WO2020103908A1 - 确定终端位置的方法、装置及存储介质 - Google Patents

确定终端位置的方法、装置及存储介质

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Publication number
WO2020103908A1
WO2020103908A1 PCT/CN2019/119975 CN2019119975W WO2020103908A1 WO 2020103908 A1 WO2020103908 A1 WO 2020103908A1 CN 2019119975 W CN2019119975 W CN 2019119975W WO 2020103908 A1 WO2020103908 A1 WO 2020103908A1
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WO
WIPO (PCT)
Prior art keywords
base station
terminal
distance
measurement
measurement parameters
Prior art date
Application number
PCT/CN2019/119975
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English (en)
French (fr)
Inventor
蒲莉红
曾刚
周功财
Original Assignee
中兴通讯股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中兴通讯股份有限公司 filed Critical 中兴通讯股份有限公司
Priority to EP19887006.5A priority Critical patent/EP3886510A4/en
Publication of WO2020103908A1 publication Critical patent/WO2020103908A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location

Definitions

  • the present disclosure relates to the field of communication technologies, and in particular, to a method, device, and storage medium for determining the location of a terminal.
  • the positioning process of a base station in a WCDMA (Wideband Code Division Multiple Access) network usually includes the following processing:
  • Step 1 The SP (Service Provider) initiates a positioning request and carries the information of the target terminal that needs to be located;
  • Step 2 The GMLC (Gateway Mobile Location) Center authenticates the request message and triggers the positioning request To the core network;
  • Step 3 After receiving the positioning request, the core network performs routing judgment, and then sends a positioning request (POSITIONINITIATIONREQUEST) to SAS (Stand-Alone SMLC (Service Mobile Mobile Locating Center)); step four : SAS triggers a measurement request (POSITION ACTIVATION REQUEST) after receiving a positioning request;
  • Step 5 SAS receives a measurement report (POSITION ACTIVATION RESPONSE); allows the steps of triggering a measurement request and receiving a measurement report repeatedly when the measurement process is executed multiple times.
  • Step 6 After receiving the measurement report, SAS performs positioning to estimate the terminal position, and finally returns the estimated position result to POSITION INITIATION RESPONSE; after receiving the positioning result, GMLC responds
  • the above positioning method is only based on the measurement information of a single base station, and the positioning accuracy is low, which can only be used as the most basic positioning capability.
  • the present disclosure provides a method, device and storage medium for determining the location of a terminal to solve the problem of low positioning accuracy of the terminal in the related art.
  • a method for determining the location of a terminal including: acquiring measurement parameters of each base station from a positioning measurement report of the terminal; and inputting the measurement parameters corresponding to each base station into the estimation of each base station respectively Distance model to obtain multiple estimates of the distance between each base station and the terminal, the distance estimation model is obtained by training based on historical measurement parameters of the base station and the measured distance between the base station and the terminal; according to the difference between each base station and the terminal Multiple estimates of the distance between determine the location of the terminal.
  • an apparatus for determining the location of a terminal including: an acquisition module for acquiring measurement parameters of each base station from a positioning measurement report of the terminal; an input module for mapping corresponding The measurement parameters are respectively input into the distance estimation model of each base station to obtain multiple estimated values of the distance between each base station and the terminal, and the distance estimation model is trained based on historical measurement parameters of the base station and the measured distance between the base station and the terminal.
  • the determination module is used to determine the position of the terminal according to multiple estimates of the distance between each base station and the terminal.
  • an apparatus for determining the location of a terminal including: a processor; a memory for storing processor-executable instructions; when the instructions are executed by the processor, the following operations are performed: Obtain the measurement parameters of each base station in the positioning measurement report of the terminal; input the measurement parameters corresponding to each base station into the distance estimation model of each base station respectively to obtain multiple estimated values of the distance between each base station and the terminal.
  • the distance estimation model is obtained by training based on historical measurement parameters of the base station and the measured distance between the base station and the terminal; the location of the terminal is determined according to multiple estimates of the distance between each base station and the terminal.
  • a non-transitory computer-readable storage medium which, when instructions in the storage medium are executed by a processor, enables the processor to execute the description according to the first aspect of the present disclosure Methods.
  • Fig. 1 is a flowchart of a method for determining a location of a terminal according to an exemplary embodiment
  • Fig. 2 is a flowchart of a method for determining a location of a terminal according to an exemplary embodiment
  • Fig. 3 is an effect diagram showing a distance between a terminal and a base station obtained based on measurement parameters of a base station of 30104 according to an exemplary embodiment
  • Fig. 4 is a linear regression fitting diagram of a base station 30104 according to an exemplary embodiment
  • Fig. 5 is an effect diagram showing a distance between a terminal and a base station obtained based on measurement parameters of a base station of 32924 according to an exemplary embodiment
  • Fig. 6 is a linear regression fitting diagram of 32924 base stations according to an exemplary embodiment
  • Fig. 7 is an effect diagram showing a distance between a terminal and a base station obtained based on measurement parameters of 34465 base stations according to an exemplary embodiment
  • Fig. 8 is a linear regression fitting diagram of a 34465 base station according to an exemplary embodiment
  • Fig. 9 is an effect diagram showing a distance between a terminal and a base station obtained based on measurement parameters of a base station of 33876 according to an exemplary embodiment
  • Fig. 10 is a linear regression fitting diagram of a base station of 33876 shown according to an exemplary embodiment
  • Fig. 11 is a block diagram of a device for determining a location of a terminal according to an exemplary embodiment.
  • Fig. 12 is a block diagram of a device for determining a location of a terminal according to an exemplary embodiment.
  • Fig. 1 is a flowchart of a method for determining a location of a terminal according to an exemplary embodiment. As shown in Fig. 1, the method includes the following steps:
  • Step 101 Obtain the measurement parameters of each base station from the positioning measurement report of the terminal;
  • the positioning measurement report may be obtained by terminal measurement.
  • the measurement report is also called Cell-ID Measurements Results Info List.
  • the measurement report is the information of the serving cell where the terminal is located and the base station of the neighboring cell.
  • the measurement report may include multiple The measurement parameters of each base station, such as the wireless measurement parameters of the 3G base station, can analyze the measured fields of each base station according to the corresponding communication protocol, and the obtained measurement parameters can include: RSCP (ReceivedSignalCodePower, receiving Signal code power), RNC (Radio Network Controller) ID, CELL ID (cell identification in the base station), RTT (Round-Trip Time, round trip time) and RTT Diffence (difference) or Various parameters.
  • RSCP ReceiveivedSignalCodePower, receiving Signal code power
  • RNC Radio Network Controller
  • CELL ID cell identification in the base station
  • RTT Diffence redifference
  • Step 102 Input the measurement parameters corresponding to each base station into the distance estimation model of each base station respectively to obtain multiple estimated values of the distance between each base station and the terminal.
  • the distance estimation model is based on historical measurement parameters of the base station and the base station Obtain training by measuring the distance with the terminal;
  • the distance estimation model may be, for example, a linear regression model.
  • the model may be based on the concept of big data to find a combination parameter between multiple historical measurement parameters of the same base station, and to approximate the relationship between the dependent variable and the variable to establish the model. Assuming that the measurement parameters RSCP, Ec / N0, Pathloss, and measurement distance (the measurement distance between the base station and the terminal) are known, a linear regression model is used to calculate the linear combination parameters (ie, the corresponding coefficients of each measurement parameter in the relationship), and find The optimal linear combination parameters are obtained, and the linear regression model of the base station is obtained.
  • RSCP represents the signal strength of the base station received by the terminal
  • Ec / N0 represents the interference level of the received signal strength
  • Pathloss represents the path loss, also known as the loss of the communication line.
  • the distance estimation model can be established based on the measurement parameters in the measurement report obtained from multiple measurements, and the model can be continuously updated using the measurement parameters collected subsequently.
  • Step 103 Determine the location of the terminal according to multiple estimates of the distance between each base station and the terminal.
  • a triangular positioning algorithm may be used to solve and calculate the terminal position information based on the distance between each base station and the terminal. Therefore, the accuracy of the measurement distance between the base station and the terminal involved in the positioning calculation directly affects the positioning accuracy.
  • the distance between the base station and the terminal cannot be directly obtained, and can only be calculated indirectly through parameters such as signal strength RSCP. Therefore, on the basis of accurately estimating the distance between the base station and the terminal using a linear regression model, the positioning accuracy of the positioning algorithm can be effectively improved.
  • the above distance estimation model may be established in advance, and based on this, the method for determining the location of the terminal may further include: before inputting the measurement parameters corresponding to each base station into the distance estimation model of each base station, The linear relationship between each historical measurement parameter and the measurement distance between the base station and the terminal establishes a linear regression relationship, wherein, in the relationship, each historical measurement parameter corresponds to a linear parameter; The value of each historical measurement parameter and the value of the measurement distance are used to calculate the value of each linear parameter to obtain a distance estimation model of the base station. Among them, all linear parameters in the relationship are also called combined parameters.
  • the historical measurement parameter may include at least one of the following: the signal strength of the base station received by the terminal, the interference of the signal strength received by the terminal, and the communication path loss. These parameters can be obtained from the bill by reading the bill.
  • the bill can include base station RNCID, CELLID, RSCP, RTT, and RTTDiffence.
  • the distance estimation model corresponding to each base station is trained. During the use of the distance model, the distance estimation model corresponding to the base station may be updated according to the measurement parameters of each base station subsequently collected to further improve the accuracy of the distance estimation model.
  • the distance between the base station and the terminal is determined by multiple measurement parameters, such as RSCP, Ec / N0, and Pathloss. Therefore, a linear regression model is established according to the three determinants of distance (1, RSCP, Ec / N0, Pathloss), and the influence weight of each measurement parameter is ( ⁇ 0 , ⁇ R , ⁇ E , ⁇ P ).
  • the linear regression function is constructed as follows:
  • h ( ⁇ ) is the distance between the base station and the terminal
  • x is (x R , x E , x P ... x n ) T column vector, which can represent the influencing factors of the regression function, for example, x R , x E and x P can be the above-mentioned RSCP, Ec / N0 and Pathloss in sequence, where ⁇ T can be a ( ⁇ 0 , ⁇ R , ⁇ E , ⁇ P ) row vector, which represents the weight coefficient of each influencing factor.
  • the loss function is constructed as follows:
  • x (i) represents the i-th element in the vector x
  • y (i) represents the i-th element in the vector y
  • h ⁇ (x (i) represents a known hypothesis function
  • m is the number of training sets Using this loss function, the deviation of the distance obtained by the above linear regression function can be calculated.
  • the linear regression parameter estimation process in the above linear regression model may include the following processing:
  • the parameter a can be used to represent ⁇ and the parameter b to be represented (xR, xE, xP ... xn).
  • the parameters a and b are both unknown and need to be calculated based on the sample data (x i , y i ).
  • the principle of determining the values of parameters a and b is to make the fitting state of the regression line of the sample to the observation value the best, that is, to minimize the deviation between the estimated distance between the terminal and the base station and the measured value.
  • the least square method can be used to seek the optimal parameters.
  • a y i can be obtained, which is an estimated value of y i .
  • Deviation between estimated and measured values There are n deviations corresponding to n measured values.
  • the linear regression model can be determined with the minimum sum of squared errors as the criterion. At this time, the demand is as follows:
  • Q is a minimum value. According to the extreme value theorem in calculus, if the above formula is to take an extreme value, the partial derivative of a and b should be 0, so that the above linear parameters can be obtained.
  • calculating the values of the linear parameters according to the values of the historical measurement parameters and the measured distance values may include: finding the optimal values of the linear parameters by a gradient descent method value.
  • the gradient descent method is an optimization algorithm, usually also called the steepest descent method.
  • the steepest descent method uses the negative gradient direction as the search direction. The closer the steepest descent method is to the target value, the smaller the step size is, and the slower the progress.
  • the gradient descent method can be used to solve iteratively step by step to obtain the minimized loss function and the model parameter value.
  • the following processing may be included: determining the step size to the next step, called Learning rate: ⁇ ; arbitrarily given an initial value: ( ⁇ 1 , ⁇ 2 ,... ⁇ n-1 , ⁇ n ); determine a downward direction, and make a predetermined step downward, and update ( ⁇ 1 , ⁇ 2 ,... ⁇ n-1 , ⁇ n ); when the height of the drop is less than a certain
  • the method for determining the position of a terminal calculates the distance between the terminal and each base station based on the distance estimation model obtained by pre-training corresponding to each base station, and then positions the terminal according to the distance between the terminal and each base station Terminal positioning accuracy.
  • the slave terminal Obtaining the measurement parameters of each base station in the positioning measurement report may include: according to the RNC ID or the cell ID CELL ID in the measurement report, merge the measurement parameters of sectors with the same longitude and latitude as the measurement parameters of the same base station.
  • the measurement parameter used is RSCP as an example, as shown in FIG. 2,
  • the process can include:
  • the measurement report may include measurement parameters of multiple base stations. According to the corresponding protocol, parsing the fields of each measurement parameter of each base station can obtain: RSCP / RNCID / CELLID / RTT / RTTDiffence and other parameters.
  • the latitude and longitude of each base station are queried, and the measurement parameters corresponding to the sectors with the same latitude and longitude are combined into the measurement parameters of one base station as the measurement parameters of the base station.
  • the training library According to the signal strength RSCP value measured by each base station in the measurement report, query the training library to obtain the training distance estimate S, which may include historical measurement parameters and the distance between the terminal and the base station calculated based on the historical parameters to , And may also include the aforementioned distance estimation model.
  • the distance estimation model can be used to calculate the distance between the terminal and the base station. In this step, each base station in the measurement report needs to calculate an estimated value Sn.
  • the training data (the above-mentioned historical measurement parameters) used by the training distance estimation model is derived from the measured parameters of a 3G base station.
  • the distance estimation model is mainly constructed by the relationship between RSCP and the measured distance.
  • the format of the collected data is, for example (CELLID, RNCID, RADIUS, RSCP), where CELL ID / RNC ID uniquely identifies a base station; RADIUS indicates the measured delay from the terminal to the base station, and thus the distance is found; RSCP indicates that the terminal is in the The signal strength of the point.
  • the measurement parameters of base stations such as 30104, 32924, 34465, and 33876 are selected as reference; each base station collects about 1000 wireless measurement parameters.
  • the regression parameters can be estimated based on least squares, gradient descent, etc., but gradient descent can be implemented using preset step size recursion compared to least squares.
  • the following describes how to use the gradient descent method to estimate the regression parameters of the distance estimation model based on the above method for determining the location of the terminal to improve the system optimization effect.
  • a linear regression model is coded in Python to prove the feasibility of the method for determining the position of the terminal according to an embodiment of the present disclosure.
  • the effect diagram of the distance between the terminal and the base station obtained based on the measurement parameters of the base station of 30104 based on the method for determining the position of the terminal according to the embodiment of the present disclosure is shown in FIG. 3, the horizontal axis represents the signal strength RSCP, and the vertical axis represents the distance between the terminal and the base station. It can be seen from FIG. 3 that the data points are mainly distributed in the signal strength range of 20 to 60 dB, that is, the terminal with the signal strength in the range of 20 to 60 dB can be located.
  • the linear regression fitting diagram of the base station is shown in Fig. 4, and the fitting error is: 164.4403052879.
  • the horizontal axis represents the number of sample points
  • the vertical axis represents the loss function error. Among them, curve A represents the estimated value calculated by the linear regression distance estimation model, and curve B represents the actual measured value.
  • the method for determining the position of the terminal based on the embodiment of the present disclosure is based on the measurement parameters of the base station 32924.
  • the effect diagram of the distance between the terminal and the base station is shown in FIG. 5, and the linear regression fitting diagram of the base station is shown in FIG. 6.
  • the error is 76.500434572.
  • the method for determining the position of the terminal based on the embodiment of the present disclosure is based on the measurement parameters of the base station of 34465.
  • the effect diagram of the distance between the terminal and the base station is shown in FIG. 7, and the linear regression fitting of the base station is shown in FIG. 8. It is 97.8078453185.
  • the method for determining the position of the terminal based on the embodiment of the present disclosure is based on the measurement parameters of the base station of 33876 base station.
  • the effect diagram of the distance between the terminal and the base station is shown in FIG. 9, and the linear regression fitting of the base station is shown in FIG. 10.
  • the fitting error It is 353.987710441.
  • the fitting error between the constructed distance estimation model and the measured value is between 50 and 400. It can be considered that the distance estimation model is close to the real measurement value. Therefore, the distance estimation model can be It is used to calculate the distance estimation in the positioning of the terminal. However, for scenes with large fitting error values, further optimization is needed.
  • Figure 3 From the relationship between 30104, 32924 and 34465 base stations, Figure 3, Figure 5 and Figure 7 show that as the signal strength increases, the distance between the terminal and the base station shows a decentralized downward trend.
  • the distance estimation model based on the gradient descent method has a linear regression function that converges to fit the relationship between the base station signal strength and the distance.
  • the distance estimation model Based on the above base station measurement data, combined with the distance estimation model to train the regression parameters, and verifying and analyzing the error between the distance value obtained from the distance estimation model and the actual measured value, it is known that the distance value obtained from the distance estimation model has a higher accuracy.
  • Fig. 11 is a block diagram of an apparatus for determining a location of a terminal according to an exemplary embodiment.
  • the apparatus 110 includes: an acquiring module 111, configured to acquire the measurement of each base station from a positioning measurement report of the terminal Parameters; input module 112, used to input the measurement parameters corresponding to each base station into the distance estimation model of each base station, respectively, to obtain multiple estimates of the distance between each base station and the terminal, the distance estimation model is based on the base station The historical measurement parameters and the measured distance between the base station and the terminal are obtained by training; the determination module 113 is configured to determine the position of the terminal according to multiple estimated values of the distance between each base station and the terminal.
  • the device for determining the location of the terminal may further include: a building module, configured to use the historical measurement parameters of the base station before inputting the measurement parameters corresponding to the base stations into the distance estimation models of the base stations respectively Establish a linear regression relationship with the linear relationship between the measurement distance between the base station and the terminal, wherein, in the relationship, each historical measurement parameter corresponds to a linear parameter; a calculation module is used to The value of each historical measurement parameter and the value of the measurement distance calculate the value of each linear parameter to obtain a distance estimation model of the base station.
  • a building module configured to use the historical measurement parameters of the base station before inputting the measurement parameters corresponding to the base stations into the distance estimation models of the base stations respectively Establish a linear regression relationship with the linear relationship between the measurement distance between the base station and the terminal, wherein, in the relationship, each historical measurement parameter corresponds to a linear parameter
  • a calculation module is used to The value of each historical measurement parameter and the value of the measurement distance calculate the value of each linear parameter to obtain a distance estimation model of the base station.
  • the calculation module may be used to obtain the optimal value of each linear parameter by a gradient descent method.
  • the historical measurement parameter may include at least one of the following: the signal strength of the base station received by the terminal, the interference of the signal strength received by the terminal, and the communication path loss.
  • the acquisition module may be used to: according to the RNC ID or CELL ID in the measurement report, combine the measurement parameters of sectors with the same longitude and latitude as the measurement parameters of the same base station.
  • Fig. 12 is a block diagram of a device for determining a location of a terminal according to an exemplary embodiment.
  • the device 1200 may be provided as a server.
  • the device 1200 includes a processor 1222, the number of which may be one or more, and a memory 1232 for storing a computer program executable by the processor 1222.
  • the computer program stored in the memory 1232 may include one or more modules each corresponding to a set of instructions.
  • the processor 1222 may be configured to execute the computer program to perform the above-described method of determining the location of the terminal.
  • the device 1200 may further include a power supply component 1226 and a communication component 1250, which may be configured to perform power management of the device 1200, and the communication component 1250 may be configured to implement communication of the device 1200, for example, wired or wireless communication .
  • the device 1200 may also include an input / output (I / O) interface 1258.
  • the device 1200 can operate an operating system based on the memory 1232, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, and so on.
  • a non-transitory computer-readable storage medium including program instructions for example, a memory 1232 including program instructions
  • the program instructions may be executed by the processor 1222 of the device 1200 to complete the above Method to determine the location of the terminal.
  • the beneficial effects of the present disclosure are as follows:
  • the method for determining the location of a terminal calculates the distance between the terminal and each base station based on the distance estimation model obtained by pre-training corresponding to each base station, and then determines the distance between the terminal and each base station Positioning the terminal improves the positioning accuracy of the terminal.

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Mobile Radio Communication Systems (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

本公开提供一种确定终端位置的方法、装置及存储介质，用以解决相关技术对终端进行定位的定位精度较低对的问题。该方法包括：从终端的定位测量报告中获取各基站的测量参数；将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。

Description

确定终端位置的方法、装置及存储介质
本公开要求享有2018年11月23日提交的名称为“确定终端位置的方法、装置及存储介质”的中国专利申请CN201811407465.3的优先权，其全部内容通过引用并入本文中。
技术领域
本公开涉及通信技术领域，特别是涉及一种确定终端位置的方法、装置及存储介质。
背景技术
目前，在WCDMA(Wideband Code Division Multiple Access，宽带码分多址)网络下的基站定位流程通常包括如下处理：
步骤一：SP(Service Provider，服务提供商)发起定位请求，携带需要定位的目标终端的信息；步骤二：GMLC(Gateway Mobile Location Center，网关移动定位中心)对请求消息鉴权等，触发定位请求到核心网；步骤三：核心网接收到定位请求后，进行路由判断，然后向SAS(Stand-Alone SMLC(Service Mobile Locating Center，服务移动定位中心))发送定位请求(POSITION INITIATION REQUEST)；步骤四：SAS接收到定位请求后，触发测量请求(POSITION ACTIVATION REQUEST)；步骤五：SAS接收测量报告(POSITION ACTIVATION RESPONSE)；允许多次执行测量流程时，可重复触发测量请求以及接收测量报告的步骤。步骤六：SAS接收到测量报告后，进行定位估算终端位置，最后把估算的位置结果返回给POSITION INITIATION RESPONSE(位置起始响应)；GMLC收到定位结果后，响应给SP，定位流程结束。
上述定位方式仅基于单个基站的测量信息进行定位，定位精度较低，仅可作为最基础的定位能力使用。
发明内容
本公开提供一种确定终端位置的方法、装置及存储介质，用以解决相关技术中对终端进行定位精度较低的问题。
根据本公开的第一个方面，提供了一种确定终端位置的方法，包括：从终端的定位测量报告中获取各基站的测量参数；将各基站对应的所述测量参数分别输入各基站的估距模 型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
根据本公开的第二个方面，提供了一种确定终端位置的装置，包括：获取模块，用于从终端的定位测量报告中获取各基站的测量参数；输入模块，用于将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；确定模块，用于根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
根据本公开的第三个方面，提供了一种确定终端位置的装置，包括：处理器；用于存储处理器可执行指令的存储器；当所述指令被处理器执行时，执行如下操作：从终端的定位测量报告中获取各基站的测量参数；将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
根据本公开的第四个方面，提供了一种非临时性计算机可读存储介质，当所述存储介质中的指令由处理器执行时，使得处理器能够执行根据本公开第一个方面所述的方法。
附图说明
图1是根据一示例性实施例示出的一种确定终端位置的方法的流程图；
图2是根据一示例性实施例示出的一种确定终端位置的方法的流程图；
图3是根据一示例性实施例示出的基于30104基站的测量参数得到的终端与基站的距离的效果图；
图4是根据一示例性实施例示出的30104基站的线性回归拟合图；
图5是根据一示例性实施例示出的基于32924基站的测量参数得到的终端与基站的距离的效果图；
图6是根据一示例性实施例示出的32924基站的线性回归拟合图；
图7是根据一示例性实施例示出的基于34465基站的测量参数得到的终端与基站的距离的效果图；
图8是根据一示例性实施例示出的34465基站的线性回归拟合图；
图9是根据一示例性实施例示出的基于33876基站的测量参数得到的终端与基站的距离的效果图；
图10是根据一示例性实施例示出的33876基站的线性回归拟合图；
图11是根据一示例性实施例示出的一种确定终端位置的装置的框图。
图12是根据一示例性实施例示出的一种确定终端位置的装置的框图。
具体实施方式
下面将结合本公开实施例中的附图，对本公开的实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本公开一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其他实施例，都属于本公开保护的范围。
图1是根据一示例性实施例示出的一种确定终端位置的方法的流程图，如图1所示，该方法包括如下步骤：
步骤101：从终端的定位测量报告中获取各基站的测量参数；
其中，定位测量报告可以是终端测量得到的，该测量报告也称Cell-ID Measured Results Info List，该测量报告是测量终端所在的服务小区及邻小区的基站的信息，该测量报告中可包括多个基站的测量参数，该测量参数例如为3G基站的无线测量参数，可根据相应的通信协议，解析测量得到的各基站的字段，解析得到的测量参数可包括：RSCP(Received Signal Code Power，接收信号码功率)、RNC(Radio Network Controller，无线网络控制器)ID、CELL ID(基站中的小区的标识)、RTT(Round-Trip Time，往返时间)以及RTT Diffence(差异)中的一种或多种参数。
步骤102：将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；
其中，估距模型例如可以是一个线性回归模型，该模型可以基于大数据概念寻找同一基站的多个历史测量参数间的组合参数，近似的模拟因变量与变量之间的关系来建立该模型。假设已知测量参数RSCP、Ec/N0、Pathloss和测量距离(基站与终端之间的测量距离)，运用线性回归模型，计算线性组合参数(即在关系式中各测量参数的对应系数)，求出最优线性组合参数，从而得出该基站的线性回归模型。其中，RSCP表示终端接收到基站的信号强度，Ec/N0表示所接收到的信号强度的干扰水平，Pathloss表示路损，也称通讯线路损失。此外，为了提高估距模型计算出的距离的精度，可基于多次测量得到测量报告中的测量参数建立估距模型，以及，可不断使用后续采集到的测量参数对该模型进行更新。
步骤103：根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
在步骤103中，可采用三角定位算法基于各基站与终端之间的距离求解计算终端的位置信息。因此，参与定位计算的基站到终端的测量距离的准确性直接影响定位精度。但由 于3G网络的特性，无法直接获取基站与终端之间的距离，仅可通过信号强度RSCP等参数间接进行计算。因此，在运用线性回归模型准确地估算出基站与终端之间的距离的基础上，可有效的提升定位算法的定位精度。
在一种可实现方式中，可预先建立上述估距模型，基于此，确定终端位置的方法还可包括：在将各基站对应的所述测量参数分别输入各基站的估距模型之前，以基站的各项历史测量参数与基站和终端之间的测量距离之间的线性关系建立线性回归关系式，其中，在所述关系式中，所述各项历史测量参数分别对应一个线性参数；根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线性参数的值，得到基站的估距模型。其中，关系式中的所有线性参数也称组合参数。所述历史测量参数至少可包括以下一种：终端接收到的基站的信号强度、终端所接收到的信号强度的干扰以及通讯路径损失。这些参数可通过读入话单，从话单中获取，例如，话单中可包括基站RNCID、CELLID、RSCP、RTT以及RTTDiffence等参数，对各基站的测量参数的有效性进行检查，当某一基站的测量参数有误时，确定该基站的测量参数为无效参数，该参数不参与估距模型的训练过程，基于得到的各基站的测量参数训练各基站对应的估距模型，同时，在估距模型的使用过程中，可根据后续采集到的各基站的测量参数对该基站对应的估距模型进行更新，以进一步提高估距模型的精度。
以下通过一个例子对建立估距模型的过程进行说明，在该例子中，基站与终端之间的距离由多个测量参数决定，如RSCP、Ec/N0以及Pathloss等。因此，根据距离的三个决定因素(1，RSCP，Ec/N0，Pathloss)建立线性回归模型，并且各测量参数的影响权重为(θ 0，θ R，θ E，θ P)。
线性回归函数构建如下：
h(θ)＝θ 0+θ Rx R+θ Ex E+θ Px P+...＝θ Tx；
上式中，h(θ)为基站与终端之间的距离，x为(x R、x E、x P…x n) T列向量，可表示回归函数的影响因素，例如，x R、x E、x P可依次为上述RSCP、Ec/N0以及Pathloss，其中，θ T可为(θ 0，θ R，θ E，θ P)行向量，表示各影响因素的权重系数。
损失函数构建如下：
Figure PCTCN2019119975-appb-000001
其中，x (i)表示向量x中的第i个元素；y (i)表示向量y中的第i个元素；h θ(x (i)表示已知的假设函数；m为训练集的数量。利用该损失函数，可计算通过上述线性回归函数求得的距离的偏差。
上述线性回归模型中的线性回归参数的估算过程可包括如下处理：
上述线性回归模型中，可以用参数a表示θ，用参数b表示(xR、xE、xP…xn)。参数 a与b都是未知数，需要根据样本数据(x i，y i)计算。确定参数a与b值的原则，是要使得样本的回归直线同观察值的拟合状态最好，即要使得估计得到的终端与基站之间的距离值与实测值的偏差最小。为此，可以采用最小二乘法寻求最优参数。
在基于最小二乘法寻求最优参数时，对应于每一个x i，根据回归直线方程，可以求出一个y i，它就是y i的一个估计值。估计值和实测值之间的偏差
Figure PCTCN2019119975-appb-000002
有n个实测值就有相应的n个偏差。要使线性回归模型的拟合状态最好，需使n个偏差的总和最小。为了便于计算，可以以误差的平方和最小为标准来确定线性回归模型。此时需求如下Q值：
Figure PCTCN2019119975-appb-000003
Q是个极小值，根据微积分中的极值定理，要使上式取极值，其对a与b所求的偏导数应为0，从而可求出上述各线性参数。
在一种可实现方式中，根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线性参数的值可包括：通过梯度下降法求出所述各线性参数的最优值。其中，梯度下降法是一个最优化算法，通常也称为最速下降法。最速下降法用负梯度方向为搜索方向，最速下降法越接近目标值，步长越小，前进越慢。在上述确定终端的位置的方法中，在最小化损失函数时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数，和模型参数值。举例说明，在基于梯度下降法寻求最优参数时，可包括如下处理：确定向下一步的步伐大小，称为Learning rate：α；任意给定一个初始值：(θ 1，θ 2，…θ n-1，θ n)；确定一个向下的方向，并向下进行预先规定的步伐，并更新(θ 1，θ 2，…θ n-1，θ n)；当下降的高度小于某个定义的值，则停止下降；在该算法中：步长为α；梯度下降迭代：
Figure PCTCN2019119975-appb-000004
进而得到θ i：＝θ i-α[h θ(x)-y]x (i)；执行上述迭代方程小于设定阈值时，可得到最优参数。
本公开实施例的确定终端位置的方法，根据各基站对应的预先训练得到的估距模型计算终端与各基站之间的距离，进而根据终端与各基站之间的距离对终端进行定位，提高了终端的定位精度。
在一种可实现方式中，由于同一个基站下可以部署多个扇区，故可合并在同一基站下部署的不同扇区获取的测量参数作为该基站的测量参数，基于此，所述从终端的定位测量报告中获取各基站的测量参数可包括：根据所述测量报告中的RNC ID或小区标识CELL ID，将经度以及纬度相同的扇区的测量参数合并，作为同一基站的测量参数。
以下结合图2对基于上述确定终端的位置的方法对终端的位置进行确定的流程进行示例性说明，在图2所示的方法中，使用的测量参数以RSCP为例，如图2所示，该流程可包括：
获取测量报告，测量报告中可能包括有多个基站的测量参数。根据相应的协议，解析各基站的各个测量参数的字段可获得：RSCP/RNCID/CELLID/RTT/RTTDiffence等参数。
根据从测量报告中得到的基站RNCID/CELLID，查询各个基站的经纬度，将经纬度相同的扇区对应的测量参数合并为一个基站的测量参数，作为该基站的测量参数上述定位计算。
根据测量报告中各个基站测得的信号强度RSCP值，查询训练库，得到训练估距值S，该训练库中可包括历史测量参数，以及基于历史参数计算得到的终端与基站之间的距离至，还可包括上述估距模型。在查询训练库时，可利用估距模型计算得到终端与基站之间的距离。在该步骤中，对于测量报告中的各基站都需要计算得到一个估距值Sn。
对估距Sn的合法性进行检查，例如，可根据基站经度、纬度与估距Sn，校验估距值的有效性，通过校验，无效估距的基站不参与上述定位计算；
利用三角定位计算，估算终端的位置；
输出估算出的终端的位置结果。
以下通过一个例子来对上述确定终端的位置的方法的进行进一步说明。
在该例子中，训练估距模型所利用的训练数据(上述历史测量参数)来源于对一3G基站的实测参数，在该例子中，估距模型主要以RSCP与测量距离之间的关系构建。采集的数据的格式例如(CELLID，RNCID，RADIUS，RSCP)，其中CELL ID/RNC ID唯一标识一个基站；RADIUS表示终端到基站测量到的时延，从而求出的距离；RSCP表示该终端在该点的信号强度。训练数据例如选取30104、32924、34465以及33876等基站的测量参数为参考；每个基站采集约有1000条无线测量参数。
上文阐述了回归参数可基于最小二乘法、梯度下降法等进行估计，但梯度下降法较最小二乘法可采用预设步长递推实现。以下阐述基于上述确定终端的位置的方法中采用梯度下降法估算上述估距模型的回归参数对***的优化效果提升。
在以现场数据为基础，通过Python编码线性回归模型，来证明本公开实施例的确定终端的位置的方法的可实施性。
基于本公开实施例的确定终端的位置的方法基于30104基站的测量参数得到的终端与基站的距离的效果图如图3所示，横轴表示信号强度RSCP，纵轴表示终端与基站的距离。从图3可以看出，数据点主要分布在信号强度20～60dB区间，即可对信号强度在20～60dB区间的终端进行定位。该基站的线性回归拟合图如图4所示，拟合误差为：164.403052879。图4中横轴表示样本点数，纵轴表示损失函数误差。其中，曲线A表示线性回归估距模型计算的预估值，曲线B表示实测值。
基于本公开实施例的确定终端的位置的方法基于32924基站的测量参数得到的终端与 基站的距离的效果图如图5所示，该基站的线性回归拟合图如图6所示，拟合误差为76.500434572。
基于本公开实施例的确定终端的位置的方法基于34465基站的测量参数得到的终端与基站的距离的效果图如图7所示，该基站的线性回归拟合如图8所示，拟合误差为97.8078453185。
基于本公开实施例的确定终端的位置的方法基于33876基站的测量参数得到的终端与基站的距离的效果图如图9所示，该基站的线性回归拟合如图10所示，拟合误差为353.987710441。
在图3、图5、图7以及图9中，构建的估距模型与实测值的拟合误差在50～400之间，可以认为估距模型接近真实测量值，因此，该估距模型可以用于对终端的定位中的估距计算。但是拟合误差值较大的场景，还需要做进一步优化。
在图4、图6、图8以及图10中，随着信号强度的增长，终端到基站的距离均呈现下降趋势。
从30104、32924和34465基站的关系图3、图5以及图7来看，随着信号强度的增长，终端与基站的距离呈现分散下降趋势。
从33876基站与RSCP关系图图10来看，随着信号强度的增长，终端与基站的距离呈现集中下降趋势。
从图4、图6以及8与图10相比较，随着信号强度的增长，对于终端与基站的距离关系来说，分散下降较集中下降拟合出的线性回归函数更接近实测值。
根据上述理论分析，基于梯度下降法的估距模型存在收敛的线性回归函数拟合基站信号强度与距离的关系。
基于上述基站测量数据，结合估距模型训练回归参数，并验证分析基于估距模型得到的距离值与实测值的误差，可知基于估距模型得到的距离值的精度较高。
图11是根据一示例性实施例示出的一种确定终端位置的装置的框图，如图11所示，该装置110包括：获取模块111，用于从终端的定位测量报告中获取各基站的测量参数；输入模块112，用于将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；确定模块113，用于根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
在一种可实现方式中，确定终端位置的装置还可包括：建立模块，用于在将各基站对应的所述测量参数分别输入各基站的估距模型之前，以基站的各项历史测量参数与基站和终端之间的测量距离之间的线性关系建立线性回归关系式，其中，在所述关系式中，所述 各项历史测量参数分别对应一个线性参数；计算模块，用于根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线性参数的值，得到基站的估距模型。
在一种可实现方式中，所述计算模块可用于：通过梯度下降法求出所述各线性参数的最优值。
在一种可实现方式中，所述历史测量参数至少可包括以下一种：终端接收到的基站的信号强度、终端所接收到的信号强度的干扰以及通讯路径损失。
在一种可实现方式中，所述获取模块可用于：根据所述测量报告中的RNC ID或CELL ID，将经度以及纬度相同的扇区的测量参数合并，作为同一基站的测量参数。
图12是根据一示例性实施例示出的一种确定终端位置的装置的框图。例如，装置1200可以被提供为一服务器。参照图12，装置1200包括处理器1222，其数量可以为一个或多个，以及存储器1232，用于存储可由处理器1222执行的计算机程序。存储器1232中存储的计算机程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理器1222可以被配置为执行该计算机程序，以执行上述的确定终端位置的方法。
另外，装置1200还可以包括电源组件1226和通信组件1250，该电源组件1226可以被配置为执行装置1200的电源管理，该通信组件1250可以被配置为实现装置1200的通信，例如，有线或无线通信。此外，该装置1200还可以包括输入/输出(I/O)接口1258。装置1200可以操作基于存储在存储器1232的操作***，例如Windows ServerTM，Mac OS XTM，UnixTM，LinuxTM等等。
在另一示例性实施例中，还提供了一种包括程序指令的非临时性计算机可读存储介质，例如包括程序指令的存储器1232，上述程序指令可由装置1200的处理器1222执行以完成上述的确定终端的位置的方法。
本公开有益效果如下：本公开实施例的确定终端位置的方法，根据各基站对应的预先训练得到的估距模型计算终端与各基站之间的距离，进而根据终端与各基站之间的距离对终端进行定位，提高了终端的定位精度。
尽管为示例目的，已经公开了本公开的优选实施例，本领域的技术人员将意识到各种改进、增加和取代也是可能的，因此，本公开的范围应当不限于上述实施例。

Claims (12)

  1. 一种确定终端位置的方法，其中，包括：
    从终端的定位测量报告中获取各基站的测量参数；
    将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；
    根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
  2. 根据权利要求1所述的方法，其中，所述方法还包括：
    在将各基站对应的所述测量参数分别输入各基站的估距模型之前，以基站的各项历史测量参数与基站和终端之间的测量距离之间的线性关系建立线性回归关系式，其中，在所述关系式中，所述各项历史测量参数分别对应一个线性参数；
    根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线性参数的值，得到基站的估距模型。
  3. 根据权利要求2所述的方法，其中，所述根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线性参数的值，包括：
    通过梯度下降法求出所述各线性参数的最优值。
  4. 根据权利要求1所述的方法，其中，所述历史测量参数至少包括以下一种：
    终端接收到的基站的信号强度、终端所接收到的信号强度的干扰以及通讯路径损失。
  5. 根据权利要求1至4任一项所述的方法，其中，所述从终端的定位测量报告中获取各基站的测量参数，包括：
    根据所述测量报告中的无线网络控制器的标识RNC ID或小区标识CELL ID，将经度以及纬度相同的扇区的测量参数合并，作为同一基站的测量参数。
  6. 一种确定终端位置的装置，其中，包括：
    获取模块，用于从终端的定位测量报告中获取各基站的测量参数；
    输入模块，用于将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；
    确定模块，用于根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
  7. 根据权利要求6所述的装置，其中，所述装置还包括：
    建立模块，用于在将各基站对应的所述测量参数分别输入各基站的估距模型之前，以基站的各项历史测量参数与基站和终端之间的测量距离之间的线性关系建立线性回归关系式，其中，在所述关系式中，所述各项历史测量参数分别对应一个线性参数；
    计算模块，用于根据所述各项历史测量参数的值以及所述测量距离的值计算所述各线 性参数的值，得到基站的估距模型。
  8. 根据权利要求7所述的装置，其中，所述计算模块用于：
    通过梯度下降法求出所述各线性参数的最优值。
  9. 根据权利要求6所述的装置，其中，所述历史测量参数至少包括以下一种：
    终端接收到的基站的信号强度、终端所接收到的信号强度的干扰以及通讯路径损失。
  10. 根据权利要求6至9任一项所述的装置，其中，所述获取模块用于：
    根据所述测量报告中的无线网络控制器的标识RNC ID或小区标识CELL ID，将经度以及纬度相同的扇区的测量参数合并，作为同一基站的测量参数。
  11. 一种确定终端位置的装置，其中，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    当所述指令被处理器执行时，执行如下操作：
    从终端的定位测量报告中获取各基站的测量参数；
    将各基站对应的所述测量参数分别输入各基站的估距模型，得到各基站与所述终端之间的距离的多个估计值，所述估距模型基于基站历史测量参数以及基站与终端之间测量距离进行训练得到；
    根据各基站与所述终端之间的距离的多个估计值确定所述终端的位置。
  12. 一种非临时性计算机可读存储介质，当所述存储介质中的指令由处理器执行时，使得处理器能够执行根据权利要求1至5任一项所述的方法。
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