意简单修改、等同变化、结合或修饰，均仍属于本发明技术方案的保护范围。
工业实用性
上述技术方案简单的实现了定位。

Claims (17)

  1. 一种终端定位方法，包括：
    根据终端位置-信道响应数据库生成核映射矩阵；根据所述核映射矩阵获取主特征向量；根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数；
    获取终端当前位置的信道响应向量；
    根据所述主特征向量及所述终端位置-行向量函数，计算所述终端当前位置的信道响应向量对应的位置信息。
  2. 如权利要求1所述的终端定位方法，该方法之前还包括：
    获取终端的多个已知位置的位置-信道响应向量，根据所述终端的多个已知位置的位置-信道响应向量建立所述终端位置-信道响应数据库。
  3. 如权利要求2所述的终端定位方法，其中，所述根据终端位置-信道响应数据库生成核映射矩阵包括：
    调用所述终端位置-信道响应数据库内的数据矩阵，利用核函数，计算所述数据矩阵的核映射矩阵。
  4. 如权利要求3所述的终端定位方法，其中，所述调用所述终端位置-信道响应数据库内的数据矩阵包括：
    利用射线追踪算法计算所述终端的初始范围，调用所述终端位置-信道响应数据库在所述初始范围内的位置-信道响应向量，形成所述数据矩阵。
  5. 如权利要求4所述的终端定位方法，还包括：根据所述位置信息对所述初始范围进行修正，并继续后续步骤。
  6. 如权利要求1至5任一项所述的终端定位方法，其中，所述根据所述核映射矩阵获取主特征向量包括：对所述核映射矩阵进行标准化处理，获得标准化核映射矩阵；对所述标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据所述累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为所述主特征向量。
  7. 如权利要求6所述的终端定位方法，其中，所述根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数包括：
    将所述核映射矩阵向所述主特征向量进行投影，获得新的信道响应特征向量矩阵；对所述新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的新的信道响应特征向量对应的终端位置对降维处理后的新的信道响应特征向量矩阵进行线性回归处理，获取所述终端位置-行向量函数。
  8. 如权利要求7所述的终端定位方法，其中，所述根据所述主特征向量及所述终端位置-行向量函数，计算终端当前位置的信道响应向量对应的位置信息包括：
    将所述终端当前位置的信道响应向量向所述主特征向量进行投影，得到新的特征向量；将所述新的特征向量代入所述终端位置-行向量函数，计算得到所述位置信息。
  9. 一种终端定位装置，包括：
    建模模块，设置为根据终端位置-信道响应数据库生成核映射矩阵；根据所述核映射矩阵获取主特征向量；根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数；
    获取模块，设置为获取终端当前位置的信道响应向量；
    计算模块，设置为根据所述主特征向量及所述终端位置-行向量函数，计算所述终端当前位置的信道响应向量对应的位置信息。
  10. 如权利要求9所述的终端定位装置，所述建模模块还设置为：
    获取终端的多个已知位置的位置-信道响应向量，根据所述终端的多个已知位置的位置-信道响应向量建立所述终端位置-信道响应数据库。
  11. 如权利要求10所述的终端定位装置，其中，所述建模模块是设置为采用以下方式实现根据终端位置-信道响应数据库生成核映射矩阵：
    调用所述终端位置-信道响应数据库内的数据矩阵，利用核函数，计算所述数据矩阵的核映射矩阵。
  12. 如权利要求11所述的终端定位装置，其中，所述建模模块是设置为采用以下方式实现调用所述终端位置-信道响应数据库内的数据矩阵：
    利用射线追踪算法计算所述待定位终端的初始范围，调用所述终端位置-信道响应数据库在所述初始范围内的位置-信道响应向量，形成所述数据矩阵。
  13. 如权利要求12所述的终端定位装置，所述建模模块还设置为：
    根据所述位置信息对所述初始范围进行修正。
  14. 如权利要求9至13任一项所述的终端定位装置，其中，所述建模模块是设置为采用以下方式实现根据所述核映射矩阵获取主特征向量：
    对所述核映射矩阵进行标准化处理，获得标准化核映射矩阵；对所述标准化核映射矩阵进行特征值分解，获得由特征值构成的对角矩阵；计算每一个特征值贡献率、及累计贡献率，根据所述累计贡献率及门限值选出主特征值，将所有主特征值对应的特征向量作为所述主特征向量。
  15. 如权利要求14所述的终端定位装置，其中，所述建模模块是设置为采用以下方式实现根据所述主特征向量及所述核映射矩阵生成终端位置-行向量函数：
    将所述核映射矩阵向所述主特征向量进行投影，获得新的信道响应特征向量矩阵；对所述新的信道响应特征向量矩阵进行降维处理；根据每一个降维处理后的新的信道响应特征向量对应的终端位置对降维处理后的新的信道响应特征向量矩阵进行线性回归处理，获取所述终端位置-行向量函数。
  16. 如权利要求15所述的终端定位装置，其中，所述计算模块是设置为：
    将所述终端当前位置的信道响应向量向所述主特征向量进行投影，得到新的特征向量；将所述新的特征向量代入所述终端位置-行向量函数，计算得到所述位置信息。
  17. 一种终端定位***，包括如权利要求9至16任一项所述的终端定位装置。
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