CN113759392A - Robust GNSS interference source positioning method based on flight big data - Google Patents

Robust GNSS interference source positioning method based on flight big data Download PDF

Info

Publication number
CN113759392A
CN113759392A CN202110959579.4A CN202110959579A CN113759392A CN 113759392 A CN113759392 A CN 113759392A CN 202110959579 A CN202110959579 A CN 202110959579A CN 113759392 A CN113759392 A CN 113759392A
Authority
CN
China
Prior art keywords
interference source
gnss
cost function
interfered
interference
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.)
Granted
Application number
CN202110959579.4A
Other languages
Chinese (zh)
Other versions
CN113759392B (en
Inventor
吴仁彪
王一鸣
王晓亮
何炜琨
王文益
贾琼琼
胡铁乔
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.)
Civil Aviation University of China
Original Assignee
Civil Aviation University of China
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 Civil Aviation University of China filed Critical Civil Aviation University of China
Priority to CN202110959579.4A priority Critical patent/CN113759392B/en
Publication of CN113759392A publication Critical patent/CN113759392A/en
Application granted granted Critical
Publication of CN113759392B publication Critical patent/CN113759392B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

A robust GNSS interference source positioning method based on flight big data comprises the steps of constructing a cost function according to characteristics of arrival power of a QAR or ADS-B data in the flight big data and interference initial positions of the interference big data, grouping the interference tracks to construct an optimized cost function which is more consistent with actual conditions under the condition that different aircraft GNSS receiver performances are considered to be different, and calculating the position of an interference source through the minimized cost function. The GNSS interference source positioning method provided by the invention can still achieve higher positioning accuracy under the condition that the performances of airborne GNSS receiving equipment adopted by different aircrafts are different.

Description

Robust GNSS interference source positioning method based on flight big data
Technical Field
The invention belongs to the field of positioning of Global Navigation Satellite System (GNSS) interference sources, and particularly relates to a GNSS interference source positioning method using interfered tracks in the field.
Background
The GNSS signal arrives at the receiver very weakly, 20dB lower than the noise of the receiver, and is very susceptible to the influence of the interference signal. In recent years, the interference of the GNSS signals of civil aviation aircrafts is on the rise, the interference of the GNSS signals of civil aviation is a great threat to the running safety of civil aviation, and the investigation of the GNSS interference is also a difficult problem which puzzles the civil aviation.
The existing ground interference investigation technology has limited search range, is easily influenced by shielding of ground buildings, has low investigation efficiency and low accuracy, and can find the interference source when the interference source is close to the interference source. The technical trend of utilizing space-based flight big data, utilizing interfered tracks to carry out GNSS interference source positioning and interference source investigation is potential.
The method for obtaining the interfered track data by using the data of the Broadcast Automatic Dependent Surveillance Broadcast (ADS-B) abroad and positioning the interference source mainly comprises the following steps: (1) the european EUROCONTROL researchers proposed a method for estimating the location Of an interference source by using a thermodynamic diagram to represent probability based on the assumption Of Power Difference Of Arrival (PDOA), which requires a predetermined determination Of the reacquisition-to-loss-Of-lock Power ratio Of a GNSS receiver, and the principle Of calculating the thermodynamic diagram is not reasonable. (2) The researchers at the university Of stanford GPS laboratory propose a method for estimating the location Of an interference source by using a convex optimization method based on the assumption Of Power Of Arrival (POA). Because the GNSS receivers of different models have differences, the above two methods do not consider the differences of the GNSS receivers on board different airplanes, which greatly affects the estimation performance of the interference source position.
Disclosure of Invention
The invention aims to provide a robust GNSS interference source positioning method based on flight big data so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the robust GNSS interference source positioning method based on flight big data comprises the following steps:
firstly, obtaining all interfered tracks in a certain range and three-dimensional coordinates of the interference starting position of each interfered track by using Quick Access Recorder (QAR) data or ADS-B data in big flight data.
And step two, screening the data, and grouping according to differences of GNSS receivers carried by the interfered flights.
And step three, constructing a cost function according to the characteristic that the distance from the interference initial position of the same group of interfered tracks to the interference source is the same, and estimating the position of the interference source by minimizing the cost function.
Preferably, the method for differentially grouping the GNSS receivers carried by the interfered flights includes converting the corresponding relation between the flight path and the airplane type into the corresponding relation between the flight path and the airborne GNSS receiver according to the prior information of the model of the GNSS receiver carried by different airplane types and the corresponding relation between the airborne GNSS receiver and the airplane type, and grouping the flight paths with the same GNSS receiver into one group. When the model information of different types of loaded GNSS receivers cannot be obtained, the types of the loaded GNSS receivers are directly grouped, and the tracks of the same type are grouped into one group.
Preferably, the cost function is designed according to the following points:
(1) the distances from the starting position of the interfered flight path to the interference source in the same group are nearly equal and are all close to the same theoretical distance rt(ii) a The theoretical distances from the starting positions of different groups of interfered tracks to the position of the interference source differ by a multiplicative correction coefficient alpha (i); ith group j th disturbed track disturbance starting position { x1(i,j),y1(i,j),z1(i, j) } to the interference source location (x)0,y0,z0) Is alpha (i) r0；
(2) Ith group j th disturbed track disturbance starting position { x1(i,j),y1(i,j),z1(i, j) } to the interference source location (x)0,y0,z0) Is squared as the residual term of the actual distance to the theoretical distance
{[x1(i,j)-x0]2+[y1(i,j)-y0]2+[z1(i,j)-z0]2-[α(i)r0]2}2；
(3) The cost function is the sum of squares of residual error terms of actual distances from all interfered track interference initial positions to interference source positions to be analyzed and theoretical distances;
(4) the cost function includes (x)0,y0,z0)、r0A (i) a number of variables to be optimized.
The cost function designed is:
Figure BDA0003224973050000021
wherein the i-th group has J in the interfered trackiAnd (I) 1,2, … and I, wherein the I group has the interfered track.
Preferably, in step three, when a priori information about the performance of the GNSS receiver is unknown, the variable (x) is solved0,y0,z0)、r0α (i) an optimization problem that minimizes the cost function.
Preferably, in step three, when the prior information of the performance of the GNSS receiver can be obtained, α (i) is determined in advance by using the prior information of the performance of the GNSS receiver, and then the variable (x) is solved0,y0,z0)、r0To the optimization problem of (2).
Preferably, when solving the optimization problem, a grid method is used for searching extreme points of the cost function, and the (x) corresponding to the minimum value of the cost function is taken0,y0,z0) The method provides an estimate of the location of the source of the interferer.
Compared with the prior art, the invention has the following beneficial effects:
the difference of the airborne GNSS receiver is considered, the model is closer to the real situation, and the interference source positioning algorithm still has better positioning performance and is more robust when the difference exists in the airplane airborne GNSS receiver corresponding to the analyzed interfered track.
The objects, features, and advantages of the present invention can be described in detail by the following drawings and examples.
Drawings
FIG. 1 is a flowchart illustrating a method for positioning a robust GNSS interference source based on big flight data according to the present invention;
FIG. 2 is a schematic diagram of an interference source positioning model established by the present invention;
FIG. 3 is a schematic diagram of the positioning effect of simulation data;
fig. 4 is a comparison of the interference source localization results obtained using the method of the present invention on a set of simulation data with other methods.
Detailed Description
The robust gnss interference source positioning method based on big flight data provided by the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the robust gnss interference source positioning method based on big flight data provided by the present invention includes the following steps performed in sequence:
step one, obtaining an interfered track:
after the airborne GNSS receiver is interfered, the interference is reflected in corresponding data items of QAR data and ADS-B data. And obtaining all interfered tracks in the range of the attention area and the longitude and latitude and the height of the interference starting position of each interfered track by using the QAR data or the ADS-B data, and converting the longitude and latitude and the height into three-dimensional coordinates under a rectangular coordinate system.
Step two, interfered track screening and grouping:
(1) disturbed track screening
The disturbed tracks that are too short and the disturbed tracks with start and stop positions near the airport are screened out.
(2) Disturbed track grouping
The interfered tracks are divided into a plurality of groups according to the information of different types of carried GNSS receivers, the interfered tracks with the same type of the GNSS receivers are divided into the same group, and the interfered tracks are divided into different groups according to different types of the GNSS receivers. When the model information of different types of GNSS receivers carried by the different types of GNSS receivers cannot be obtained, the different types of GNSS receivers can be directly grouped according to the types of the GNSS receivers, interfered tracks of the same type are divided into the same group, and interfered tracks of different types are divided into different groups.
Assuming that N interfered tracks are provided after screening, the N interfered tracks comprise a group I, and the number of the interfered tracks in the group I is Ji(I ═ 1,2, …, I), assuming that the total data set after grouping is represented by the symbol TsortRepresents, i.e.:
Tsort＝{[t(1,1),…,t(1,J1)],…,[t(I,1),…,t(I,JI)]} (2)
wherein t (i, J) (J ═ 1,2, …, Ji) J-th data representing i-th group and containing three-dimensional coordinates { x } of rectangular coordinate system of interference starting position1(i,j),y1(i,j),z1(i,j)}。
Constructing a cost function and solving:
(1) constructing cost functions within groups
For the ith group of interfered tracks, the coordinates of the interference starting position of each interfered track are { x }1(i,j),y1(i,j),z1(i, j) }, 1 st group interfered track interference starting position { x1(i,j),y1(i,j),z1(i, j) } to the interference source location (x)0,y0,z0) Is a theoretical distance of r0(namely the radius of an interference influence area of the interference source to the airplane in the 1 st group), the correction coefficients of the theoretical distances from the interference source position to the interference starting position of different groups of interfered tracks are α (1), α (2), …, α (I), wherein α (1) is 1. Searching for the location of the interference source (x)0,y0,z0) The intra-group cost function to be optimized is:
Figure BDA0003224973050000041
(2) constructing a total cost function
Summing the cost functions of all groups to obtain the position (x) of the searched interference source0,y0,z0) The total cost function to be optimized is:
Figure BDA0003224973050000042
(3) optimization problem structure
When constructing the optimization problem based on the POA hypothesis, the following 2 cases can be classified according to the prior information on the performance of the GNSS receiver:
(a) solving for variables (x) when a priori information about GNSS receiver performance is unknown0,y0,z0)、r0α (i) an optimization problem that minimizes the cost function. I.e. by solving
Figure BDA0003224973050000051
The location of the possible interference sources is estimated.
(b) When the prior information of the performance of the GNSS receiver can be obtained, the unknown α (i) is determined in advance by using the prior information of the performance of the GNSS receiver, and the prior information may be a receiver parameter, historical data or a prior experiment. The correction factors α (i) for the different on-board GNSS receivers now become known quantities, and the optimization problem reduces to solving for the variables (x)0,y0,z0)、r0To the optimization problem of (2). I.e. by solving
Figure BDA0003224973050000052
The location of the possible interference sources is estimated.
(4) Optimization problem solving
Taking the unknown situation of the prior information about the performance of the GNSS receiver as an example, the cost function extreme point is searched by using a grid method, and the specific steps are as follows:
step one, gridding the parameters to be optimized. Gridding division is carried out on a search area, and the grid size delta r of the position search of the interference source can be set according to the requirement of positioning precision; for searching in the height direction, the operation is simplified by limiting a search area to the earth surface by using topographic data; obtaining a group of three-dimensional close to the earth surface after griddingLocation mesh vertex { x0(k),y0(k),z0(k) (K ═ 1,2, …, K); determining a group of radius search sequences { r ] according to the preset interference radius search precision and the correction term search precision0(M) }, (M ═ 1,2, …, M) and a set of correction term search sequences { α }0(p)}，(p＝1,2,…,P)。
Step two, calculating (x) at each three-dimensional position grid vertex0(k),y0(k),z0(k) Cost function ρ of (c)POAIs measured. For the grouped 1 st group of track data, traversing the radius search sequence on the vertex of each three-dimensional position grid to enable the cost function rho in the groupPOA(1) Obtaining the minimum value on the vertex; for the ith (I-2, … I) group, all combinations of radius search sequences and correction term search sequences are traversed at each three-dimensional position grid vertex, resulting in an intra-group cost function ρPOA(i) The minimum value at the vertex is obtained. Summing the minimum values of all the intra-group cost functions to obtain the three-dimensional position grid vertex { x0(k),y0(k),z0(k) ρ of the cost function atPOAA minimum value.
Step three, obtaining the minimum cost function rho of each three-dimensional position grid vertex according to the step twoPOAFinding the minimum value, the three-dimensional position grid vertex corresponding to the minimum value
Figure BDA0003224973050000053
The method provides an estimate of the location of the source of the interferer.
The effects of the present invention can be further explained by the following simulation results.
Description of simulation data: simulation data is shown in figure 3, after the position of the interference source is set, interfered tracks of two types of the GNSS receivers are generated, and the airborne GNSS receiver of the type 1 is unlocked and interfered with the power P1Out-of-lock threshold interference power P of airborne GNSS receiver of comparator type 223dB smaller. Radius r of influence area of loss of lock caused by interference influence on model 11Set to 40km according to the relation of the receiver out-of-lock threshold power and the radius of the affected area
Figure BDA0003224973050000061
The radius r of the influence area of the unlocking caused by the interference influence of the machine type 2 can be known2And at 28.32km, adding Gaussian white noise with the average value mu of 0m and the standard deviation sigma of 300m to each interfered track interference starting position and the actual distance of the interference source on the basis of the set distance. And generating 50 simulated interfered tracks (only part of which is shown in the third figure), wherein the model 1 and the model 2 respectively comprise 25 interfered tracks.
Fig. 4 shows that, according to the positioning result of the interference source obtained from the simulation data, the grid interval Δ r during the grid search is 100 m. Contour plots of the results of the set of simulation data locations and the probability distribution of the occurrence of an interferer are shown. The error epsilon between the estimated position of the interference source and the set position of the simulation obtained by the simulation dataPOA48.14m, which is less than the maximum distance of any point in the single grid from the center point of the grid
Figure BDA0003224973050000062
The method can effectively position the position of the interference source and can bear certain data errors.
Table 1 compares the positioning results of simulation experiments under different conditions using the current stanford university method and the method. When the POA method of Stanford university estimates the position of an interference source, the POA method directly estimates the initial positions of all interfered tracks without grouping, and the cost function is recorded as:
Figure BDA0003224973050000063
wherein r is0Representing the radius of the interference source influence area, and the estimation process is represented as:
Figure BDA0003224973050000064
when a simulation experiment is carried out, interfered tracks generated when 2 different airborne GNSS receivers are interfered are generated. Respectively setting out-of-lock threshold interference of second type airborne GNSS receiverPower P2Out-of-lock threshold interference power P with first type airborne GNSS receiver1Ratio of (A to B)
Figure BDA0003224973050000065
0dB, 3dB, 5 dB. 50 monte carlo experiments were performed for each power ratio. The experimental tracks are randomly distributed along two main directions each time, and the condition that the civil aircraft flies along a fixed air route is simulated. The deviation of the actual distance from the interfered track starting position to the position of the interference source in each experiment to the theoretical distance is also randomly distributed. Interference source position x for each experiment0,y0The positions of the set interference sources are taken as centers and are randomly distributed in a range of 100m of a grid delta r. The results of the interference source location estimation of the method of the present invention compared to the university of stanford method are shown in table 1. Both methods use a grid method for solving the optimization problem. It can be seen that the method can keep the root mean square error smaller than the root mean square error under different GNSS receiver out-of-lock threshold power ratios
Figure BDA0003224973050000071
The estimation accuracy of (1) is higher than that of the Stanford POA method, and the Stanford POA method can only be kept smaller than the out-of-lock threshold power ratio of 0dB
Figure BDA0003224973050000072
The accuracy of the estimation of.
In conclusion, the method can effectively position the ground GNSS interference position, when the airborne GNSS receivers corresponding to different tracks have differences, the positioning effect of the method is better than that of the existing method, the differences among the GNSS receivers are more robust, and the method is more beneficial to estimating the position of the GNSS interference source by analyzing flight big data in actual situations, so that effective help is provided for further troubleshooting and searching for the interference source.
TABLE 1 comparison of the results of the localization of the method of the invention and the POA method of Stanford university
Figure BDA0003224973050000081

Claims (6)

1. The robust GNSS interference source positioning method based on flight big data is characterized by comprising the following steps:
analyzing QAR or ADS-B data in the flight big data to obtain all interfered tracks in a certain range and three-dimensional coordinates of the interference starting position of each interfered track;
step two, screening the data, and grouping according to differences of GNSS receivers carried by interfered flights;
and step three, constructing a cost function according to the characteristic that the distance from the interference initial position of the same group of interfered tracks to the interference source is the same, and estimating the position of the interference source by minimizing the cost function.
2. The robust GNSS interference source positioning method based on flight big data of claim 1, wherein: in the second step, the method for grouping the GNSS receivers carried by the interfered flights according to the differences comprises the steps of converting the corresponding relation between the flight path and the airplane type into the corresponding relation between the flight path and the airborne GNSS receivers according to the prior information of the types of the GNSS receivers carried by different types, and grouping the same flight paths of the GNSS receivers into a group. When prior information of different types carrying GNSS receiver models cannot be obtained, grouping is directly carried out according to the types, and tracks of the same type are grouped into one group.
3. The robust GNSS interference source positioning method based on flight big data of claim 1, wherein: in step three, the cost function is designed according to the following points:
(1) the distances from the starting position of the interfered flight path to the interference source in the same group are nearly equal and are all close to the same theoretical distance rt(ii) a The theoretical distances from the starting positions of different groups of interfered tracks to the position of the interference source differ by a multiplicative correction coefficient alpha (i); ith group j th disturbed track disturbance starting position { x1(i,j),y1(i,j),z1(i, j) } to the interference source location (x)0,y0,z0) Is a theoretical distance ofα(i)r0；
(2) Ith group j th disturbed track disturbance starting position { x1(i,j),y1(i,j),z1(i, j) } to the interference source location (x)0,y0,z0) Is { [ x ] as the square of the residual term of the actual distance and the theoretical distance1(i,j)-x0]2+[y1(i,j)-y0]2+[z1(i,j)-z0]2-[α(i)r0]2}2；
(3) The cost function is the sum of the squares of residual error terms of the actual distance from the interfered initial position of the interfered track to the position of the interference source to be analyzed and the theoretical distance;
(4) the cost function includes (x)0,y0,z0)、r0A (i) a number of variables to be optimized.
4. The robust GNSS interference source positioning method based on flight big data of claim 1, wherein: in step three, the specific method for estimating the location of the interference source by minimizing the cost function is to solve for the variable (x) when the prior information about the performance of the GNSS receiver is unknown0,y0,z0)、r0α (i) an optimization problem that minimizes the cost function.
5. The robust GNSS interference source positioning method based on flight big data of claim 1, wherein: in the third step, the specific method for estimating the position of the interference source by minimizing the cost function is that, when the prior information of the performance of the GNSS receiver can be obtained, the specific method for estimating the position of the interference source by minimizing the cost function is that firstly, α (i) is determined in advance by using the prior information of the performance of the GNSS receiver, and then, the variable (x) is solved0,y0,z0)、r0To the optimization problem of (2).
6. The specific method of estimating the location of an interferer by minimizing a cost function as recited in claim 4, wherein: solving the optimization questionsWhen the problem is solved, a grid method is used for searching extreme points of the cost function, and the (x) corresponding to the minimum value of the cost function is taken0,y0,z0) The method provides an estimate of the location of the source of the interferer.
CN202110959579.4A 2021-08-23 2021-08-23 Robust GNSS interference source positioning method based on flight big data Active CN113759392B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110959579.4A CN113759392B (en) 2021-08-23 2021-08-23 Robust GNSS interference source positioning method based on flight big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110959579.4A CN113759392B (en) 2021-08-23 2021-08-23 Robust GNSS interference source positioning method based on flight big data

Publications (2)

Publication Number Publication Date
CN113759392A true CN113759392A (en) 2021-12-07
CN113759392B CN113759392B (en) 2023-08-18

Family

ID=78790551

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110959579.4A Active CN113759392B (en) 2021-08-23 2021-08-23 Robust GNSS interference source positioning method based on flight big data

Country Status (1)

Country Link
CN (1) CN113759392B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230350074A1 (en) * 2022-04-29 2023-11-02 The Boeing Company Determining outages of a satellite navigation system and signal interference
CN117130012A (en) * 2023-10-26 2023-11-28 中国民航大学 Rough positioning method for interference source by using open-land topography shielding on undulating topography

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070009011A1 (en) * 2003-06-25 2007-01-11 Coulson Alan J Narrowband interference suppression for ofdm system
KR20100051192A (en) * 2008-11-07 2010-05-17 포항공과대학교 산학협력단 Method for mitigating anti-jam and multipath interference signal in a global navigation satellite receiver
CN102508264A (en) * 2011-11-23 2012-06-20 中国民航大学 Method for inhibiting pulse interference of satellite navigation system
US20150035699A1 (en) * 2012-11-30 2015-02-05 Korea Aerospace Research Institute Detecting and localization method of unknown signal using aircraft with ads-b system
CN104459713A (en) * 2014-12-10 2015-03-25 西安建筑科技大学 Method used for estimating direction of arrival of deception jamming through satellite navigation receiver
US20150270917A1 (en) * 2014-03-21 2015-09-24 Nvidia Corporation Estimating channel information
US20150293234A1 (en) * 2014-04-09 2015-10-15 The Mitre Corporation Positioning, Navigation, and Timing Device Interference and Spoofing Detector With Timing Mitigation
CN109541538A (en) * 2018-12-12 2019-03-29 华东理工大学 The method for realizing distribution ADS-B passive location anti-fraud function based on chance reference source
CN110463322A (en) * 2017-04-05 2019-11-15 高通股份有限公司 Channel for multi-subscriber dispatching retains
CN110988922A (en) * 2019-10-19 2020-04-10 中国电波传播研究所（中国电子科技集团公司第二十二研究所） ADS-B-based three-dimensional ellipsoid cross GNSS interference source positioning method

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070009011A1 (en) * 2003-06-25 2007-01-11 Coulson Alan J Narrowband interference suppression for ofdm system
KR20100051192A (en) * 2008-11-07 2010-05-17 포항공과대학교 산학협력단 Method for mitigating anti-jam and multipath interference signal in a global navigation satellite receiver
CN102508264A (en) * 2011-11-23 2012-06-20 中国民航大学 Method for inhibiting pulse interference of satellite navigation system
US20150035699A1 (en) * 2012-11-30 2015-02-05 Korea Aerospace Research Institute Detecting and localization method of unknown signal using aircraft with ads-b system
US20150270917A1 (en) * 2014-03-21 2015-09-24 Nvidia Corporation Estimating channel information
US20150293234A1 (en) * 2014-04-09 2015-10-15 The Mitre Corporation Positioning, Navigation, and Timing Device Interference and Spoofing Detector With Timing Mitigation
CN104459713A (en) * 2014-12-10 2015-03-25 西安建筑科技大学 Method used for estimating direction of arrival of deception jamming through satellite navigation receiver
CN110463322A (en) * 2017-04-05 2019-11-15 高通股份有限公司 Channel for multi-subscriber dispatching retains
CN109541538A (en) * 2018-12-12 2019-03-29 华东理工大学 The method for realizing distribution ADS-B passive location anti-fraud function based on chance reference source
CN110988922A (en) * 2019-10-19 2020-04-10 中国电波传播研究所（中国电子科技集团公司第二十二研究所） ADS-B-based three-dimensional ellipsoid cross GNSS interference source positioning method

Non-Patent Citations (10)

* Cited by examiner, † Cited by third party
Title
SU TING: "TDOA estimation of dual-satellites interference localization based on blind separation", 《JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS》 *
WENYI WANG: "ADS-B Signal Separation based on Blind Adaptive Beamforming", 《EEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 *
周增波;陈曦;孙振宇;李想;: "飞行模拟器导航电子干扰系统设计", 系统仿真学报, no. 08 *
庞晶;倪少杰;聂俊伟;欧钢;: "GNSS欺骗干扰技术研究", 火力与指挥控制, no. 07 *
张昊;王璐;王文益;卢丹;贾琼琼;吴仁彪;: "单天线合成阵列欺骗式干扰抑制", 信号处理, no. 03 *
梅林: "GNSS 干扰分析及抗干扰技术综述", 《监测检测》 *
熊洁;何平川;石玉;: "GSM-R加权最小二乘干扰源定位方法及性能分析", 高速铁路技术, no. 03 *
王文益;吴庆;: "利用改进型AlexNet的ADS-B欺骗式干扰检测", 信号处理, no. 05 *
范广伟;蔚保国;邓志鑫;陈沛林;: "GNSS干扰测向中的自适应阵元标校算法研究", 计算机仿真, no. 11 *
赵志民;覃德林;罗林宇;: "基于大数据精准定位干扰源的策略及应用", 广西通信技术, no. 02 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230350074A1 (en) * 2022-04-29 2023-11-02 The Boeing Company Determining outages of a satellite navigation system and signal interference
CN117130012A (en) * 2023-10-26 2023-11-28 中国民航大学 Rough positioning method for interference source by using open-land topography shielding on undulating topography
CN117130012B (en) * 2023-10-26 2024-01-26 中国民航大学 Rough positioning method for interference source by using open-land topography shielding on undulating topography

Also Published As

Publication number Publication date
CN113759392B (en) 2023-08-18

Similar Documents

Publication Publication Date Title
CN113759392B (en) Robust GNSS interference source positioning method based on flight big data
CN113740882B (en) Robust GNSS interference source positioning method based on arrival power difference by utilizing QAR/ADS-B data
CN102088769B (en) Wireless location method for directly estimating and eliminating non-line-of-sight (NLOS) error
CN112885155A (en) Unmanned aerial vehicle flight collision risk assessment method in fusion airspace
Luo et al. LAAS ionosphere spatial gradient threat model and impact of LGF and airborne monitoring
CN110988922B (en) ADS-B-based three-dimensional ellipsoid cross GNSS interference source positioning method
CN102427602B (en) Sparse-based direct position determination method
CN108562885B (en) High-voltage transmission line airborne LiDAR point cloud extraction method
CN111079859A (en) Passive multi-station multi-target direction finding cross positioning and false point removing method
Bang et al. Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS
CN104392113B (en) A kind of evaluation method of COASTAL SURFACE cold reactive antibodies wind speed
CN115775473A (en) Aircraft positioning system in ADS-B aviation monitoring system
CN117130012B (en) Rough positioning method for interference source by using open-land topography shielding on undulating topography
CN112333634A (en) Hybrid node positioning method based on UAV
Marini-Pereira et al. Advanced warning of threatening equatorial plasma bubbles to support GBAS in low latitudes
CN109669185B (en) Infrasound table network directional search correlation method
CN110308419A (en) A kind of robust TDOA localization method based on static solution and particle filter
CN113298113B (en) Rail-following environment classification method based on train-mounted satellite positioning observation data
CN113008235B (en) Multi-source navigation information fusion method based on matrix K-L divergence
CN115993839A (en) Multi-unmanned aerial vehicle collaborative ground searching online planning method under communication limited condition
Gade et al. Probability distribution for association of maneuvering vehicles
CN114763998A (en) Unknown environment parallel navigation method and system based on micro radar array
CN114384556B (en) Regional high-resolution ionosphere TEC map reconstruction method
Zhao et al. A high-precision positioning scheme under non-point visible transmitters
Gao et al. BSSH: Bluetooth Indoor Positioning Strategy for Ship Cabin Based on Hierarchical Area

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