CN112946695A - Satellite positioning suppression interference identification method based on singular value decomposition - Google Patents

Satellite positioning suppression interference identification method based on singular value decomposition Download PDF

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CN112946695A
CN112946695A CN202110224899.5A CN202110224899A CN112946695A CN 112946695 A CN112946695 A CN 112946695A CN 202110224899 A CN202110224899 A CN 202110224899A CN 112946695 A CN112946695 A CN 112946695A
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刘江
蔡伯根
李健聪
王剑
陆德彪
姜维
上官伟
柴琳果
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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Abstract

The invention provides a satellite positioning suppression interference identification method based on singular value decomposition. The method comprises the following steps: acquiring intermediate frequency signal samples of a satellite positioning receiver in different satellite positioning interference suppression modes, determining singular value sequences in different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain corresponding discrete feature data sets; carrying out interpolation processing on the discrete characteristic data set to obtain an extended discrete characteristic data set, carrying out fitting parameter estimation on the extended discrete characteristic data set by adopting a Logistic function, and establishing a mapping model between an interference mode characteristic vector set and an interference attribute set; acquiring intermediate frequency signals of a navigation satellite in real time, calculating an actual interference characteristic vector, inquiring a mapping model between an interference mode characteristic vector set and an interference attribute set according to the actual interference characteristic vector, and estimating the actual suppressed interference signal attribute at the current moment. The technical scheme of the invention can effectively realize the detection and identification of satellite positioning suppression interference.

Description

Satellite positioning suppression interference identification method based on singular value decomposition
Technical Field
The invention relates to the technical field of satellite navigation positioning, in particular to a singular value decomposition-based satellite positioning suppressed interference identification method.
Background
A Global Navigation Satellite System (GNSS) can provide all-weather, real-time and high-precision positioning, Navigation, time service and other functional services, and is widely applied to various traffic System applications. Particularly, in a novel train operation control system based on vehicle-mounted centralization, a GNSS satellite positioning technology is adopted to sense state information such as train position, speed and direction, dependence of traditional train operation control system equipment on trackside infrastructure (such as a track circuit, a transponder and the like) can be effectively reduced, and estimation and decision of the train operation state can be realized only by using a satellite positioning receiver and a specific vehicle-mounted auxiliary sensor carried by a vehicle-mounted system, so that the train operation control system can serve for generation and execution of train operation control instructions.
The satellite positioning technology is used for the speed measurement and positioning process of a train, the vehicle-mounted antenna is required to acquire the observation information of enough navigation satellites in real time, and then the specific resolving logic is used for completing the positioning calculation, so that the observation quality of satellite signals has decisive influence on the positioning resolving performance. However, the navigation satellite operates in a spatial orbit, the signal power of the transmitted satellite signal is very weak when the transmitted satellite signal reaches the ground receiver antenna after being remotely transmitted, the navigation satellite signal adopts a broadcast type broadcasting mode, the signal transmission process is directly exposed in an open space, and under the condition that the format of the satellite navigation signal and the format of data are completely disclosed, the electromagnetic signal existing in the local environment of the receiver antenna only needs low directional power to possibly form interference and suppression on the useful GNSS signal. In addition, with the rapid development and transformation of the economic society form, in addition to the conventional unintentional signal interference, some satellite positioning suppression interference for deliberate and malicious purposes further increases the safety risk of the GNSS-based train positioning and operation control process. Therefore, active interference protection is applied to the novel train operation control system based on satellite positioning, and the method has extremely important significance for improving the credibility and safety level of the application of the satellite positioning function.
At present, the satellite positioning interference protection technology in the prior art mainly focuses on the anti-interference optimization design of a satellite positioning terminal system, specific software and hardware transformation needs to be implemented for conventional satellite positioning antennas and receiver terminals, and the application real-time performance, complexity and cost characteristics of the technology are difficult to effectively meet the wide application requirements of a rail transit system. When interference analysis is carried out aiming at a possible interference mode of a navigation satellite, different interference detection methods are needed aiming at different types of interference signals in the conventional transform domain interference detection technology, and a uniform processing flow and a judgment basis are difficult to exist; the interference detection technology based on the receiver can realize the high-efficiency detection of specific interference in a targeted manner by extracting parameters after the correlator, but the difficulty of interference type estimation is increased because part of frequency spectrum information is lost in the de-spreading process. In addition, in the existing interference detection and analysis means, the type of the interference signal is mainly used as a processing target, and the identification and confirmation of the specific type of interference under different interference intensities are not further involved.
Therefore, the interference type and the interference signal strength are simultaneously used as target quantities for detection and identification, a corresponding interference characteristic model is constructed facing specific application, and then accurate identification of satellite navigation suppression interference is realized by using the interference characteristic model as a template, so that the method has important practical significance for active interference protection and flexible configuration response of positioning carriers such as trains and the like in a real-time operation process, and research on related methods and technologies is urgently needed to be developed.
Disclosure of Invention
The invention provides a satellite positioning suppression interference identification method based on singular value decomposition, which is used for effectively detecting and identifying satellite positioning suppression interference in real time.
In order to achieve the purpose, the invention adopts the following technical scheme.
A satellite positioning suppression interference identification method based on singular value decomposition comprises the following steps:
under different satellite positioning suppression interference modes, acquiring original intermediate frequency signal samples of a satellite positioning receiver, and constructing an offline interference signal sample library;
performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences in different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain a corresponding discrete feature data set;
carrying out interpolation processing on the discrete characteristic data set to obtain an extended discrete characteristic data set, and carrying out fitting parameter estimation on the extended discrete characteristic data set by adopting a Logistic function to obtain fitting parameters of different suppressed interference modes;
constructing an interference mode characteristic vector based on fitting parameters of different suppressed interference modes, and establishing a mapping model between an interference mode characteristic vector set and an interference attribute set;
acquiring intermediate frequency signals of a navigation satellite in real time, calculating an actual interference characteristic vector, inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set according to the actual interference characteristic vector, and estimating the actual suppressed interference signal attribute at the current moment.
Preferably, the acquiring of the original intermediate frequency signal sample of the satellite positioning receiver under different satellite positioning suppressed interference modes to construct an offline interference signal sample library includes:
step S1.1, synchronously injecting suppressed interference signal j into satellite positioning receiveri,p(t), generating original interference signals with different interference signal intensities in a specific satellite positioning signal observation environment by using interference signal injection equipment to form a satellite positioning suppression interference mode, wherein the interference mode is expressed by the formula (1):
λi,p＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, λi,pIs a main stemA two-dimensional vector consisting of the interference signal type and the interference signal intensity;
s1.2, acquiring original radio frequency signals R under different satellite positioning and interference suppression modes by utilizing a satellite signal acquisition terminali,p(t) converting the original radio frequency signal Ri,pAnd (t) converting the signals into intermediate frequency digital signals, and forming an off-line interference signal sample library by the intermediate frequency digital signals in different satellite positioning and interference suppression modes.
Preferably, the performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences in different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain corresponding discrete feature data sets includes:
step S2.1, based on the intermediate frequency digital signal obtained by positioning and suppressing the interference mode by a specific satellite, intercepting an intermediate frequency data signal sequence X with the length of li,pRepresented by formula (2):
Figure BDA0002956871590000041
the intermediate frequency digital signal sequence Xi,pArranged into an n-order square matrix Ai,pRepresented by the formula (3):
Figure BDA0002956871590000042
for matrix Ai,pSingular value decomposition is carried out to obtain a singular value sequence Wi,pRepresented by formula (4):
Figure BDA0002956871590000043
wherein the singular value sequence Wi,pEach element w inkBased on Ai,p＝USVTDecomposing and extracting each diagonal element of S to obtain;
step S2.2, for the singular value sequence Wi,pThe kth element w ofkTo its sequence number k and its sequence value respectivelywkLogarithmic operation is carried out to obtain ak＝lnk、bk＝lnwkFrom a to ak、bkDiscrete characteristic data set H forming specific satellite positioning suppressed interference modei,pIs (k) n, as represented by equation (5):
Figure BDA0002956871590000044
and S2.3, repeating the steps S2.1 and S2.2, traversing each satellite positioning and suppressing interference mode until the obtained discrete feature data set covers all interference signal types and interference signal intensity.
Preferably, the interpolating the discrete feature data set to obtain an extended discrete feature data set, and performing fitting parameter estimation on the extended discrete feature data set by using a Logistic function to obtain fitting parameters of different suppressed interference modes includes:
step S3.1, for discrete feature data set Hi,pInterpolation is carried out, and a discrete characteristic data set H is removedi,pIn the data akData points > lnn-1, resulting in an extended discrete feature data set Ii,pExpressed by the formula (6):
Figure BDA0002956871590000045
wherein ,(ck,dk) Representing a discrete feature data set Hi,pExtended discrete feature data obtained after interpolation, ck＝a1+(k-1)δ，ckLnn-1, wherein delta represents interpolation granularity, and m represents an extended discrete feature data set I obtained after interpolationi,pThe total number of data elements contained;
step S3.2, adopting Logistic function to obtain an extended discrete feature data set Ii,pPerforming a first fit as represented by equation (7):
Figure BDA0002956871590000051
wherein ,
Figure BDA0002956871590000052
representing the general form of the Logistic function, taking tau, gamma, upsilon and eta as model parameters, representing phi as a specified function space,
Figure BDA0002956871590000053
representing the extension of a discrete feature dataset I by Primary fittingi,pAnd obtaining the Logistic model of the target gene,
Figure BDA0002956871590000054
including corresponding model fitting parameters
Figure BDA0002956871590000055
Figure BDA0002956871590000056
Wherein the letter R denotes the fitting result corresponding to the initial fit;
step S3.3, setting a fitting weight set
Figure BDA0002956871590000057
For the extended discrete feature data set I according to the model parameters obtained by fittingi,pIn (c)kThe magnitude of the values determines the fitting weight βkRepresented by formula (8):
Figure BDA0002956871590000058
wherein ,αhigh、αlowRespectively represent the fitting weights betakUpper and lower bound eigenvalues of (c).
Step S3.4, according to the obtained fitting weight set Bi,pAdopting Logistic function to carry out pair on obtained extended discrete characteristic data set Ii,pA fine fit is performed, as represented by equation (9):
Figure BDA0002956871590000059
wherein ,
Figure BDA00029568715900000510
representing the extension of a discrete feature dataset I by a Fine-fittingi,pAnd obtaining the Logistic model of the target gene,
Figure BDA00029568715900000511
including corresponding model fitting parameters
Figure BDA00029568715900000512
Where the letter P indicates the fit result corresponding to a fine fit.
Preferably, the constructing an interference pattern feature vector based on the fitting parameters of the different suppressed interference patterns, and the establishing a mapping model between an interference pattern feature vector set and an interference attribute set includes:
s4.1, calculating Logistic models obtained by fine fitting under different satellite positioning suppression interference modes
Figure BDA0002956871590000061
Model value at x ═ 0
Figure BDA0002956871590000062
Model value
Figure BDA0002956871590000063
With corresponding model parameters
Figure BDA0002956871590000064
Co-construction of interference pattern eigenvectors χi,pAs represented by formula (10):
Figure BDA0002956871590000065
s4.2, repeating the step 4.1, traversing each satellite positioning suppression interference mode, and summarizing the positions of different satellite positioning suppression interference modesObtaining the interference pattern feature vector to form an interference pattern feature vector set { χ }i,pAnd combining the interference signal attribute set { lambda ] corresponding to each satellite positioning suppression interference modei,pEstablishing an interference mode characteristic vector set { chi } by taking the interference signal type i and the interference signal strength p as indexesi,pAnd a set of interference signal attributes λi,pMapping model between lambdai,p＝f(χi,p) And f (#) is a mapping function.
Preferably, the acquiring intermediate frequency signals of the navigation satellite in real time, calculating an actual interference eigenvector, querying a mapping model between the interference pattern eigenvector set and the interference attribute set according to the actual interference eigenvector, and estimating an actual interference suppression signal attribute at the current time includes:
step S5.1, in the actual train running process, according to a given interference detection period TdFrom the current operating time tsAcquired intermediate frequency digital signal sequence points
Figure BDA0002956871590000066
Performing time backtracking by intercepting a section of starting point
Figure BDA0002956871590000067
End point is
Figure BDA0002956871590000068
Is of length l, is used to generate a sequence of intermediate frequency digital signal samples X (t)s) Represented by formula (11):
Figure BDA0002956871590000069
the intermediate frequency digital signal sequence X (t)s) Arranged in an n-order square matrix A (t)s) Obtaining a singular value sequence W (t) through singular value decompositions) Based on A (t)s)＝USVTDecomposing and extracting S diagonal elements to obtain W (t)s) Each element w ink；
Step S5.2, the obtained singular value sequence W (t)s) IntoLine number transformation and logarithm operation to form discrete characteristic data set H (t)s) Expanding a discrete feature data set I (t)s) Using Logistic function to obtain extended discrete characteristic data set I (t)s) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t)s) The resulting Logistic model
Figure BDA0002956871590000071
Corresponding parameter of
Figure BDA0002956871590000072
Step S5.3, calculating a precise fitting model
Figure BDA0002956871590000073
Model value at x ═ 0
Figure BDA0002956871590000074
And corresponding model parameters
Figure BDA0002956871590000075
Together form tsActual interference characteristic vector χ (t) at moments) As shown in formula (12):
Figure BDA0002956871590000076
step S5.4, according to tsActual interference characteristic vector χ (t) at moments) Inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set to obtain tsEstimation value lambda (t) of interference signal property at moments)＝f[χ(ts)]From λ (t)s) To obtain tsType i (t) of the time-stamped interference signals) And intensity p (t)s)。
The technical scheme provided by the invention shows that the invention provides a satellite positioning suppression interference identification method based on singular value decomposition, aims to establish an interference signal singular value sequence model by utilizing related characteristic samples of different suppression interference signals, and provides a feasible way for rapidly and accurately detecting and identifying the type and the strength of the satellite interference signal.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for identifying interference suppression in satellite positioning based on singular value decomposition according to an embodiment of the present invention.
Fig. 2 is an intermediate frequency digital signal sequence intercepted from an intermediate frequency digital signal obtained by suppressing an interference pattern based on a CWI under a condition that an interference signal strength is-70 dBm according to an embodiment of the present invention.
Fig. 3 is a discrete feature data set obtained by suppressing the interference pattern based on the CWI under the condition that the interference signal strength is-70 dBm according to the embodiment of the present invention.
Fig. 4 is a mapping model between a CWI interference feature vector set and a CWI attribute set according to an embodiment of the present invention.
Fig. 5 shows an estimation result of an attribute of an aggressor signal with a test aggressor signal strength p-58 dBm in a CWI squashing interference mode according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention provides a satellite positioning suppressed interference identification method based on singular value decomposition, which aims to establish an interference signal singular value sequence model by utilizing related characteristic samples of different suppressed interference signals and provide a feasible way for quickly and accurately detecting and identifying the type and the strength of the interference signals, so that effective response is made in pertinence, the accuracy and the safety of train satellite positioning are improved, and a Beidou satellite navigation system is supported to play an expected role in safety-related traffic system application.
Example one
Step S1, collecting original intermediate frequency signal samples of the satellite positioning receiver under different satellite positioning interference suppression modes, and constructing an off-line interference signal sample library;
step S2, performing singular value decomposition on the collected original intermediate frequency signal sample, determining singular value sequences under different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain corresponding discrete feature data sets;
step S3, carrying out interpolation processing on the discrete characteristic data set to obtain an extended discrete characteristic data set, and carrying out fitting parameter estimation on the extended discrete characteristic data set by adopting a Logistic function to obtain fitting parameters of different suppression interference modes;
step S4, constructing interference mode feature vectors based on the fitting parameters of different suppression interference modes, and establishing a mapping model between an interference mode feature vector set and an interference attribute set;
step S5, collecting intermediate frequency signals of the navigation satellite in real time, calculating actual interference characteristic vectors, inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set according to the actual interference characteristic vectors, and estimating actual suppressed interference signal attributes at the current moment.
Preferably, step S1 specifically includes: the interference signal injection device is used for generating original interference signals with different interference signal strengths in a specific satellite positioning signal observation environment. The satellite positioning interference suppression mode refers to that under the same satellite positioning signal observation environment, a suppression interference signal j is synchronously injected into a satellite positioning receiveri,p(t) forming a positioning suppression interference effect by the signal, and recording the attribute of the interference signal as lambdai,pRepresented by formula (1):
λi,p＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, λi,pIs a two-dimensional vector consisting of the type of the interfering signal and the strength of the interfering signal.
Acquiring original radio frequency signals R under different satellite positioning suppression interference modes by utilizing satellite signal acquisition terminali,pAnd (t) converting the intermediate frequency digital signals into intermediate frequency digital signals, and forming an off-line interference signal sample library by the intermediate frequency digital signals in different satellite positioning suppression interference modes.
Preferably, step S2 specifically includes:
s2.1, based on the intermediate frequency digital signal obtained by the specific satellite positioning suppression interference mode, intercepting an intermediate frequency data signal sequence X with the length of li,pRepresented by formula (2):
Figure BDA0002956871590000101
the intermediate frequency digital signal sequence X is processedi,pArranged into an n-order square matrix Ai,pRepresented by the formula (3):
Figure BDA0002956871590000102
for matrix Ai,pSingular value decomposition is carried out to obtain a singular value sequence Wi,pRepresented by formula (4):
Figure BDA0002956871590000103
wherein the singular value sequence Wi,pEach element w inkCan be based on Ai,p＝USVTAnd decomposing and extracting S diagonal elements.
Step S2.2, positioning and suppressing singular value sequence W obtained in interference mode for specific satellitei,pCarrying out numerical value transformation, and carrying out logarithm operation on the sequence number and the sequence value, namely: for a sequence of singular values Wi,pThe kth element w ofkFor its sequence number k and its sequence value w, respectivelykLogarithmic operation is carried out to obtain ak＝lnk、bk＝lnwkFrom a to ak、bkDiscrete characteristic data set H forming specific satellite positioning suppressed interference modei,pIs (k) n, as represented by equation (5):
Figure BDA0002956871590000111
and S2.3, repeating the steps S2.1 and S2.2, traversing each satellite positioning and suppressing interference mode until the obtained discrete feature data set covers all interference signal types and interference signal intensity.
Preferably, the step S3 includes:
step S3.1, the obtained discrete feature data set Hi,pInterpolation is carried out to eliminate Hi,pIn the data akData points > lnn-1, resulting in an extended discrete feature data set Ii,pExpressed by the formula (6):
Figure BDA0002956871590000112
wherein ,(ck,dk) Representing a discrete feature data set Hi,pExtended discrete feature data obtained after interpolation, ck＝a1+(k-1)δ，ckLnn-1, wherein delta represents interpolation granularity, and m represents an extended discrete feature data set I obtained after interpolationi,pThe total number of data elements contained.
Step S3.2, adopting Logistic function to obtain an extended discrete feature data set Ii,pThe initial fit is performed, the principle of which is expressed by the formula (7):
Figure BDA0002956871590000113
wherein ,
Figure BDA0002956871590000114
representing the general form of the Logistic function, taking tau, gamma, upsilon and eta as model parameters, representing phi as a specified function space,
Figure BDA0002956871590000115
representing the extension of a discrete feature dataset I by Primary fittingi,pTo obtainThe Logistic model of (a) is described,
Figure BDA0002956871590000116
including corresponding model fitting parameters
Figure BDA0002956871590000117
Wherein the letter R indicates the fitting result corresponding to the initial fit.
Step S3.3, setting a fitting weight set
Figure BDA0002956871590000118
For the extended discrete feature data set I according to the model parameters obtained by fittingi,pIn (c)kThe magnitude of the values determines the fitting weight βkThe main value principle is expressed by the formula (8):
Figure BDA0002956871590000121
wherein ,αhigh、αlowRespectively represent the fitting weights betakUpper and lower bound eigenvalues of (c).
Step S3.4, according to the obtained fitting weight set Bi,pAdopting Logistic function to carry out pair on obtained extended discrete characteristic data set Ii,pA fine fit is performed, as represented by equation (9):
Figure BDA0002956871590000122
wherein ,
Figure BDA0002956871590000123
representing the extension of a discrete feature dataset I by a Fine-fittingi,pAnd obtaining the Logistic model of the target gene,
Figure BDA0002956871590000124
including corresponding model fitting parameters
Figure BDA0002956871590000125
Where the letter P indicates the fit result corresponding to a fine fit.
Preferably, the step S4 includes:
s4.1, calculating Logistic models obtained by fine fitting under different satellite positioning suppression interference modes
Figure BDA0002956871590000126
Model value at x ═ 0
Figure BDA0002956871590000127
It is associated with the corresponding model parameters
Figure BDA0002956871590000128
Co-construction of interference pattern eigenvectors χi,pAs represented by formula (10):
Figure BDA0002956871590000129
s4.2, repeating the step 4.1, traversing each satellite positioning suppression interference mode, summarizing interference mode characteristic vectors obtained by different satellite positioning suppression interference modes, and forming an interference mode characteristic vector set { χ }i,pAnd combining the interference signal attribute set { lambda ] corresponding to each satellite positioning suppression interference modei,pEstablishing an interference mode characteristic vector set { chi } by taking the interference signal type i and the interference signal strength p as indexesi,pAnd a set of interference signal attributes λi,pMapping model between lambdai,p＝f(χi,p) And f (#) is a mapping function.
Preferably, the step S5 includes:
step S5.1, in the actual train running process, according to a given interference detection period TdExtracting a section of intermediate frequency digital signal X (t) of vehicle-mounted satellite signal acquisition equipment with sequence length of l by using a fixed time windows) I.e. from the current operating time tsAcquired intermediate frequency digital signal sequence points
Figure BDA0002956871590000131
Performing time backtracking by intercepting a section of starting point
Figure BDA0002956871590000132
End point is
Figure BDA0002956871590000133
Is l, as expressed in equation (11):
Figure BDA0002956871590000134
the intermediate frequency digital signal sequence X (t) is processeds) Arranged in an n-order square matrix A (t)s) Obtaining a singular value sequence W (t) through singular value decompositions) Based on A (t)s)＝USVTDecomposing and extracting S diagonal elements to obtain W (t)s) Each element w ink。
Step S5.2, adopting the similar procedures as the steps S2 and S3 to obtain the singular value sequence W (t)s) Performing numerical transformation and logarithm operation to form discrete feature data set H (t)s) Expanding a discrete feature data set I (t)s) Using Logistic function to obtain extended discrete characteristic data set I (t)s) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t)s) The resulting Logistic model
Figure BDA0002956871590000135
Corresponding parameter of
Figure BDA0002956871590000136
Step S5.3, calculating a precise fitting model
Figure BDA0002956871590000137
Model value at x ═ 0
Figure BDA0002956871590000138
And corresponding model parameters
Figure BDA0002956871590000139
Together form tsActual interference characteristic vector χ (t) at moments) As shown in formula (12):
Figure BDA00029568715900001310
step S5.4, calling a mapping model f between the interference mode characteristic vector set and the interference attribute set obtained in the step S4.2, and based on tsActual interference characteristic vector χ (t) at moments) To obtain tsEstimation value lambda (t) of interference signal property at moments)＝f[χ(ts)]From λ (t)s) To obtain tsType i (t) of the time-stamped interference signals) And intensity p (t)s)。
Example two
A specific processing flow of the method for identifying interference suppression in satellite positioning based on singular value decomposition according to this embodiment is shown in fig. 1, and includes the following steps:
and step S1, acquiring original intermediate frequency signal samples of the satellite positioning receiver under different satellite positioning interference suppression modes, and constructing an off-line interference signal sample library.
And step S2, performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences in different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain corresponding discrete feature data sets.
And step S3, performing interpolation processing on the discrete characteristic data set to obtain an extended discrete characteristic data set, and performing fitting parameter estimation on the obtained extended discrete characteristic data set by adopting a Logistic function to obtain fitting parameters of different suppressed interference modes.
And step S4, constructing interference mode characteristic vectors based on the fitting parameters of different suppressed interference modes, and establishing a mapping model between the interference mode characteristic vector set and the interference attribute set.
And step S5, acquiring intermediate frequency signals of the navigation satellite in real time in the actual operation process, extracting a sample sequence in an instant window based on a fixed time window, and calculating an actual interference characteristic vector. And inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set according to the actual interference characteristic vector, and estimating the actual suppressed interference signal attribute at the current moment.
Step S1 further includes the following sub-steps:
substep S1.1, in this embodiment, taking a single frequency interference signal (CWI) interference suppression mode as an example, in the same satellite positioning signal observation environment, a CWI signal j is synchronously injected into a satellite positioning receiverCW,Ip(t) forming a positioning suppression interference effect by the signal, and recording the attribute of the interference signal as lambdaCWI,pAs represented by formula (13):
λCWI,p＝[CWI,p] (13)
wherein: the character CWI represents the interference signal type CWI signal, p the interference signal strength, λCWI,pIs a two-dimensional vector consisting of the type of the interfering signal and the strength of the interfering signal.
In the sub-step S1.2,
taking a single frequency interference signal (CWI) interference suppression mode as an example, a satellite signal acquisition terminal is used for acquiring an original radio frequency signal R under different interference intensities under the CWI satellite positioning interference suppression modeCWI,p(t) converting the original radio frequency signal RCWI,pAnd (t) converting the signal into an intermediate frequency digital signal, and forming an off-line interference signal sample library by the intermediate frequency digital signal in the CWI satellite positioning interference suppression mode.
Step S2 further includes the following sub-steps:
substep S2.1, based on the intermediate frequency digital signal obtained from the CWI satellite positioning interference suppression mode, truncating the length l to 1 × 106Of the intermediate frequency data signal sequence XCWI,pAs represented by formula (14):
Figure BDA0002956871590000151
sequence X at different suppression interference intensity levelsCWI,pThe method provides original basic information for constructing an interference pattern feature vector mapping model, and the embodimentIntermediate frequency digital signal sequence X intercepted from intermediate frequency digital signal obtained by suppressing interference mode based on CWI with interference strength of-70 dBmCWI,-70As shown in fig. 2.
The intermediate frequency digital signal sequence X is processedCWI,pArranged into an n-order square matrix ACWI,pRepresented by the formula (15):
Figure BDA0002956871590000152
for matrix ACWI,pSingular value decomposition is carried out to obtain a singular value sequence WCWI,pAs represented by formula (16):
Figure BDA0002956871590000153
wherein the singular value sequence WCWI,pEach element w inkCan be based on ACWI,p＝USVTAnd decomposing and extracting S diagonal elements.
Substep S2.2, for the sequence of singular values WCWI,pAnd carrying out numerical value transformation to obtain a corresponding discrete feature data set. Singular value sequence W obtained by suppressing interference mode in CWI satellite positioningCWI,pCarrying out numerical value transformation, and carrying out logarithm operation on the sequence number and the sequence value, namely: for a sequence of singular values WCWI,pThe kth element w ofkFor its sequence number k and its sequence value w, respectivelykLogarithmic operation is carried out to obtain ak＝lnk、bk＝lnwkFrom a to ak、bkDiscrete feature data set H forming CWI satellite positioning suppression interference modeCW,IpThe kth element (k.ltoreq.n) in (1), as represented by formula (17):
Figure BDA0002956871590000161
FIG. 3 shows a discrete feature data set H obtained by suppressing the interference pattern based on CWI under the condition of-70 dBm of the interference signal strength in the present embodimentCWI,-70。
Step S3 further includes the following sub-steps:
substep S3.1, for the resulting discrete feature data set HCWI,pInterpolation is performed, in this embodiment, a linear interpolation method is adopted, and the interpolation calculation process is shown in equation (18):
Figure BDA0002956871590000162
on the basis, H is further removedCWI,pIn the data akData points > lnn-1, resulting in an extended discrete feature data set ICWI,pAs represented by formula (19):
Figure BDA0002956871590000163
wherein ,(ck,dk) Representing a discrete feature data set HCWI,pThe extended discrete feature data obtained after interpolation calculation, m represents the extended discrete feature data set I obtained after interpolationCWI,pThe total number of data elements contained, the interpolation granularity, is taken to be δ 0.1.
Substep S3.2, using Logistic function to obtain extended discrete characteristic data set ICWI,pThe initial fit is performed, the principle of which is expressed by equation (20):
Figure BDA0002956871590000164
wherein ,
Figure BDA0002956871590000165
representing the general form of the Logistic function, taking tau, gamma, upsilon and eta as model parameters, representing phi as a specified function space,
Figure BDA0002956871590000166
representing the extension of a discrete feature dataset I by Primary fittingCWI,pAnd obtaining the Logistic model of the target gene,
Figure BDA0002956871590000171
including corresponding model fitting parameters
Figure BDA0002956871590000172
Figure BDA0002956871590000173
Wherein the letter R indicates the fitting result corresponding to the initial fit.
Substep S3.3, set the set of fitting weights
Figure BDA0002956871590000174
For the extended discrete feature data set I according to the model parameters obtained by fittingCWI,pIn (c)kThe magnitude of the values determines the fitting weight βkThe main value principle is expressed by the formula (21):
Figure BDA0002956871590000175
substep S3.4, based on the obtained fitting weight set BCWI,pAdopting Logistic function to carry out pair on obtained extended discrete characteristic data set ICWI,pA fine fit is performed, as represented by equation (22):
Figure BDA0002956871590000176
wherein ,
Figure BDA0002956871590000177
representing the extension of a discrete feature dataset I by a Fine-fittingCWI,pAnd obtaining the Logistic model of the target gene,
Figure BDA0002956871590000178
including corresponding model fitting parameters
Figure BDA0002956871590000179
Where the letter P indicates the fit result corresponding to a fine fit.
Step S4 further includes the following sub-steps:
substep S4.1, calculating Logistic model obtained by fine fitting
Figure BDA00029568715900001710
Model value at x ═ 0
Figure BDA00029568715900001711
It is associated with the corresponding model parameters
Figure BDA00029568715900001712
Co-construction of interference pattern eigenvectors χCWI,pAs represented by formula (23):
Figure BDA00029568715900001713
substep S4.2, repeating substep S4.1, traversing different interference signal intensities under the CWI interference suppression mode, enabling the constructed interference mode feature vector to cover all interference signal intensity levels, and summarizing the interference mode feature vectors to form a CWI interference feature vector set { χ [% ]CWI,pAnd combining CWI attribute sets { lambda ] corresponding to CWI satellite positioning suppression interference modes with different interference signal strengthsCWI,pEstablishing a CWI interference feature vector set { χ ] by using the unique interference signal type CWI and a plurality of interference signal strengths p as indexesCWI,pIs compared with CWI attribute set lambdaCWI,pMapping model between | λCWI,p＝f(χCWI,p) And f (#) is a mapping function.
Preferably, the CWI interference feature vector set { χ ] in this embodimentCWI,pIs compared with CWI attribute set lambdaCWI,pThe mapping model f between f is constructed as follows:
for a two-dimensional vector [ phi ]1,φ2]If there is an adjacency
Figure BDA0002956871590000187
Satisfy the requirement of
Figure BDA0002956871590000188
The mapping model f is as shown in equation (24):
f(χ)＝λ，χ＝[φ1,φ2],λ＝[φ3,φ4] (24)
wherein ,φ3、φ4Respectively representing two characteristic quantities of interference signal type and interference signal strength, and the determination principle is shown in formulas (25) and (26):
Figure BDA0002956871590000181
Figure BDA0002956871590000182
wherein the boundary parameter κL and κHAs shown in formulas (27) and (28):
Figure BDA0002956871590000183
Figure BDA0002956871590000184
wherein ,
Figure BDA0002956871590000186
respectively take
Figure BDA0002956871590000185
FIG. 4 shows a CWI interference feature vector set { χ } established in the above mannerCWI,pIs compared with CWI attribute set lambdaCWI,pMapping model between lambdaCWI,p＝f(χCWI,p) Wherein, fig. 4 shows the results of 9 cases in which the interference signal strength is in the range of-90 dBm to-50 dBm in the CWI squelching interference mode.
Step S5 further includes the following sub-steps:
substep S5.1, during the actual train operation, detecting the period T according to the given interferencedExtracting a sequence with length of 1 × 10 in a fixed time window6The intermediate frequency digital signal X (t) of the vehicle-mounted satellite signal acquisition equipments) I.e. from the current operating time tsAcquired intermediate frequency digital signal sequence points
Figure BDA0002956871590000191
Performing time backtracking by intercepting a section of starting point
Figure BDA0002956871590000192
End point is
Figure BDA0002956871590000193
Length of (1) × 106Is expressed as equation (29):
Figure BDA0002956871590000194
the intermediate frequency digital signal sequence X (t) is processeds) Arranged in an n-order square matrix A (t)s) As represented by formula (30):
Figure BDA0002956871590000195
for matrix A (t)s) Singular value decomposition is carried out to obtain a singular value sequence W (t)s) As represented by formula (31):
Figure BDA0002956871590000196
wherein the singular value sequence W (t)s) Each element w inkCan be based on A (t)s)＝USVTAnd decomposing and extracting S diagonal elements.
Substep S5.2, for the obtained singular value sequence W (t)s) Performing numerical transformation, and performing logarithm operation on the sequence number and the sequence value, i.e.: for a sequence of singular values W (t)s) The kth element w ofkFor its sequence number k and its sequence value w, respectivelykLogarithmic operation is carried out to obtain ak＝lnk、bk＝lnwkFrom a to ak、bkComposition tsDiscrete feature data set H (t) for distinguishing interference signal attribute at moments) Is (k) n, as represented by equation (32):
Figure BDA0002956871590000197
this embodiment uses linear interpolation to discrete feature data set H (t)s) Interpolation is performed, and the interpolation calculation method is shown in formula (33):
Figure BDA0002956871590000201
on the basis, H (t) is eliminateds) In the data akData > lnn-1, resulting in an extended discrete feature data set I (t)s) As represented by formula (34):
Figure BDA0002956871590000202
wherein ,(ck,dk) Representation versus discrete feature data set H (t)s) Extended discrete feature data obtained after interpolation, ck＝a1+(k-1)d，ckIs less than or equal to ln (n-1), and m represents an extended discrete feature data set I (t) obtained after interpolations) The total number of data elements contained, the interpolation granularity, is taken to be δ 0.1.
Using Logistic function to obtain extended discrete characteristic data set I (t)s) The initial fit is performed, the principle of which is expressed by equation (35):
Figure BDA0002956871590000203
wherein ,
Figure BDA0002956871590000204
representing the general form of the Logistic function, taking tau, gamma, upsilon and eta as model parameters, representing phi as a specified function space,
Figure BDA0002956871590000205
representing the expansion of a discrete feature data set I (t) by a first fits) And obtaining the Logistic model of the target gene,
Figure BDA0002956871590000206
including corresponding model fitting parameters
Figure BDA0002956871590000207
Figure BDA0002956871590000208
Wherein the letter R indicates the fitting result corresponding to the initial fit.
Setting a set of fitting weights
Figure BDA0002956871590000209
For the extended discrete feature data set I (t) according to the model parameters obtained by fittings) In (c)kThe magnitude of the values determines the fitting weight βkThe main value principle is expressed by the formula (36):
Figure BDA00029568715900002010
according to the obtained fitting weight set B (t)s) Using Logistic function to obtain extended discrete characteristic data set I (t)s) A fine fit is performed, as represented by equation (37):
Figure BDA0002956871590000211
wherein ,
Figure BDA0002956871590000212
representing the expansion of a discrete feature data set I (t) by a fine fits) And obtaining the Logistic model of the target gene,
Figure BDA0002956871590000213
including corresponding model fitting parameters
Figure BDA0002956871590000214
Where the letter P indicates the fit result corresponding to a fine fit.
Substep S5.3, calculating Logistic model obtained by fine fitting
Figure BDA0002956871590000215
Model value at x ═ 0
Figure BDA0002956871590000216
With corresponding model parameters
Figure BDA0002956871590000217
Are formed jointly
Figure BDA0002956871590000218
Actual interference characteristic vector χ (t) at moments) As shown in equation (38):
Figure BDA0002956871590000219
substep S5.4 of calling the mapping model f between the interference pattern feature vector set and the interference attribute set obtained in substep S4.2, based on tsActual interference characteristic vector χ (t) at moments) To obtain tsEstimation value lambda (t) of interference signal property at moments)＝f[χ(ts)]From λ (t)s) To obtain tsType i (t) of the time-stamped interference signals) And intensity p (t)s). Fig. 5 shows the estimation result of the measured interference signal property with p-58 dBm in the CWI squashing interference mode of the present embodiment, which correctly identifies the corresponding signal strength level.
In summary, the present invention provides a method for identifying suppressed interference in satellite positioning based on singular value decomposition, which does not require a large software and hardware amplification of the existing vehicle-mounted satellite positioning terminal, extracts specific data only from the middle link of the satellite positioning signal receiving and processing flow, constructs a relevant feature model of suppressed interference in satellite positioning through a certain data processing computation amount, and can dynamically apply data in the actual operation process to implement corresponding interference feature discrimination and determine the actual type and intensity level of suppressed interference that may exist in the operation environment by using the construction of the sample model and the relevant detection based on the model. From the core idea, the main flow and the implementation mode of the method provided by the invention, the method has the advantages of clear low-cost characteristic, implementation convenience and application adaptability, and can achieve excellent interference characteristic coverage and identification performance by combining a large number of satellite positioning suppressed interference mode samples.
The method for identifying the satellite positioning suppressed interference based on singular value decomposition can be embedded into satellite positioning terminals contained in carriers such as existing road vehicles, rail transit trains and the like at lower implementation cost, and further superposes corresponding suppressed interference monitoring functions on the basis of implementing autonomous fault detection and diagnosis by a traditional satellite positioning receiver terminal, thereby providing additional interference defense and reinforcement for the GNSS-based carrier maneuvering positioning, providing an active monitoring, early warning and calibration approach of a satellite positioning interference feature library for complex road networks and rail transit network environments, and creating a better information security guarantee mechanism for specific application of satellite positioning.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A satellite positioning suppression interference identification method based on singular value decomposition is characterized by comprising the following steps:
under different satellite positioning suppression interference modes, acquiring original intermediate frequency signal samples of a satellite positioning receiver, and constructing an offline interference signal sample library;
performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences in different interference suppression modes, and performing numerical value conversion on the singular value sequences to obtain a corresponding discrete feature data set;
carrying out interpolation processing on the discrete characteristic data set to obtain an extended discrete characteristic data set, and carrying out fitting parameter estimation on the extended discrete characteristic data set by adopting a Logistic function to obtain fitting parameters of different suppressed interference modes;
constructing an interference mode characteristic vector based on fitting parameters of different suppressed interference modes, and establishing a mapping model between an interference mode characteristic vector set and an interference attribute set;
acquiring intermediate frequency signals of a navigation satellite in real time, calculating an actual interference characteristic vector, inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set according to the actual interference characteristic vector, and estimating the actual suppressed interference signal attribute at the current moment.
2. The method according to claim 1, wherein the acquiring of the original intermediate frequency signal samples of the satellite positioning receiver in different satellite positioning interference suppression modes to construct an offline interference signal sample library comprises:
step S1.1, synchronously injecting suppressed interference signal j into satellite positioning receiveri,p(t), generating original interference signals with different interference signal intensities in a specific satellite positioning signal observation environment by using interference signal injection equipment to form a satellite positioning suppression interference mode, wherein the interference mode is expressed by the formula (1):
λi,p＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, λi,pA two-dimensional vector consisting of the interference signal type and the interference signal strength is formed;
s1.2, acquiring original radio frequency signals R under different satellite positioning and interference suppression modes by utilizing a satellite signal acquisition terminali,p(t) converting the original radio frequency signal Ri,p(t) converting the signal into an intermediate frequency digital signal, and suppressing the intermediate frequency digital signal in the interference mode by positioning different satellitesThe numbers constitute an off-line interference signal sample library.
3. The method according to claim 1, wherein the performing singular value decomposition on the collected original intermediate frequency signal samples, determining singular value sequences in different interference suppression modes, and performing numerical value transformation on the singular value sequences to obtain corresponding discrete feature data sets comprises:
step S2.1, based on the intermediate frequency digital signal obtained by positioning and suppressing the interference mode by a specific satellite, intercepting an intermediate frequency data signal sequence X with the length of li,pRepresented by formula (2):
Figure FDA0002956871580000021
the intermediate frequency digital signal sequence Xi,pArranged into an n-order square matrix Ai,pRepresented by the formula (3):
Figure FDA0002956871580000022
for matrix Ai,pSingular value decomposition is carried out to obtain a singular value sequence Wi,pRepresented by formula (4):
Figure FDA0002956871580000023
wherein the singular value sequence Wi,pEach element w inkBased on Ai,p＝USVTDecomposing and extracting each diagonal element of S to obtain;
step S2.2, for the singular value sequence Wi,pThe kth element w ofkFor its sequence number k and its sequence value w, respectivelykLogarithmic operation is carried out to obtain ak＝lnk、bk＝lnwkFrom a to ak、bkDiscrete characteristic data set H forming specific satellite positioning suppressed interference modei,pThe k element of (2)(k.ltoreq.n) as represented by formula (5):
Figure FDA0002956871580000024
and S2.3, repeating the steps S2.1 and S2.2, traversing each satellite positioning and suppressing interference mode until the obtained discrete feature data set covers all interference signal types and interference signal intensity.
4. The method according to claim 1, wherein the interpolating the discrete feature data set to obtain an extended discrete feature data set, and performing fitting parameter estimation on the extended discrete feature data set by using a Logistic function to obtain fitting parameters of different interference suppression modes comprises:
step S3.1, for discrete feature data set Hi,pInterpolation is carried out, and a discrete characteristic data set H is removedi,pIn the data akData points > lnn-1, resulting in an extended discrete feature data set Ii,pExpressed by the formula (6):
Figure FDA0002956871580000031
wherein ,(ck,dk) Representing a discrete feature data set Hi,pExtended discrete feature data obtained after interpolation, ck＝a1+(k-1)δ，ckLnn-1, wherein delta represents interpolation granularity, and m represents an extended discrete feature data set I obtained after interpolationi,pThe total number of data elements contained;
step S3.2, adopting Logistic function to obtain an extended discrete feature data set Ii,pPerforming a first fit as represented by equation (7):
Figure FDA0002956871580000032
wherein ,
Figure FDA0002956871580000033
representing the general form of the Logistic function, taking tau, gamma, upsilon and eta as model parameters, representing phi as a specified function space,
Figure FDA0002956871580000034
representing the extension of a discrete feature dataset I by Primary fittingi,pAnd obtaining the Logistic model of the target gene,
Figure FDA0002956871580000035
including corresponding model fitting parameters
Figure FDA0002956871580000036
Figure FDA0002956871580000037
Wherein the letter R denotes the fitting result corresponding to the initial fit;
step S3.3, setting a fitting weight set
Figure FDA0002956871580000038
For the extended discrete feature data set I according to the model parameters obtained by fittingi,pIn (c)kThe magnitude of the values determines the fitting weight βkRepresented by formula (8):
Figure FDA0002956871580000039
wherein ,αhigh、αlowRespectively represent the fitting weights betakUpper and lower bound eigenvalues of (c).
Step S3.4, according to the obtained fitting weight set Bi,pAdopting Logistic function to carry out pair on obtained extended discrete characteristic data set Ii,pA fine fit is performed, as represented by equation (9):
Figure FDA0002956871580000041
wherein ,
Figure FDA0002956871580000042
representing the extension of a discrete feature dataset I by a Fine-fittingi,pAnd obtaining the Logistic model of the target gene,
Figure FDA0002956871580000043
including corresponding model fitting parameters
Figure FDA0002956871580000044
Where the letter P indicates the fit result corresponding to a fine fit.
5. The method according to claim 1, wherein the constructing interference pattern feature vectors based on the fitting parameters of different suppressed interference patterns, and establishing a mapping model between a set of interference pattern feature vectors and a set of interference attributes comprises:
s4.1, calculating Logistic models obtained by fine fitting under different satellite positioning suppression interference modes
Figure FDA0002956871580000045
Model value at x ═ 0
Figure FDA0002956871580000046
Model value
Figure FDA0002956871580000047
With corresponding model parameters
Figure FDA0002956871580000048
Co-construction of interference pattern eigenvectors χi,pAs represented by formula (10):
Figure FDA0002956871580000049
s4.2, repeating the step 4.1, traversing each satellite positioning suppression interference mode, summarizing interference mode characteristic vectors obtained by different satellite positioning suppression interference modes, and forming an interference mode characteristic vector set { χ }i,pAnd combining the interference signal attribute set { lambda ] corresponding to each satellite positioning suppression interference modei,pEstablishing an interference mode characteristic vector set { chi } by taking the interference signal type i and the interference signal strength p as indexesi,pAnd a set of interference signal attributes λi,pMapping model between lambdai,p＝f(χi,p) And f (#) is a mapping function.
6. The method of claim 1, wherein the acquiring intermediate frequency signals of the navigation satellite in real time, calculating an actual interference eigenvector, querying a mapping model between the set of interference pattern eigenvectors and the set of interference attributes according to the actual interference eigenvector, and estimating actual suppressed interference signal attributes at a current time comprises:
step S5.1, in the actual train running process, according to a given interference detection period TdFrom the current operating time tsAcquired intermediate frequency digital signal sequence points
Figure FDA0002956871580000051
Performing time backtracking by intercepting a section of starting point
Figure FDA0002956871580000052
End point is
Figure FDA0002956871580000053
Is of length l, is used to generate a sequence of intermediate frequency digital signal samples X (t)s) Represented by formula (11):
Figure FDA0002956871580000054
the intermediate frequency digital signal sequence X (t)s) Arranged in an n-order square matrix A (t)s) Obtaining a singular value sequence W (t) through singular value decompositions) Based on A (t)s)＝USVTDecomposing and extracting S diagonal elements to obtain W (t)s) Each element w ink；
Step S5.2, the obtained singular value sequence W (t)s) Performing numerical transformation and logarithm operation to form discrete feature data set H (t)s) Expanding a discrete feature data set I (t)s) Using Logistic function to obtain extended discrete characteristic data set I (t)s) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t)s) The resulting Logistic model
Figure FDA0002956871580000055
Corresponding parameter of
Figure FDA0002956871580000056
Step S5.3, calculating a precise fitting model
Figure FDA0002956871580000057
Model value at x ═ 0
Figure FDA0002956871580000058
And corresponding model parameters
Figure FDA0002956871580000059
Together form tsActual interference characteristic vector χ (t) at moments) As shown in formula (12):
Figure FDA00029568715800000510
step S5.4, according to tsActual interference characteristic vector χ (t) at moments) Inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set to obtain tsEstimation value lambda (t) of interference signal property at moments)＝f[χ(ts)]From λ (t)s) To obtain tsType i (t) of the time-stamped interference signals) And intensity p (t)s)。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114563756A (en) * 2022-01-12 2022-05-31 中国电子科技集团公司第十研究所 Linear interpolation method for direction finding sample of dual-channel correlation interferometer

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004037996A2 (en) * 2002-10-24 2004-05-06 Duke University Evaluation of breast cancer states and outcomes using gene expression profiles
CN103064096A (en) * 2012-08-21 2013-04-24 上海交通大学 Beidou navigation message extraction device and method based on blind signal processing
CN103777214A (en) * 2014-01-22 2014-05-07 中国民航大学 Method for suppressing non-stationary blanket interference signal in satellite navigation system
CN110648017A (en) * 2019-08-30 2020-01-03 广东工业大学 Short-term impact load prediction method based on two-layer decomposition technology

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004037996A2 (en) * 2002-10-24 2004-05-06 Duke University Evaluation of breast cancer states and outcomes using gene expression profiles
CN103064096A (en) * 2012-08-21 2013-04-24 上海交通大学 Beidou navigation message extraction device and method based on blind signal processing
CN103777214A (en) * 2014-01-22 2014-05-07 中国民航大学 Method for suppressing non-stationary blanket interference signal in satellite navigation system
CN110648017A (en) * 2019-08-30 2020-01-03 广东工业大学 Short-term impact load prediction method based on two-layer decomposition technology

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
YUHUAN LI等: "A Higher-Order Singular Value Decomposition-Based Radio Frequency Interference Mitigation Method on High-Frequency Surface Wave Radar", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》, vol. 58, no. 4, pages 2770, XP011780028, DOI: 10.1109/TGRS.2019.2955472 *
秦源: "基于信道差异和决策融合的欺骗干扰检测识别", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 04, pages 136 - 213 *
贾静怡有情: "非均一方差的GNSS定位误差模型检验", 《天文研究与技术》, vol. 16, no. 4, pages 437 - 445 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114563756A (en) * 2022-01-12 2022-05-31 中国电子科技集团公司第十研究所 Linear interpolation method for direction finding sample of dual-channel correlation interferometer

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