WO2019072062A1 - 一种基于5g信号的doa指纹库定位方法 - Google Patents

一种基于5g信号的doa指纹库定位方法 Download PDF

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WO2019072062A1
WO2019072062A1 PCT/CN2018/104919 CN2018104919W WO2019072062A1 WO 2019072062 A1 WO2019072062 A1 WO 2019072062A1 CN 2018104919 W CN2018104919 W CN 2018104919W WO 2019072062 A1 WO2019072062 A1 WO 2019072062A1
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target
angle information
positioning
fingerprint database
cell
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PCT/CN2018/104919
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English (en)
French (fr)
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张在琛
李月朝
吴亮
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东南大学
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Priority to US16/755,595 priority Critical patent/US11089563B2/en
Publication of WO2019072062A1 publication Critical patent/WO2019072062A1/zh

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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
    • 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
    • G01S5/0252—Radio frequency fingerprinting
    • 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
    • G01S5/0273—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 using multipath or indirect path propagation signals in position determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information

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  • the invention belongs to the field of positioning technology, and particularly relates to a DOA fingerprint library positioning method based on a 5G signal.
  • Traditional positioning technologies mainly include outdoor GPS positioning technology, wireless network sensor positioning technology, sonar underwater positioning technology, radar positioning technology and cellular network base station positioning technology, while indoor positioning includes Wi-Fi indoor positioning technology, Bluetooth indoor positioning technology. And ultra-wideband indoor positioning technology.
  • GPS Global Positioning System
  • Other positioning methods are mainly based on Received Signal Strength Indication (RSSI), based on Time of Arrival (TOA), based on Time Difference of Arrival (TDOA). , based on the Signal Angle of Arrival (DOA) and joint positioning method. In the NLOS environment, these positioning technologies are very poorly positioned and even unable to locate.
  • the location of the existing fingerprint database is mostly based on the RSSI location fingerprint method.
  • the fingerprint database is constructed by using the difference of signal strengths at different nodes, and then the fingerprint matching algorithm such as K nearest neighbor (KNN) is used for positioning. Since the RSSI of the reference position is to be measured point by point, the method takes a lot of manpower and time in the stage of offline fingerprint collection, which results in low application efficiency.
  • KNN K nearest neighbor
  • the object of the present invention is to solve the deficiencies in the prior art and provide a DOA fingerprint library positioning method based on 5G signals.
  • a method for positioning a DOA fingerprint database based on a 5G signal includes the following steps:
  • step (1) the original area is first divided into a plurality of micro cells, and a reference point is set in each micro cell, and then the data received by the base station antenna array is calculated by the ESPRIT method to obtain each micro cell. Estimated angle information of the reference point in the middle.
  • step (2) the estimated angle information of each micro cell needs to be in one-to-one correspondence with the reference point and the actual location information corresponding to the cell; and the estimated angle information in the fingerprint database is updated every fixed time.
  • step (3) when there is a target in the positioning area, the data received by the base station antenna array is calculated by the MUSIC method or the ESPRIT method, thereby estimating the angle information of the target in the area.
  • the specific method of the step (4) is: matching the angle information of the target estimation in the step (3) with the estimated angle information in the fingerprint database, and searching for the best matching estimated angle information in the fingerprint database, and obtaining the best result.
  • the cell corresponding to the matched point is the location where the target is now, that is, the location where the target is located.
  • the angle value in the fingerprint database is updated in real time according to the current environment and channel conditions.
  • the angle between the estimated angle and the fingerprint database can be matched without considering any channel information. Therefore, the scheme can perform accurate positioning in a complicated channel environment, and is applicable to a wide range of applications.
  • the scheme is divided into cells by region, and the target point is located in the cell by using angle matching. When the divided cells are small enough, high-precision positioning can be achieved.
  • the fingerprint library location of the present invention is less affected by channel conditions, and can achieve accurate positioning in a poor channel condition and NLOS environment. At the same time, it solves the positioning problem in the NLOS environment, and has high positioning accuracy and applicability. A wide range of advantages.
  • FIG. 1 is a layout view of a positioning area of a fingerprint library in Embodiment 1;
  • FIG. 2 is a schematic diagram of a signal received by an antenna array in Embodiment 1;
  • FIG. 3 is a layout view of a positioning area of a fingerprint library in Embodiment 2;
  • Embodiment 4 is a positioning target positioning diagram in Embodiment 2.
  • Figure 5 is a block diagram of the sub-array of the ESPRIT algorithm.
  • the traditional positioning technology is greatly affected by the environment, channel conditions, and the presence or absence of the direct path.
  • the positioning accuracy is poor or even impossible to locate in the poor channel conditions and NLOS environment.
  • the invention divides the original area into a plurality of micro cells, uses an algorithm to estimate the angle information of the reference points in the divided cells, and then saves the angle information of each cell reference point and the location information of the corresponding cell in the fingerprint database. And updating the angle information in the fingerprint library at a fixed time; so that when there is a target in the area, by determining the angle information of the target, and then matching with the angle information in the fingerprint database, determining the cell where the target is located, You can get the location information of the target to achieve the target's positioning.
  • the required location area is divided into N ⁇ N cells, and a reference point is set in each cell (in practice, the number of reference points can be set according to requirements), according to the reference point.
  • the positions are numbered 1, 2, 3..., N ⁇ N and assigned different ID numbers, and the reference points of different ID numbers are transmitted at different times, as shown in Fig. 1. (The slot interval is greater than the maximum arrival time)
  • the location information of each reference point and cell is entered into the fingerprint database according to the number.
  • One or more sets of antenna arrays are set at the boundary of the positioning area, and the wideband OFDM signals transmitted from the reference points of the respective cells are sequentially received, and each signal is used by using the ESPRIT algorithm or the MUSIC algorithm.
  • the subcarrier signals (narrowbands) used for positioning are processed, and the corresponding angle information of each reference point is estimated, as shown in FIG. 2.
  • the reference point is correspondingly matched with the estimated angle and the coordinates.
  • all the estimated angle information is entered into the fingerprint database, and the estimated information in the fingerprint database is updated at regular intervals according to actual conditions.
  • the wideband OFDM signal transmitted by the target is located in the area, and after receiving the antenna array, the subcarrier signal is processed by the ESPRIT algorithm or the MUSIC algorithm, and all the estimated angle information of the target and corresponding are obtained. Signal strength information.
  • the estimated angle information is matched with the angle information in the fingerprint database. Due to the complexity of the environment, there may be multiple estimation angles. When correlation matching is performed, different weights a1, a2 are set according to the signal strength information of each angle. ...an. By selecting the reference point with the largest correlation with the target angle by correlation matching, you can know the area where the target is located and locate the target.
  • the positioning area is first divided into N ⁇ N cells, and a reference point is set in each cell (in practice, the number of reference points can be set according to requirements), according to the reference point
  • the positions are numbered 1, 2, 3..., N ⁇ N and assigned different IDs, and different numbers are transmitted at different times, as shown in FIG. (The slot interval is greater than the maximum arrival time)
  • the location information of each reference point and cell is entered into the fingerprint database according to the number.
  • each base station has a set of antenna arrays, which in turn receive wideband OFDM signals transmitted from reference points of respective cells, and use ESPRIT algorithm or MUSIC algorithm for each signal.
  • the subcarrier signals (narrowbands) used for positioning are processed, and all angle information of each corresponding reference point is estimated, as shown in FIG.
  • the virtual coordinate information of the anchor point can be obtained by solving the following equation.
  • the reference point is in one-to-one correspondence with the coordinates, and all the virtual coordinate information is entered into the fingerprint database, and the estimated information in the fingerprint database is updated at regular intervals according to actual conditions.
  • the wideband OFDM signal transmitted by the target is located in the area, and after receiving the antenna array, the subcarrier signal is processed by using the ESPRIT algorithm or the MUSIC algorithm, and all the estimated angle information of the target and corresponding are obtained. Signal strength information.
  • the virtual position information of the target is calculated by the method in step (3).
  • the estimated virtual location information is matched with the virtual location information in the fingerprint database. Due to the complexity of the environment, there may be multiple virtual coordinates. When the correlation is matched, the signal strength information of each virtual coordinate is set differently. Weights a1, a2...an. By correlating the reference points that have the greatest correlation with the target virtual coordinates, the target area can be known and the target can be located.
  • the DOA fingerprint library positioning of the present invention utilizes a large-scale MIMO antenna array and an algorithm to directly estimate the signal direction angle, and the information of the unused signal strength (in the NLOS environment, is not affected by the channel environment). And using the intersection of two or more directional angles, accurately calculate the position of the target, and build a fingerprint library.
  • Both sub-array X 1 and sub-array X 2 comprise N-1 array elements, wherein sub-array X 1 is composed of the first N-1 array elements of the ULA array, and sub-array X 2 is composed of N-1 array elements after the ULA array.
  • the sub-array X 1 is shifted to the right by one array element spacing to obtain the sub-array X 2 .
  • the two sub-arrays have translation invariance, and the signal subspace corresponding to the two sub-arrays has rotation invariance. This relationship is expressed by the formula as
  • the steering vector B Since the steering vector B is equal to the signal space, it must exist and there is only one non-singular transformation matrix T such that the formula (5) is established.
  • the signal feature subspaces E 1 and E 2 are obtained by the eigenvalue decomposition of the autocovariance matrix R yy , and then the eigenvalue decomposition is obtained by the formula (9) to obtain the angle of arrival parameters as shown below.

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

Abstract

本发明公开一种基于5G信号的DOA指纹库定位方法，包括以下步骤：将初始区域划分成多个微型小区，并对划分的微型小区中的参考点的角度信息进行估计；然后将各个微型小区参考点的角度信息和相应小区的位置信息保存在指纹库中，每隔固定的时间对指纹库中的角度信息进行更新；使得当有目标在该区域时，通过估计目标的角度信息；与指纹库中的角度信息进行匹配，确定该目标所在的小区，即可得到目标的位置信息，从而实现目标的定位。

Description

一种基于5G信号的DOA指纹库定位方法 技术领域
本发明属于定位技术领域，具体涉及一种基于5G信号的DOA指纹库定位方法。
背景技术
传统的定位技术主要有室外的GPS定位技术，无线网络传感器定位技术，声呐水下定位技术，雷达定位技术以及蜂窝网络基站定位技术等，而室内定位包括Wi-Fi室内定位技术，蓝牙室内定位技术以及超宽带室内定位技术等。其中，全球定位***(Global Positioning System，GPS)显著提高了定位效率，降低了定位误差，然而该***的配套设备非常昂贵。其他的定位方法主要分为基于接收信号强度定位法(Received Signal Strength Indication,RSSI)，基于信号到达时间定位法(Time of Arrival,TOA)，基于信号到达时间差定位法(Time Difference of Arrival,TDOA)，基于信号到达角度定位法(Direction of Arrival,DOA)以及联合定位法等。在NLOS环境下，这些定位技术定位效果非常差甚至无法定位。
现有的指纹库的定位大多是基于RSSI位置指纹方法的，利用在不同节点的信号强度的差异构建指纹库，然后通过指纹匹配算法如K近邻算法(K nearest neighbor,KNN)进行定位。由于要逐点测量参考位置的RSSI，因此该方法在离线采集指纹的阶段会耗费大量人力和时间，导致其应用效率不高。
随着4G技术成功部署进行商用，5G技术也将在2020年左右投 入商用，基站的天线数量也将会继续增加，因此基于到达角的定位方法的定位精度也会大幅度提高。然而，在复杂的环境下尤其在NLOS环境下，基于到达角的定位技术定位精度会很差甚至无法定位，而在基于指纹库的定位可以很好的解决NLOS环境下的定位问题。
发明内容
发明目的：本发明的目的在于解决现有技术中存在的不足，提供一种基于5G信号的DOA指纹库定位方法。
技术方案：本发明所述的一种基于5G信号的DOA指纹库定位方法，包括以下步骤：
(1)将初始区域划分成N*N个大小相等的微型小区，选定每个小区的中心点为参考点，并对划分的微型小区中的参考点的角度信息进行估计；
(2)然后将各个微型小区参考点的角度信息和相应小区的位置信息保存在指纹库中，每隔固定的时间对指纹库中的角度信息进行更新；
(3)使得当有目标在该区域时，通过估计目标的角度信息；
(4)与指纹库中的角度信息进行匹配，确定该目标所在的小区，即可得到目标的位置信息，从而实现目标的定位。
进一步的，步骤(1)中首先将原本的区域划分成很多微型小区，并在每个微型小区中都设置参考点，接着通过ESPRIT法对基站天线阵列接收的数据进行计算，得到每个微型小区中参考点的估计角度信 息。
进一步的，步骤(2)中需将每个微型小区的估计角度信息与参考点和小区相应的的实际位置信息一一对应；每隔固定的时间对指纹库中的估计角度信息进行更新。
进一步的，步骤(3)当有目标在定位区域的时候，接着通过MUSIC法或者ESPRIT法对基站天线阵列接收的数据进行计算，进而估计出目标在该区域的角度信息。
进一步的，步骤(4)的具体方法为：将步骤(3)目标估计的角度信息与指纹库中的估计角度信息进行匹配，在指纹库中寻找最佳匹配的估计角度信息，得到的最佳匹配的点所对应的小区便是目标现在所处的位置，即定位出目标的位置。
有益效果：与现有技术相比，本发明具有以下优点：
1、指纹库中的角度值是根据当前的环境与信道状况进行实时更新的，在定位时，对估计角度和指纹库中的角度进行匹配即可，不考虑任何信道信息。因此，该方案可以在复杂的信道环境下进行准确的定位，适用的场合广泛。
2、在NLOS环境下，由于不存在直达径，传统的定位方案都无法进行较好的定位，而该方案是利用对当前目标角度与指纹库中的已知角度进行匹配的方法，因此不需要考虑是否存在直达径的问题，可以在NLOS环境下进行准确的定位。
3、该方案是通过区域划分成小区，利用角度匹配将目标点定位在小区内，当划分的小区足够小的时候，可以实现高精度的定位。
综上所述，本发明的指纹库定位受信道状况的影响较小，能够在信道状况较差和NLOS环境下实现精确的定位；同时解决NLOS环境下的定位问题，具有定位精度高、适用性广泛等优点。
附图说明
图1是实施例1中指纹库的定位区域布置图；
图2是实施例1中天线阵列接收信号示意图；
图3是实施例2中指纹库的定位区域布置图；
图4是实施例2中定位目标定位图；
图5是ESPRIT算法子阵列分块示意图。
具体实施方式
下面对本发明技术方案进行详细说明，但是本发明的保护范围不局限于所述实施例。
传统定位技术受环境、信道状况、以及有无直达径的影响较大，在信道状况较差和NLOS环境下定位精度很差甚至无法定位。而本发明通过将原本的区域划分成很多微型小区，利用算法对划分的小区中的参考点进行角度信息进行估计，然后将各个小区参考点的角度信息和相应小区的位置信息保存在指纹库中，每隔固定的时间对指纹库中的角度信息进行更新；使得当有目标在该区域时，通过估计目标的角度信息，然后与指纹库中的角度信息进行匹配，确定该目标所在的小区，即可得到目标的位置信息，从而实现目标的定位。
本发明主要针对于5G的DOA指纹库定位，是一种基于5G基站的定位方法，利用5G中的大规模MIMO的天线阵列来进行DOA(Direction  of Arrival)估计，从而建立指纹库，主要面向的对象是手机等移动终端。
下面结合附图以及相应实施例对各个步骤进行更详尽的说明。
实施例1：
(1)在指纹库的构建模式，首先将需要定位区域划分成N×N个小区，并在每个小区中均设置一个参考点(实际中可以按照需求设置参考点个数)，按照参考点的位置进行编号1,2,3…，N×N并赋予不同的ID编号，不同的ID编号的参考点发射的时间不同，如图1所示。(时隙间隔大于最大到达时间)将每个参考点和小区的位置信息按照编号录入指纹库中。
(2)在定位区域边界处设置一组或多组天线阵列(本实例中采用一组)，依次接受来自各个小区参考点发射的宽带OFDM信号，并利用ESPRIT算法或者MUSIC算法对每个信号中的用来定位的子载波信号(窄带)进行处理，估计得到相应的每个参考点的角度信息，如图2所示。根据时序和编号将参考点与估计角度、坐标一一对应，按照参考点的编号，将所有估计角度信息录入指纹库中，根据实际情况每隔一段时间对指纹库中的估计信息进行更新。
(3)在定位模式时，区域内定位目标发射的宽带OFDM信号，天线阵列接收到后，利用ESPRIT算法或者MUSIC算法对定位的子载波信号进行处理，估计得到目标的所有的估计角度信息和相应的信号强度信息。
(4)最终将估计角度信息与指纹库中的角度信息进行相关性匹 配，由于环境复杂可能存在多个估计角度，在做相关性匹配时，根据各个角度信号强度信息设置不同的权重a1，a2…an。通过相关性匹配选出与目标角度相关性最大的参考点，即可知道目标所在的区域，对目标进行定位。
实施例2：
(1)在指纹库的构建时，首先将定位区域划分成N×N个小区，并在每个小区中均设置一个参考点(实际中可以按照需求设置参考点个数)，按照参考点的位置进行编号1,2,3…，N×N并赋予不同的ID，不同的编号发射的时间不同，如图3所示。(时隙间隔大于最大到达时间)将每个参考点和小区的位置信息按照编号录入指纹库中。
(2)在定位区域边界处设置两个基站A，B，其中每个基站都有一组天线阵列，依次接受来自各个小区参考点发射的宽带OFDM信号，并利用ESPRIT算法或者MUSIC算法对每个信号中的用来定位的子载波信号(窄带)进行处理，估计得到相应的每个参考点的所有角度信息，如图4所示。
(3)根据基站的位置信息和定位区域的物理信息，设定位点的坐标(x，y)，基站A(0,0)为坐标原点，基站B的坐标为(0，d)，可以得到基站AB的距离为d，基站A的估计角为∝(∝ 1，∝ 2...，∝ N×N)，基站B的估计角为β(β 1，β 2...，β N×N)，通过解如下的方程即可得到定位点的虚拟坐标信息。
Figure PCTCN2018104919-appb-000001
根据定位区域的编号及ID将参考点与坐标一一对应，将所有虚拟坐标信息录入指纹库中，根据实际情况每隔一段时间对指纹库中的估计信息进行更新。
(4)在定位模式时，区域内定位目标发射的宽带OFDM信号，天线阵列接收到后，利用ESPRIT算法或者MUSIC算法对定位的子载波信号进行处理，估计得到目标的所有的估计角度信息和相应的信号强度信息。利用步骤(3)中的方法，将目标的虚拟位置信息计算出来。
(5)最终将估计虚拟位置信息与指纹库中的虚拟位置信息进行相关性匹配，由于环境复杂可能存在多个虚拟坐标，在做相关性匹配时，根据各个虚拟坐标的信号强度信息设置不同的权重a1，a2…an。通过相关性匹配选出与目标虚拟坐标相关性最大的参考点，即可知道目标所在的区域，对目标进行定位。
本发明的DOA指纹库定位是利用大规模MIMO天线阵列与算法直接估计出信号方向角度，未使用信号强度的信息(NLOS环境下，不受信道环境的影响)。并且利用两个或者多个方向角度的相交，精确计算出目标的位置，构建指纹库。
上述过程中，所有的角度信息都通过ESPRIT法对基站天线阵列接收的数据进行计算得到，ESPRIT算法通过矩阵平移得到信号空间旋转不变的原理来估计到达角。ULA阵列分块如图6所示。
子阵列X 1和子阵列X 2都包含N-1个阵元，其中子阵列X 1由ULA阵列前N-1个阵元组成，子阵列X 2由ULA阵列后N-1个阵元组成。子阵列X 1往右平移一个阵元间距即得子阵 列X 2，两子阵列存在平移不变性，对应两子阵列的信号子空间存在旋转不变性，此关系通过公式表达为
Figure PCTCN2018104919-appb-000002
其中
Figure PCTCN2018104919-appb-000003
Figure PCTCN2018104919-appb-000004
可见到达角信息隐藏在矩阵Φ的特征值中，得到Φ的特征值就能估计出到达角参数。将公式(1)方程组改写为矩阵形式可得
Figure PCTCN2018104919-appb-000005
推出接收信号矩阵y(t)的自协方差矩阵R yy为
Figure PCTCN2018104919-appb-000006
由于导向矢量B与信号空间等秩，所以必定存在且只存在一个非奇异的变换矩阵T使得公式(5)成立
Figure PCTCN2018104919-appb-000007
则矩阵E 1和E 2具有如下关系
E 2＝BΦT＝E 1T -1ΦT＝E 1Ψ       公式(7)
其中，
Ψ＝T -1ΦT       公式(8)
由上面分析可以知晓，矩阵Ψ与矩阵Φ具有相同的特征值，可得
Figure PCTCN2018104919-appb-000008
通过自协方差矩阵R yy特征值分解得到信号特征子空间E 1和E 2，再由公式(9)得到Ψ后对其进行特征值分解即可得到到达角参数如下所示
Figure PCTCN2018104919-appb-000009

Claims (5)

  1. 一种基于5G信号的DOA指纹库定位方法，其特征在于：包括以下步骤：
    (1)将初始区域划分成N*N个大小相等的微型小区，选定每个小区的中心点为参考点，并对划分的微型小区中的参考点的角度信息进行估计；
    (2)然后将各个微型小区参考点的角度信息和相应小区的位置信息保存在指纹库中，每隔固定的时间对指纹库中的角度信息进行更新；
    (3)使得当有目标在该区域时，通过估计目标的角度信息；
    (4)与指纹库中的角度信息进行匹配，确定该目标所在的小区，即可得到目标的位置信息，从而实现目标的定位。
  2. 根据权利要求1所述的基于5G信号的DOA指纹库定位方法，其特征在于：所述步骤(1)中首先将原本的区域划分成很多微型小区，并在每个微型小区中都设置参考点，接着通过MUSIC法或者ESPRIT法对基站天线阵列接收的数据进行计算，得到每个微型小区中参考点的估计角度信息。
  3. 根据权利要求1所述的基于5G信号的DOA指纹库定位方法，其特征在于：所述步骤(2)中需将每个微型小区的参考点估计角度信息与参考点和小区相应的的实际位置信息一一对应；每隔固定的时间对指纹库中的估计角度信息进行更新。
  4. 根据权利要求1所述的基于5G信号的DOA指纹库定位方法，其特 征在于：所述步骤(3)当有目标在定位区域的时候，接着通过ESPRIT法对基站天线阵列接收的数据进行计算，进而估计出目标在该区域的角度信息。
  5. 根据权利要求1所述的基于5G信号的DOA指纹库定位方法，其特征在于：所述步骤(4)的具体方法为：将步骤(3)目标估计的角度信息与指纹库中的估计角度信息进行匹配，在指纹库中寻找最佳匹配的估计角度信息，得到的最佳匹配的点所对应的小区便是目标现在所处的位置，即定位出目标的位置。
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