CN108174383B - Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system - Google Patents

Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system Download PDF

Info

Publication number
CN108174383B
CN108174383B CN201711327497.8A CN201711327497A CN108174383B CN 108174383 B CN108174383 B CN 108174383B CN 201711327497 A CN201711327497 A CN 201711327497A CN 108174383 B CN108174383 B CN 108174383B
Authority
CN
China
Prior art keywords
signal
monitoring
matrix
stations
vector
Prior art date
Legal status (The legal status 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 status listed.)
Active
Application number
CN201711327497.8A
Other languages
Chinese (zh)
Other versions
CN108174383A (en
Inventor
朱天枢
张竟枢
李斗
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Peking University
Original Assignee
Peking University
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 Peking University filed Critical Peking University
Priority to CN201711327497.8A priority Critical patent/CN108174383B/en
Publication of CN108174383A publication Critical patent/CN108174383A/en
Application granted granted Critical
Publication of CN108174383B publication Critical patent/CN108174383B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a monitoring station deployment method and a blind source separation method of a gridding radio signal monitoring system. The monitoring station deployment method comprises the following steps: four base stations and four monitoring stations are arranged in each monitoring area of the gridding radio signal monitoring system; the base stations and the monitoring stations in the same monitoring area are located on or approximately located on the same circle, the base stations are distributed at equal intervals, and the monitoring stations are distributed symmetrically on two horizontal and vertical symmetric axes of a square with the four base stations as vertexes. The invention can efficiently and real-timely detect the abnormal signal source of the pseudo base station and is convenient to remove in time.

Description

Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system
Technical Field
The invention belongs to the technical field of digital communication, and particularly relates to a deployment method and a blind source separation method of a monitoring station and a base station in a gridding radio signal monitoring system.
Background
The development of 5G technology makes future mobile communication have the trend of ultra-dense networking. In the existing radio signal monitoring system, monitoring stations are sparsely distributed, and received monitoring signals are only signals sent by a high-power base station and are not suitable for the development requirements of future mobile communication.
A gridding radio signal monitoring system is a system which divides a monitoring area into grids according to the distribution condition of wireless communication base stations and adopts a miniature monitoring station with small size and low power consumption to monitor. The system has the advantages that the signal information received by each monitoring station can be collected, sorted and comprehensively analyzed, more accurate and comprehensive monitoring is realized, meanwhile, the interference of abnormal signals is accurately detected, and the system is in accordance with the development trend of the future mobile communication ultra-dense networking.
In a gridding radio signal monitoring system, a monitoring station receives mixed superposition of signals sent by a plurality of signal sources (base stations), and the key problems of the monitoring station are as follows: the problem of blind source separation is how to effectively separate signals sent by signal sources on the premise that only mixed signals received by a monitoring station are known.
The Independent Component Analysis (ICA) method is a classical approach to the blind source separation problem. Based on the mutual independence and non-Gaussian property of each signal source, the method searches the direction with the maximum non-Gaussian property from a random vector, then iterates until the direction converges, and considers that the signal source is a certain signal source needing to be separated. In practical application, how to find out abnormal signal sources such as a pseudo base station as soon as possible in time for removing the abnormal signal sources is an important problem to be solved urgently.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention aims to provide a monitoring station deployment method and a blind source separation method of a gridding radio signal monitoring system. Which comprises the following steps: the method has the advantages that due to the adoption of a proper blind source separation mode and a monitoring station distribution mode, the abnormal signal source of the pseudo base station can be detected with high efficiency and real-time performance, and timely elimination work is facilitated.
The technical scheme of the invention is as follows:
a monitoring station deployment method of a gridding radio signal monitoring system is characterized in that four base stations and four monitoring stations are arranged in each monitoring area of the gridding radio signal monitoring system; the base stations and the monitoring stations in the same monitoring area are located on or approximately located on the same circle, the base stations are distributed at equal intervals, and the monitoring stations are distributed symmetrically on two horizontal and vertical symmetric axes of a square with the four base stations as vertexes.
A blind source separation method comprising the steps of:
1) selecting signals of continuous N sample points from each monitoring station in the same monitoring area of the gridding radio signal monitoring system to form an M multiplied by N matrix; wherein M is the total number of monitoring stations in the same monitoring area of the gridding radio signal monitoring system; subtracting the row signal mean value from each signal in each row in the M multiplied by N matrix to obtain a matrix x, and whitening the matrix x; the signal of the sample point is a signal which is received by the monitoring station and is obtained by superposing signals sent by various signal sources;
2) randomly selecting a vector w with the length of N; according to the formula w ═ E { z (w)Tz)3-3w } calculating a vector w'; wherein E is CxThe unit norm eigenvectors are used as a matrix of columns, CxA covariance matrix which is a matrix x; z is the matrix whitened by the matrix x, vector wTIs a transpose of the vector w;
3) determining whether the vector w is converged according to a comparison result of the vector w' and the vector w, and if so, separating a signal source from the currently monitored signal source; if not, assigning w' to w, and returning to the step 2) to perform iterative calculation until w converges;
4) if p signal sources s have been previously isolated1,…,spThen the currently separated signal source s is usedp+1Then p signal sources s are separated according to the separation1,…,spFor signal source sp+1Carrying out quasi-orthogonalization; then returning to the step 2), if M +1 signal sources are separated, performing the step 5);
5) and comparing the separated signal of each signal source with the corresponding historical signal, and judging the abnormal signal source.
Further, the total number M of monitoring stations in the same monitoring area takes the value of 4; the total number of normal signal sources in the same monitoring area is 4; the normal signal sources and the monitoring stations in the same monitoring area are located on or approximately located on the same circle, the normal signal sources are distributed at equal intervals, and the monitoring stations are distributed on two symmetrical axes which are vertical and horizontal and are of a square with four normal signal sources as vertexes and are symmetrically distributed.
Further, using the formula z ═ D-1/2ETx whitening the matrix x; wherein D is a covariance matrix C with xx＝E(xxT) Each eigenvalue of (a) is a diagonal matrix of diagonal elements, and E is CxThe unit norm eigenvectors serve as a matrix of columns.
Further in accordance with
Figure BDA0001505947210000021
For signal source sp+1Carrying out quasi-orthogonalization; wherein s isjFor the jth separated signal source,
Figure BDA0001505947210000022
for the inner product of these two vectors, α ∈ [0,1]]Are quasi-orthogonal coefficients.
Further, the value range of the quasi-orthogonal coefficient alpha is 0.5-0.7.
Further, in the step 5), if the signal of one signal source is compared with the historical normal data, the signal is judged to be an abnormal signal source if the signal exceeds the upper and lower limit set range.
Compared with the prior art, the invention has the following positive effects:
the prior art is difficult to monitor abnormal signals mixed in normal radio signals, and the technology can achieve the detection rate of more than 90% of the abnormal signals under the condition that the false alarm probability does not exceed 3.5%.
Drawings
Fig. 1 is a division of the monitoring area in a typical scenario, where each circular area contains 4 base stations (five-pointed star), referred to as a monitoring area.
Fig. 2 shows the positions where monitoring stations should be located in each of the divided monitoring areas.
Fig. 3 is a flow chart of blind source separation in the present invention.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
In the exemplary scenario shown in fig. 1, the five-pointed star is the base station location. In consideration of the geographical distribution of city blocks and the ultra-dense networking mode of future mobile communication, 4 base stations are proposed as a group to divide a gridding monitoring area. Meanwhile, the results of the simulation tests also show that the detection success rate of abnormal signals is the highest by taking 4 base stations as a group. Therefore, as shown in fig. 1, 4 base stations contained in each circle are grouped, and 4 monitoring stations are arranged in the area of each group of base stations.
Within each monitoring area (a circular area containing 4 base stations as shown in fig. 1), simulations were performed based on the different relative positions of the monitoring stations and the base stations. The results show that the relative position for the best effect should be as shown in fig. 2: the 4 base stations represented by the pentagram form a square, and the monitoring stations represented by the triangle are arranged on two symmetrical axes in the horizontal and vertical directions of the square and are symmetrically distributed. The monitoring station and the base station should be located on approximately the same circle.
The signals received by the monitoring station are obtained by attenuating and superposing signals sent by all base stations in a monitoring area through different channels. During the monitoring process, a sample of the power level signal of the monitoring station is taken at an interval of one hour (e.g., 5-10 seconds), called a sample point. As shown in fig. 3, for a monitoring area, after 1500-2000 sample points of all monitoring stations are generally acquired, blind source separation can be performed on signals received by the monitoring stations according to the following steps:
1. there are M monitoring stations. And selecting signals of continuous N sample points of each monitoring station to form an M multiplied by N matrix. And subtracting the mean value of each row to finish centralization to obtain a matrix x, and whitening the matrix x according to the formula (1):
z＝D-1/2ETx (1)
wherein D is a covariance matrix C of matrix xx＝E(xxT) Each eigenvalue of (a) is a diagonal matrix of diagonal elements, E is CxThe unit norm eigenvectors are used as a matrix of columns, matrix xTA transposed matrix of x.
2. Selecting a random vector w with the length of N, and calculating according to the formula (2):
w′＝E{z(wTz)3-3w} (2)
wherein wTIs a transposed vector of w. After obtaining w ', normalizing the w' to change the norm (modulus) into unit norm (modulus);
3. if w' is almost the same as the vector w (converges), then a signal source s is considered to have been isolated; if the difference is still left, assigning w' to w, and returning to the step 2 to perform iterative calculation until w converges.
4. Suppose that p signal sources s have been separated out1,…,spWhen an s is separated againp+1Then, the quasi-orthogonalization is performed according to the formula (3):
Figure BDA0001505947210000041
wherein alpha is equal to 0,1 and is called as quasi-orthogonal coefficient, and is generally 0.5-0.7.
5. And returning to the step 2 until M +1 signal sources are separated. The separated signal sources do not have a certain sequence corresponding relation;
6. in the separated signal sources, compared with historical normal data, the abnormal signals are judged to appear if the abnormal signals exceed the upper and lower boundaries to a certain degree;
7. if the abnormal signal source characteristics exist, the abnormality is successfully found, and if the abnormal signal source characteristics do not exist, the abnormality is judged to appear, and a false alarm appears.
The foregoing description of the preferred embodiments of the present invention has been included to describe the features of the invention in detail, and is not intended to limit the inventive concepts to the particular forms of the embodiments described, as other modifications and variations within the spirit of the inventive concepts will be protected by this patent. The subject matter of the present disclosure is defined by the claims, not the detailed description of the embodiments.

Claims (6)

1. A monitoring station deployment and blind source separation method of a gridding radio signal monitoring system is characterized in that four base stations and four monitoring stations are arranged in each monitoring area of the gridding radio signal monitoring system; the base stations and the monitoring stations in the same monitoring area are positioned on or approximately positioned on the same circle, the base stations are distributed at equal intervals, and the monitoring stations are distributed on the horizontal and vertical symmetrical axes of a square with the four base stations as vertexes and are symmetrically distributed; the blind source separation method comprises the following steps: 1) selecting signals of continuous N sample points from each monitoring station in the same monitoring area of the gridding radio signal monitoring system to form an M multiplied by N matrix; wherein M is the total number of monitoring stations in the same monitoring area of the gridding radio signal monitoring system; subtracting the row signal mean value from each signal in each row in the M multiplied by N matrix to obtain a matrix x, and whitening the matrix x; the signal of the sample point is a signal which is received by the monitoring station and is obtained by superposing signals sent by various signal sources; 2) randomly selecting a vector w with the length of N; according to the formula w ═ E { z (w)Tz)3-3w } calculating a vector w'; wherein E is CxUnit norm eigenvectors as column componentsMatrix of CxA covariance matrix which is a matrix x; z is the matrix whitened by the matrix x, vector wTIs a transpose of the vector w; 3) determining whether the vector w is converged according to a comparison result of the vector w' and the vector w, and if so, separating a signal source from the currently monitored signal source; if not, assigning w' to w, and returning to the step 2) to perform iterative calculation until w converges; 4) if p signal sources s have been previously isolated1,…,spThen p signal sources s are separated according to the separation1,…,spFor currently separated signal source sp+1Carrying out quasi-orthogonalization; then returning to the step 2), if M +1 signal sources are separated, performing the step 5); 5) and comparing the separated signal of each signal source with the corresponding historical signal, and judging the abnormal signal source.
2. The method of claim 1, wherein the total number M of monitoring stations in the same monitoring area takes a value of 4; the total number of normal signal sources in the same monitoring area is 4; the normal signal sources and the monitoring stations in the same monitoring area are located on or approximately located on the same circle, the normal signal sources are distributed at equal intervals, and the monitoring stations are distributed on two symmetrical axes which are vertical and horizontal and are of a square with four normal signal sources as vertexes and are symmetrically distributed.
3. The method of claim 1, wherein the formula z-D is used-1/2ETx whitening the matrix x; wherein D is a covariance matrix C with xx＝E(xxT) Each eigenvalue of (a) is a diagonal matrix of diagonal elements, and E is CxThe unit norm eigenvectors serve as a matrix of columns.
4. A method according to claim 1, 2 or 3, characterised in that it is based on
Figure FDA0002576754100000011
For signal source sp+1Carrying out quasi-orthogonalization; wherein s isjIs the jthThe separated signal source is used for providing a signal source,
Figure FDA0002576754100000012
for the inner product of these two vectors, α ∈ [0,1]]Are quasi-orthogonal coefficients.
5. The method of claim 4, wherein the quasi-orthogonal coefficient α ranges from 0.5 to 0.7.
6. The method as claimed in claim 1, wherein in step 5), if the signal of a signal source is compared with its historical normal data, it is determined as an abnormal signal source if the signal exceeds the upper and lower limits.
CN201711327497.8A 2017-12-13 2017-12-13 Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system Active CN108174383B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711327497.8A CN108174383B (en) 2017-12-13 2017-12-13 Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711327497.8A CN108174383B (en) 2017-12-13 2017-12-13 Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system

Publications (2)

Publication Number Publication Date
CN108174383A CN108174383A (en) 2018-06-15
CN108174383B true CN108174383B (en) 2020-11-20

Family

ID=62525791

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711327497.8A Active CN108174383B (en) 2017-12-13 2017-12-13 Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system

Country Status (1)

Country Link
CN (1) CN108174383B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110784887B (en) * 2018-07-31 2021-01-19 北京大学 Method for detecting number of abnormal signal sources in gridding radio signal monitoring system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105721071A (en) * 2015-05-28 2016-06-29 中国电子科技集团公司第四十一研究所 Gridding based radio monitoring device management method
CN105871485A (en) * 2016-05-18 2016-08-17 成都福兰特电子技术股份有限公司 Gridding centralized type aviation interference monitoring system and method thereof

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7236460B2 (en) * 2002-03-29 2007-06-26 Airmagnet, Inc. Detecting a counterfeit access point in a wireless local area network
WO2005002116A2 (en) * 2003-06-13 2005-01-06 Docomo Communications Laboratories Usa, Inc. Proxy active scan for wireless networks
CN103987052B (en) * 2013-02-07 2017-11-17 华为技术有限公司 Base station collocation method and base station, telegon
CN105530660A (en) * 2015-12-15 2016-04-27 厦门大学 Channel modeling method and device based on principal component analysis
CN106358292B (en) * 2016-11-04 2020-04-07 中国人民解放军理工大学 Time arrival pattern matching and positioning method based on principle component analysis principle

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105721071A (en) * 2015-05-28 2016-06-29 中国电子科技集团公司第四十一研究所 Gridding based radio monitoring device management method
CN105871485A (en) * 2016-05-18 2016-08-17 成都福兰特电子技术股份有限公司 Gridding centralized type aviation interference monitoring system and method thereof

Also Published As

Publication number Publication date
CN108174383A (en) 2018-06-15

Similar Documents

Publication Publication Date Title
CN110166991B (en) Method, device, apparatus and storage medium for locating electronic device
CN107316048A (en) Point cloud classifications method and device
CN105701798B (en) The data reduction method and device of columnar object
Saad et al. CapsPhase: Capsule neural network for seismic phase classification and picking
CN106792465A (en) A kind of indoor fingerprint map constructing method based on mass-rent fingerprint
JP2000092556A (en) Method for identifying geographic position of pattern recognition base
CN108337645B (en) Gridding radio signal monitoring system architecture and interference signal joint detection method
CN110224771B (en) Spectrum sensing method and device based on BP neural network and information geometry
CN107071788B (en) Spectrum sensing method and device in cognitive wireless network
CN110932807B (en) Spectrum sensing method and system for MIMO (multiple input multiple output) system under non-Gaussian noise
CN110460401B (en) Cooperative spectrum sensing method based on matrix decomposition and particle swarm optimization clustering
CN102663740B (en) SAR image change detection method based on image cutting
CN111352087A (en) Passive MIMO radar multi-target positioning method based on DBSCAN
López Locating some types of random errors in digital terrain models
Lara-Cueva et al. On the use of multi-class support vector machines for classification of seismic signals at Cotopaxi volcano
CN108174383B (en) Monitoring station deployment method and blind source separation method of gridding radio signal monitoring system
CN102073867A (en) Sorting method and device for remote sensing images
Sharifi-Tehrani et al. Efficient GNSS jamming mitigation using the Marcenko Pastur law and Karhunen–Loeve decomposition
CN115826042A (en) Edge-cloud-combined distributed seismic data processing method and device
CN106162529B (en) Indoor positioning method and device
CN110245692A (en) A kind of hierarchy clustering method for Ensemble Numerical Weather Prediction member
CN108574927B (en) Mobile terminal positioning method and device
CN113271539A (en) Indoor target positioning method based on improved CNN model
CN110784887B (en) Method for detecting number of abnormal signal sources in gridding radio signal monitoring system
US20230206423A1 (en) Method, apparatus, electronic device, and storage medium for determining defect shape of wafer

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant