CN112946695B - Satellite positioning suppression interference identification method based on singular value decomposition - Google Patents
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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: under different satellite positioning interference suppression modes, acquiring intermediate frequency signal samples of a satellite positioning receiver, determining singular value sequences under different interference suppression modes, and carrying out numerical conversion on the singular value sequences to obtain corresponding discrete characteristic data sets; interpolation processing is carried out on the discrete feature data set to obtain an extended discrete feature data set, fitting parameter estimation is carried out on the extended discrete feature data set by adopting a Logistic function, and a mapping model between the interference mode feature vector set and the interference attribute set is established; and acquiring intermediate frequency signals of the navigation satellite in real time, calculating actual interference feature vectors, inquiring a mapping model between an interference mode feature vector set and an interference attribute set according to the actual interference feature vectors, and estimating actual suppression interference signal attributes at the current moment. The technical scheme of the invention can effectively realize detection and identification of satellite positioning suppression interference.
Description
Technical Field
The invention relates to the technical field of satellite navigation positioning, in particular to a satellite positioning suppression interference identification method based on singular value decomposition.
Background
Global navigation satellite systems (Global Navigation Satellite System, GNSS) are widely used in many traffic system applications because they can provide all-weather, real-time and high-precision positioning, navigation and timing services. Particularly, in the novel train operation control system based on vehicle-mounted centering, the GNSS satellite positioning technology is adopted to implement the perception of state information such as the position, the speed, the direction and the like of the train, so that the dependence of traditional train operation control system equipment on a track side infrastructure (such as an orbit circuit, a transponder and the like) can be effectively reduced, and the estimation and decision of the running state of the train can be realized only by using a satellite positioning receiver and a specific vehicle-mounted auxiliary sensor which are mounted on the vehicle-mounted system, so that the system is used for generating and executing a train operation control instruction.
The satellite positioning technology is used in the train speed measurement positioning process, and the vehicle-mounted antenna is required to acquire the observation information of a sufficient number of navigation satellites in real time, so that positioning calculation is completed by using specific calculation logic, and therefore, the observation quality of satellite signals has a decisive influence on the performance of positioning calculation. However, when the navigation satellite runs on the space orbit, the signal power of the transmitted satellite signal becomes very weak when the satellite signal reaches the ground receiver antenna after long-distance propagation, the navigation satellite signal adopts a broadcasting mode, the signal transmission process is directly exposed in the open space, and under the condition that the satellite navigation signal format and the data format are completely disclosed, the electromagnetic signal existing in the local environment of the receiver antenna only needs lower directional power to possibly interfere and suppress useful GNSS signals. In addition, with the rapid development and transition of economic and social forms, besides conventional unintentional signal interference, some satellite positioning suppression interference for intentional and malicious purposes further increases the safety risk of GNSS-based train positioning and operation control processes. Therefore, the 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 the safety level of satellite positioning function application.
At present, the satellite positioning interference protection technology in the prior art is mainly focused on the anti-interference optimization design of a satellite positioning terminal system, specific software and hardware modification is required to be implemented aiming at a conventional satellite positioning antenna and a receiver terminal, and the application instantaneity, complexity and cost characteristics of the satellite positioning interference protection technology are difficult to effectively meet the wide application requirements of a rail transit system. When interference analysis is implemented aiming at a possible navigation satellite interference mode, in the conventional transform domain interference detection technology, different interference detection methods are required aiming at different types of interference signals, and unified processing flow and discrimination basis are difficult; the interference detection technology based on the receiver can pertinently realize the efficient detection of specific interference by extracting the parameters after the correlator, but the difficulty of interference type estimation is increased because part of spectrum information is lost in the despreading process. In addition, in the existing interference detection analysis means, the type of the interference signal is mainly used as a processing target, and identification confirmation of specific type of interference under different interference intensities is not further involved.
Therefore, the method takes the interference type and the interference signal intensity as the target quantity for detection and identification, builds a corresponding interference characteristic model for specific application, and further takes the model as a template to realize accurate identification of satellite navigation suppression interference, thereby having important practical significance for active interference protection and flexible configuration response of positioning carriers such as trains in the real-time operation process, and the research of related methods and technologies is 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 above purpose, the present 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 under different suppression interference modes, and performing numerical transformation on the singular value sequences to obtain a corresponding discrete characteristic data set;
interpolation processing is carried out on the discrete feature data set to obtain an extended discrete feature data set, fitting parameter estimation is carried out on the extended discrete feature data set by adopting a Logistic function, and fitting parameters of different suppression interference modes are obtained;
based on fitting parameters of different suppression interference modes, constructing an interference mode feature vector, and establishing a mapping model between an interference mode feature vector set and an interference attribute set;
and acquiring intermediate frequency signals of the navigation satellite in real time, calculating an actual interference feature vector, inquiring a mapping model between the interference mode feature vector set and the interference attribute set according to the actual interference feature vector, and estimating the actual suppression interference signal attribute at the current moment.
Preferably, in the different satellite positioning suppression interference modes, the method collects original intermediate frequency signal samples of the satellite positioning receiver and constructs an offline interference signal sample library, including:
step S1.1, synchronously injecting a suppression interference signal j into the satellite positioning receiver i,p (t) generating original interference signals with different interference signal intensities in a specific satellite positioning signal observation environment by using an interference signal injection device to form a satellite positioning suppression interference mode, wherein the interference mode is represented by the following formula (1):
λ i,p ＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, lambda i,p Is a two-dimensional vector consisting of the type of the interference signal and the intensity of the interference signal;
step S1.2, acquiring original radio frequency signals R under different satellite positioning suppression interference modes by using a satellite signal acquisition terminal i,p (t) converting the original RF signal R i,p And (t) converting the signal into intermediate frequency digital signals, and forming an off-line interference signal sample library by the intermediate frequency digital signals in different satellite positioning interference suppression modes.
Preferably, the performing singular value decomposition on the collected original intermediate frequency signal sample to determine a singular value sequence in different suppression interference modes, and performing numerical transformation on the singular value sequence to obtain a corresponding discrete feature data set includes:
Step S2.1, based on the intermediate frequency digital signal obtained by the specific satellite positioning suppressing interference mode, intercepting an intermediate frequency data signal sequence X with the length of l i,p As represented by formula (2):
the intermediate frequency digital signal sequence X i,p Arranged into an n-order square matrix A i,p As represented by formula (3):
for matrix A i,p Singular value decomposition is carried out to obtain a singular value sequence W i,p As represented by formula (4):
wherein the singular value sequence W i,p Each element w of (a) k Based on A i,p ＝USV T Decomposing and extracting each diagonal element to obtain S;
step S2.2, for the singular value sequence W i,p W of the kth element of (2) k For its sequence number k and its sequence value w respectively k Doing logarithmic operation to obtain a k ＝lnk、b k ＝lnw k From a k 、b k Discrete feature data set H constituting specific satellite positioning suppression interference mode i,p K.ltoreq.n) of the above formula (5):
and step S2.3, repeating the steps S2.1 and S2.2, and traversing each satellite positioning suppression interference mode until the obtained discrete characteristic data set covers all interference signal types and interference signal intensities.
Preferably, the interpolating the discrete feature data set to obtain an extended discrete feature data set, and estimating fitting parameters of the extended discrete feature data set by using a Logistic function to obtain fitting parameters of different suppression interference modes, including:
Step S3.1, for discrete feature data set H i,p Interpolation is carried out, and a discrete characteristic data set H is removed i,p A in data k Data points > lnn-1, resulting in an extended discrete feature data set I i,p As represented by formula (6):
wherein ,(ck ,d k ) Representing the discrete feature data set H i,p C, obtaining extended discrete characteristic data after interpolation calculation k ＝a 1 +(k-1)δ，c k Lnn-1, delta represents interpolation granularity, m represents extended discrete feature data set I obtained after interpolation i,p The total number of data elements contained;
step S3.2, using Logistic function to obtain extended discrete feature data set I i,p Performing initial fitting as represented by formula (7):
wherein ,representing the general form of the Logistic function, wherein tau, gamma and eta are model parameters, phi represents a specified function space, < ->Representing the expansion of the discrete feature dataset I by initial fitting i,p The obtained Logistic model, +.>Comprising corresponding model fitting parameters-> Wherein the letter R represents the fitting result corresponding to the initial fitting;
step S3.3, setting a fitting weight setFor the extended discrete feature data set I according to the model parameters obtained by fitting i,p In c) k The magnitude of the value determines the fitting weight beta k As represented by formula (8):
wherein ,αhigh 、α low Respectively represent the fitting weights beta k Upper and lower bound characteristic values of (a).
Step S3.4, according to the obtained fitting weight set B i,p The Logistic function is adopted to obtain the extended discrete characteristic data set I i,p Performing fine fitting as represented by formula (9):
wherein ,representing the expansion of discrete feature dataset I by fine fitting i,p The obtained Logistic model is used for obtaining the Logistic model,comprising corresponding model fitting parameters->Where the letter P denotes the fitting result corresponding to the fine fitting.
Preferably, the constructing the interference pattern feature vector based on the fitting parameters of different suppression interference patterns, and establishing a mapping model between the interference pattern feature vector set and the interference attribute set includes:
s4.1, calculating a Logistic model obtained by fine fitting under different satellite positioning suppression interference modesModel value +.>Model value +.>Corresponding model parameters->Co-construction of interference pattern feature vector χ i,p As represented by formula (10):
step S4.2, repeating step 4.1, traversing each satellite positioning suppression interference mode, and collecting interference mode feature vectors obtained by different satellite positioning suppression interference modes to form an interference mode feature vector set { χ } i,p Combined with each satellite positioning suppression interference mode corresponding interference signal attribute set lambda i,p Establishing an interference mode characteristic vector set { χ } by taking an interference signal type i and an interference signal strength p as indexes i,p Set of { lambda } and interference signal properties i,p Mapping model lambda between } i,p ＝f(χ i,p ) F is (x)Mapping functions.
Preferably, the real-time acquisition of intermediate frequency signals of the navigation satellite, calculation of actual interference feature vectors, and searching of a mapping model between the interference pattern feature vector set and the interference attribute set according to the actual interference feature vectors, and estimation of actual suppression interference signal attributes at the current moment include:
step S5.1, during actual train operation, according to a given disturbance detection period T d From the current operating time t s Collected intermediate frequency digital signal sequence pointPerforming time backtracking, and intercepting a section of the starting point of +.>Endpoint +.>Is of length l, the intermediate frequency digital signal sample sequence X (t s ) As represented by formula (11):
-converting said intermediate frequency digital signal sequence X (t s ) Arranged in an n-order square matrix A (t s ) Singular value decomposition to obtain a singular value sequence W (t) s ) Based on A (t s )＝USV T Decomposing and extracting S diagonal elements to obtain W (t s ) Each element w of (a) k ；
Step S5.2, for the obtained singular value sequence W (t s ) Performing numerical transformation and logarithmic operation to form a discrete feature data set H (t s ) Extended discrete feature data set I (t s ) The obtained extended discrete feature data set I (t) is subjected to Logistic function s ) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t s ) The obtained Logistic modelCorresponding parameters of>
Step S5.3, calculating a fine fitting modelModel value +.>And corresponding model parameters->Together form t s Moment actual disturbance eigenvector χ (t) s ) As shown in formula (12):
step S5.4, according to t s Moment actual disturbance eigenvector χ (t) s ) Inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set to obtain t s Interference signal attribute estimation value λ (t s )＝f[χ(t s )]From lambda (t s ) Obtaining t s Time-of-day suppressing interference signal type i (t s ) And intensity p (t) s )。
According to the technical scheme provided by the invention, the satellite positioning suppression interference identification method based on singular value decomposition is provided, and aims to establish an interference signal singular value sequence model by utilizing relevant characteristic samples of different suppression interference signals, so that a feasible way is provided for quickly and accurately detecting and identifying the type and the intensity of satellite interference signals.
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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a satellite positioning suppression interference identification method based on singular value decomposition according to an embodiment of the present invention.
Fig. 2 is an intermediate frequency digital signal sequence captured by an intermediate frequency digital signal obtained by suppressing an interference mode based on a CWI under the condition that the 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 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. 4 is a mapping model between a CWI interference feature vector set and a CWI attribute set provided in an embodiment of the present invention.
Fig. 5 is an interference signal attribute estimation result with a test interference signal strength of p= -58dBm in a CWI suppressed interference mode according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for 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 expressly stated otherwise, as understood by those skilled in the art. 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. The term "and/or" as used herein 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 purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The embodiment of the invention provides a satellite positioning suppression interference identification method based on singular value decomposition, which aims to establish an interference signal singular value sequence model by utilizing relevant characteristic samples of different suppression interference signals, and provides a feasible way for quickly and accurately detecting and identifying the type and the intensity of the interference signals, so that effective response is made pertinently, the accuracy and the safety of train satellite positioning are improved, and the Beidou satellite navigation system is supported to play an expected role in safety-related traffic system application.
Example 1
Step S1, under different satellite positioning suppression interference modes, acquiring original intermediate frequency signal samples of a satellite positioning receiver, and constructing an off-line interference signal sample library;
s2, performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences under different suppression interference modes, and performing numerical transformation on the singular value sequences to obtain a corresponding discrete characteristic data set;
step S3, interpolation processing is carried out on the discrete feature data set to obtain an extended discrete feature data set, fitting parameter estimation is carried out on the extended discrete feature data set by adopting a Logistic function, and fitting parameters of different suppression interference modes are obtained;
step S4, based on fitting parameters of different suppression interference modes, constructing an interference mode feature vector, and establishing a mapping model between an interference mode feature vector set and an interference attribute set;
and S5, acquiring intermediate frequency signals of the navigation satellite in real time, calculating an actual interference feature vector, inquiring a mapping model between the interference mode feature vector set and the interference attribute set according to the actual interference feature vector, and estimating the actual suppression interference signal attribute at the current moment.
Preferably, step S1 specifically includes: and generating original interference signals with different interference signal strengths in a specific satellite positioning signal observation environment by using the interference signal injection equipment. The satellite positioning suppression interference mode refers to that a suppression interference signal j is synchronously injected into a satellite positioning receiver under the same satellite positioning signal observation environment i,p (t) forming a positioning hold-down interference from the signal, the interference signal having an attribute denoted lambda i,p As represented by formula (1):
λ i,p ＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, lambda i,p Is a two-dimensional vector consisting of the type of interfering signal and the strength of the interfering signal.
The method comprises the steps of collecting original radio frequency signals R under different satellite positioning suppression interference modes by using a satellite signal collecting terminal i,p And (t) converting the signal into an intermediate frequency digital signal, and forming an off-line interference signal sample library by the intermediate frequency digital signals in different satellite positioning interference suppression modes.
Preferably, step S2 specifically includes:
step S2.1, based on the intermediate frequency digital signal obtained by the specific satellite positioning suppression interference mode, intercepting the intermediate frequency data signal with the length of lNumber sequence X i,p As represented by formula (2):
the intermediate frequency digital signal sequence X is processed i,p Arranged into an n-order square matrix A i,p As represented by formula (3):
for matrix A i,p Singular value decomposition is carried out to obtain a singular value sequence W i,p As represented by formula (4):
wherein the singular value sequence W i,p Each element w of (a) k Can be based on A i,p ＝USV T And decomposing and extracting each diagonal element to obtain the S.
S2.2, suppressing the singular value sequence W obtained by the interference mode for the specific satellite positioning i,p Performing numerical transformation, and performing logarithmic operation on the serial number and the sequence value, namely: for singular value sequences W i,p W of the kth element of (2) k For its sequence number k and its sequence value w respectively k Doing logarithmic operation to obtain a k ＝lnk、b k ＝lnw k From a k 、b k Discrete feature data set H constituting specific satellite positioning suppression interference mode i,p K.ltoreq.n) of the above formula (5):
and step S2.3, repeating the steps S2.1 and S2.2, and traversing each satellite positioning suppression interference mode until the obtained discrete characteristic data set covers all interference signal types and interference signal intensities.
Preferably, the step S3 includes:
step S3.1, for the obtained discrete feature data set H i,p Interpolation is carried out, H is removed i,p A in data k Data points > lnn-1, resulting in an extended discrete feature data set I i,p As represented by formula (6):
wherein ,(ck ,d k ) Representing the discrete feature data set H i,p C, obtaining extended discrete characteristic data after interpolation calculation k ＝a 1 +(k-1)δ，c k Lnn-1, delta represents interpolation granularity, m represents extended discrete feature data set I obtained after interpolation i,p Total number of data elements contained.
Step S3.2, using Logistic function to obtain extended discrete feature data set I i,p Performing initial fitting, wherein the principle is represented by a formula (7):
wherein ,representing the general form of the Logistic function, wherein tau, gamma and eta are model parameters, phi represents a specified function space, < ->Representing the expansion of the discrete feature dataset I by initial fitting i,p The obtained Logistic model, +.>Comprising corresponding model fitting parameters->Wherein the letter R represents a letter corresponding to the primeFitting results.
Step S3.3, setting a fitting weight setFor the extended discrete feature data set I according to the model parameters obtained by fitting i,p In c) k The magnitude of the value determines the fitting weight beta k The main value principle is expressed as formula (8):
wherein ,αhigh 、α low Respectively represent the fitting weights beta k Upper and lower bound characteristic values of (a).
Step S3.4, according to the obtained fitting weight set B i,p The Logistic function is adopted to obtain the extended discrete characteristic data set I i,p Performing fine fitting as represented by formula (9):
wherein ,representing the expansion of discrete feature dataset I by fine fitting i,p The obtained Logistic model is used for obtaining the Logistic model,comprising corresponding model fitting parameters->Where the letter P denotes the fitting result corresponding to the fine fitting.
Preferably, the step S4 includes:
s4.1, calculating a Logistic model obtained by fine fitting under different satellite positioning suppression interference modesModel value +.>Associating it with the corresponding model parameters +.>Co-construction of interference pattern feature vector χ i,p As represented by formula (10):
step S4.2, repeating step 4.1, traversing each satellite positioning suppression interference mode, and collecting interference mode feature vectors obtained by different satellite positioning suppression interference modes to form an interference mode feature vector set { χ } i,p Combined with each satellite positioning suppression interference mode corresponding interference signal attribute set lambda i,p Establishing an interference mode characteristic vector set { χ } by taking an interference signal type i and an interference signal strength p as indexes i,p Set of { lambda } and interference signal properties i,p Mapping model lambda between } i,p ＝f(χ i,p ) F is a mapping function.
Preferably, the step S5 includes:
step S5.1, during actual train operation, according to a given disturbance detection period T d Extracting an intermediate frequency digital signal X (t) of a vehicle-mounted satellite signal acquisition device with a sequence length of l by a fixed time window s ) I.e. from the current operating time t s Collected intermediate frequency digital signal sequence pointPerforming time backtracking, and intercepting a section of the starting point of +.>Endpoint +.>A sequence of intermediate frequency digital signal samples of length l, as represented by formula (11):
the intermediate frequency digital signal sequence X (t s ) Arranged in an n-order square matrix A (t s ) Singular value decomposition to obtain a singular value sequence W (t) s ) Based on A (t s )＝USV T Decomposing and extracting each diagonal element of S to obtain W (t s ) Each element w of (a) k 。
Step S5.2, using a procedure similar to that described in steps S2, S3, for the sequence of singular values W (t s ) Performing numerical transformation and logarithmic operation to form a discrete feature data set H (t s ) Extended discrete feature data set I (t s ) The obtained extended discrete feature data set I (t) is subjected to Logistic function s ) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t s ) The obtained Logistic modelCorresponding parameters of>
Step S5.3, calculating a fine fitting modelModel value +.>And corresponding model parameters->Together form t s Moment actual disturbance eigenvector χ (t) s ) As shown in formula (12):
step S5.4, calling a mapping model f between the interference mode feature vector set and the interference attribute set obtained in step S4.2 based on t s Moment actual disturbance eigenvector χ (t) s ) Obtaining t s Interference signal attribute estimation value λ (t s )＝f[χ(t s )]From lambda (t s ) Obtaining t s Time-of-day suppressing interference signal type i (t s ) And intensity p (t) s )。
Example two
The specific processing flow of the satellite positioning suppression interference identification method based on singular value decomposition provided in the embodiment is shown in fig. 1, and includes the following steps:
step S1, under different satellite positioning suppression interference modes, original intermediate frequency signal samples of a satellite positioning receiver are collected, and an off-line interference signal sample library is constructed.
And S2, performing singular value decomposition on the acquired original intermediate frequency signal sample, determining singular value sequences under different suppression interference modes, and performing numerical conversion on the singular value sequences to obtain a corresponding discrete characteristic data set.
And S3, carrying out interpolation processing on the discrete feature data set to obtain an extended discrete feature data set, and carrying out fitting parameter estimation on the obtained extended discrete feature data set by adopting a Logistic function to obtain fitting parameters of different suppression interference modes.
And S4, constructing an interference pattern feature vector based on fitting parameters of different suppression interference patterns, and establishing a mapping model between an interference pattern feature vector set and an interference attribute set.
And S5, acquiring the intermediate frequency signals of the navigation satellite in real time in the actual running process, extracting a sample sequence in the instant window based on the fixed time window, and calculating the actual interference feature vector. And inquiring a mapping model between the interference mode feature vector set and the interference attribute set according to the actual interference feature vector, and estimating the actual suppression interference signal attribute at the current moment.
Step S1 further comprises the sub-steps of:
substep S1.1, taking a single-frequency interference signal (CWI) suppression interference mode as an example, the present embodiment synchronously injects a CWI signal j into a satellite positioning receiver under the same satellite positioning signal observation environment CW,Ip (t) forming a positioning hold-down interference from the signal, the interference signal having an attribute denoted lambda CWI,p As represented by formula (13):
λ CWI,p ＝[CWI,p] (13)
wherein: the character CWI represents the type of interference signal as CWI signal, p as interference signal strength, lambda CWI,p Is a two-dimensional vector consisting of the type of interfering signal and the strength of the interfering signal.
In a sub-step S1.2,
taking a single-frequency interference signal (CWI) suppression interference mode as an example, a satellite signal acquisition terminal is utilized to acquire original radio frequency signals R under different interference intensities in the CWI satellite positioning suppression interference mode CWI,p (t) converting the original RF signal R CWI,p And (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 a CWI satellite positioning interference suppression mode.
Step S2 further comprises the sub-steps of:
substep S2.1, intercepting an intermediate frequency digital signal obtained based on a CWI satellite positioning suppression interference mode, with a length of l=1×10 6 Intermediate frequency data signal sequence X of (2) CWI,p As represented by formula (14):
sequence X at different suppression interference intensity levels CWI,p Original basic information is provided for constructing an interference mode feature vector mapping model, and in the embodiment, an intermediate frequency digital signal sequence X obtained by suppressing an intermediate frequency digital signal obtained by an interference mode based on CWI with the interference intensity of-70 dBm is used for intercepting the intermediate frequency digital signal CWI,-70 As shown in fig. 2.
The intermediate frequency digital signal sequence X is processed CWI,p Arranged into an n-order square matrix A CWI,p As represented by formula (15):
for matrix A CWI,p Singular value decomposition is carried out to obtain a singular value sequence W CWI,p As represented by formula (16):
wherein the singular value sequence W CWI,p Each element w of (a) k Can be based on A CWI,p ＝USV T And decomposing and extracting each diagonal element to obtain the S.
Sub-step S2.2 for singular value sequence W CWI,p And carrying out numerical transformation to obtain a corresponding discrete characteristic data set. Singular value sequence W obtained by suppressing interference mode for CWI satellite positioning CWI,p Performing numerical transformation, and performing logarithmic operation on the serial number and the sequence value, namely: for singular value sequences W CWI,p W of the kth element of (2) k For its sequence number k and its sequence value w respectively k Doing logarithmic operation to obtain a k ＝lnk、b k ＝lnw k From a k 、b k Discrete feature data set H for forming CWI satellite positioning suppression interference mode CW,Ip K.ltoreq.n) of the above formula (17):
FIG. 3 shows a discrete feature data set H obtained by suppressing the interference pattern based on the CWI under the condition of the interference signal strength of-70 dBm in the present embodiment CWI,-70 。
Step S3 further comprises the sub-steps of:
sub-step S3.1, for the resulting discrete feature data set H CWI,p Interpolation is carried out, in this embodiment, a linear interpolation method is adopted, and the interpolation calculation process is shown as formula (18):
on the basis, H is further removed CWI,p A in data k Data points > lnn-1, resulting in an extended discrete feature data set I CWI,p As represented by formula (19):
wherein ,(ck ,d k ) Representing the discrete feature data set H CWI,p Extended discrete feature data obtained after interpolation calculation, m represents an extended discrete feature data set I obtained after interpolation CWI,p The total number of data elements contained, interpolation granularity, delta=0.1.
Sub-step S3.2, applying Logistic function to the obtained extended discrete feature dataset I CWI,p Performing initial fitting, wherein the principle is represented by a formula (20):
wherein ,representing the general form of the Logistic function, wherein tau, gamma and eta are model parameters, phi represents a specified function space, < ->Representing the expansion of the discrete feature dataset I by initial fitting CWI,p The obtained Logistic model, +.>Comprising corresponding model fitting parameters-> Where the letter R indicates the fitting result corresponding to the initial fitting.
Sub-step S3.3, setting a fitting weight setFor the extended discrete feature data set I according to the model parameters obtained by fitting CWI,p In c) k The magnitude of the value determines the fitting weight beta k The main value principle is expressed as a formula (21): />
Substep S3.4, according to the obtained fitting weight set B CWI,p The Logistic function is adopted to obtain the extended discrete characteristic data set I CWI,p Performing a fine fit as represented by formula (22):
wherein ,representing the expansion of discrete feature dataset I by fine fitting CWI,p The obtained Logistic model is used for obtaining the Logistic model,comprising corresponding model fitting parameters->Where the letter P denotes the fitting result corresponding to the fine fitting.
Step S4 further comprises the sub-steps of:
sub-step S4.1, calculating the Logistic model obtained by fine fittingModel value +.>Associating it with the corresponding model parameters +.>Co-construction of interference pattern feature vector χ CWI,p As represented by formula (23):
sub-step S4.2, repeating sub-step S4.1, traversing different interference signal intensities under CWI suppression interference mode, enabling the constructed interference mode feature vector to cover all interference signal intensity levels, and summarizing the obtained interference mode feature vector to form a CWI interference feature vector set { χ } CWI,p CWI satellite positioning suppression interference mode corresponding CWI attribute set { lambda } with different joint interference signal strengths CWI,p Establishing a CWI interference feature vector set { χ } by indexing the unique interference signal type CWI, the plurality of interference signal strengths p CWI,p { lambda } and CWI attribute sets CWI,p Mapping model |lambda between } CWI,p ＝f(χ CWI,p ) F is a mapping function.
Preferably, the CWI interference feature vector set { χ } in the present embodiment CWI,p { lambda } and CWI attribute sets CWI,p The mapping model f construction process between } is as follows:
For two-dimensional vector [ phi ] 1 ,φ 2 ]If there is adjacentSatisfy->The mapping model f is shown in equation (24):
f(χ)＝λ，χ＝[φ 1 ,φ 2 ],λ＝[φ 3 ,φ 4 ] (24)
wherein ,φ3 、φ 4 the two characteristic quantities respectively representing the type and the intensity of the interference signal are determined according to the following formulas (25) and (26):
wherein the boundary parameter κ L and κH As shown in formulas (27), (28):
/>
wherein ,take->
FIG. 4 shows a CWI disturbance feature vector set { χ }, established as described above CWI,p { lambda } and CWI attribute sets CWI,p Mapping model lambda between } CWI,p ＝f(χ CWI,p ) Among them, fig. 4 shows the results of 9 cases where the interference signal strength is in the range of-90 dBm to-50 dBm in the CWI suppressed interference mode.
Step S5 further comprises the sub-steps of:
sub-step S5.1, during actual train operation, according to a given disturbance detection period T d Extracting a sequence length of l=1×10 in a fixed time window 6 Intermediate frequency digital signal X (t) of on-vehicle satellite signal acquisition device s ) I.e. from the current run timet s Collected intermediate frequency digital signal sequence pointPerforming time backtracking, and intercepting a section of the starting point of +.>Endpoint +.>Is l=1×10 in length 6 As represented by formula (29):
the intermediate frequency digital signal sequence X (t s ) Arranged in an n-order square matrix A (t s ) As represented by formula (30):
For matrix A (t s ) Singular value decomposition is performed to obtain a singular value sequence W (t s ) As represented by formula (31):
wherein the singular value sequence W (t s ) Each element w of (a) k Can be based on A (t s )＝USV T And decomposing and extracting each diagonal element to obtain the S.
Substep S5.2, for the obtained sequence of singular values W (t s ) Performing numerical transformation, and performing logarithmic operation on the serial number and the sequence value, namely: for a sequence of singular values W (t s ) W of the kth element of (2) k For its sequence number k and its sequence value w respectively k Doing logarithmic operation to obtain a k ＝lnk、b k ＝lnw k From a k 、b k Composition t s Discrete feature data set H (t s ) K.ltoreq.n) of the above formula (32):
the present embodiment uses linear interpolation for the discrete feature dataset H (t s ) Interpolation is carried out, and the interpolation calculation method is shown as a formula (33):
on the basis, H (t) s ) A in data k Data of > lnn-1, resulting in an extended discrete feature data set I (t s ) As represented by formula (34):
/>
wherein ,(ck ,d k ) Representing the discrete feature data set H (t s ) C, obtaining extended discrete characteristic data after interpolation calculation k ＝a 1 +(k-1)d，c k And (2) ln (n-1), m represents the extended discrete feature data set I (t) obtained after interpolation s ) The total number of data elements contained, interpolation granularity, delta=0.1.
The obtained extended discrete feature data set I (t) is subjected to Logistic function s ) Performing initial fitting, wherein the principle is represented by a formula (35):
wherein ,representing the general form of the Logistic function, wherein tau, gamma and eta are model parameters, phi represents a specified function space, < ->Representing the expansion of the discrete feature dataset I (t) s ) The obtained Logistic model, +.>Comprising corresponding model fitting parameters-> Where the letter R indicates the fitting result corresponding to the initial fitting.
Setting fitting weight setFor the extended discrete feature dataset I (t s ) In c) k The magnitude of the value determines the fitting weight beta k The main value principle is expressed as a formula (36):
from the resulting fitting weight set B (t s ) The obtained extended discrete feature data set I (t) is subjected to Logistic function s ) Performing a fine fit as represented by formula (37):
wherein ,representing the expansion of the discrete feature dataset I (t) s ) The obtained Logistic model is used for obtaining the Logistic model,comprising corresponding model fitting parameters->Where the letter P denotes the fitting result corresponding to the fine fitting.
Sub-step S5.3, computing Logistic model obtained by fine fittingModel value +.>Corresponding model parameters->Together form->Moment actual disturbance eigenvector χ (t) s ) As shown in formula (38):
sub-step S5.4, calling a mapping model f between the interference pattern feature vector set and the interference attribute set obtained in sub-step S4.2 based on t s Moment actual disturbance eigenvector χ (t) s ) Obtaining t s Interference signal attribute estimation value λ (t s )＝f[χ(t s )]From lambda (t s ) Obtaining t s Time-of-day suppressing interference signal type i (t s ) And intensity p (t) s ). Fig. 5 shows the result of estimating the attribute of the interference signal with the test interference signal strength p= -58dBm in the CWI suppressed interference mode in this embodiment, and the result correctly identifies the corresponding signal strength level.
In summary, the invention is directed to the requirement of satellite positioning application on safety, and provides a satellite positioning suppression interference identification method based on singular value decomposition. From the core ideas, main processes and implementation modes of the method provided by the invention, the method has definite low-cost characteristics, implementation convenience and application adaptability, and can achieve excellent interference feature coverage and recognition performance by combining a large number of satellite positioning suppression interference pattern samples.
The satellite positioning suppression interference identification method 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 superimposes corresponding suppression interference monitoring functions on the basis of autonomous fault detection diagnosis implemented by the traditional satellite positioning receiver terminal, so that additional interference defense reinforcement is provided for mobile positioning of carriers based on GNSS, active monitoring, early warning and calibration approaches of a satellite positioning interference feature library are provided for complex road network and rail transit network environments, and a better information security guarantee mechanism is created for specific satellite positioning application.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The satellite positioning suppression interference identification method based on singular value decomposition is characterized by comprising the following steps of:
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 under different suppression interference modes, and performing numerical transformation on the singular value sequences to obtain a corresponding discrete characteristic data set;
interpolation processing is carried out on the discrete feature data set to obtain an extended discrete feature data set, fitting parameter estimation is carried out on the extended discrete feature data set by adopting a Logistic function, and fitting parameters of different suppression interference modes are obtained;
based on fitting parameters of different suppression interference modes, constructing an interference mode feature vector, and establishing a mapping model between an interference mode feature vector set and an interference attribute set;
acquiring intermediate frequency signals of a navigation satellite in real time, calculating actual interference feature vectors, inquiring a mapping model between the interference mode feature vector set and the interference attribute set according to the actual interference feature vectors, and estimating actual suppression interference signal attributes at the current moment;
The real-time acquisition of the intermediate frequency signals of the navigation satellite, the calculation of the actual interference feature vector, the inquiry of the mapping model between the interference mode feature vector set and the interference attribute set according to the actual interference feature vector, the estimation of the actual suppression interference signal attribute at the current moment, comprises the following steps:
step S5.1, during actual train operation, according to a given disturbance detection period T d From the current operating time t s Collected intermediate frequency digital signal sequence pointPerforming time backtracking, and intercepting a section of the starting point of +.>Endpoint +.>Is of length l, the intermediate frequency digital signal sample sequence X (t s ) As represented by formula (11):
-converting said sequence of intermediate frequency digital signal samples X (t s ) Arranged in an n-order square matrix A (t s ) Through singular value decompositionTo the singular value sequence W (t s ) Based on A (t s )＝USV T Decomposing and extracting S diagonal elements to obtain W (t s ) Each element w of (a) k ；
Step S5.2, for the obtained singular value sequence W (t s ) Performing numerical transformation and logarithmic operation to form a discrete feature data set H (t s ) Extended discrete feature data set I (t s ) The obtained extended discrete feature data set I (t) is subjected to Logistic function s ) Performing initial fitting and fine fitting to determine a fine fitting extended discrete feature data set I (t s ) The obtained Logistic modelCorresponding parameters of>
Step S5.3, calculating a fine fitting modelModel value +.>And corresponding model parameters->Together form t s Moment actual disturbance eigenvector χ (t) s ) As shown in formula (12):
step S5.4, according to t s Moment actual disturbance eigenvector χ (t) s ) Inquiring a mapping model between the interference mode characteristic vector set and the interference attribute set to obtain t s Interference signal attribute estimation value λ (t s )＝f[χ(t s )]From lambda (t s ) Obtaining t s Time-of-day suppressing interference signal type i (t s ) And intensity p (t) s )。
2. The method of claim 1, wherein the step of collecting original intermediate frequency signal samples of the satellite positioning receiver in different satellite positioning hold-down interference modes and constructing an offline interference signal sample library comprises:
step S1.1, synchronously injecting a suppression interference signal j into the satellite positioning receiver i,p (t) generating original interference signals with different interference signal intensities in a specific satellite positioning signal observation environment by using an interference signal injection device to form a satellite positioning suppression interference mode, wherein the interference mode is represented by the following formula (1):
λ i,p ＝[i,p] (1)
wherein: i represents the interference signal type, p is the interference signal strength, lambda i,p Is a two-dimensional vector consisting of the type of the interference signal and the intensity of the interference signal;
Step S1.2, acquiring original radio frequency signals R under different satellite positioning suppression interference modes by using a satellite signal acquisition terminal i,p (t) converting the original RF signal R i,p And (t) converting the signal into intermediate frequency digital signals, and forming an off-line interference signal sample library by the intermediate frequency digital signals in different satellite positioning interference suppression modes.
3. The method according to claim 1, wherein said performing singular value decomposition on the collected original intermediate frequency signal samples, determining a sequence of singular values in different suppression interference modes, and performing numerical transformation on the sequence of singular values to obtain a corresponding discrete feature data set, comprises:
step S2.1, based on the intermediate frequency digital signal obtained by the specific satellite positioning suppressing interference mode, intercepting an intermediate frequency data signal sequence X with the length of l i,p As represented by formula (2):
the intermediate frequency digital signal sequence X i,p Arranged into an n-order square matrix A i,p As represented by formula (3):
for matrix A i,p Singular value decomposition is carried out to obtain a singular value sequence W i,p As represented by formula (4):
wherein the singular value sequence W i,p Each element w of (a) k Based on A i,p ＝USV T Decomposing and extracting each diagonal element to obtain S;
step S2.2, for the singular value sequence W i,p W of the kth element of (2) k For its sequence number k and its sequence value w respectively k Doing logarithmic operation to obtain a k ＝ln k、b k ＝ln w k From a k 、b k Discrete feature data set H constituting specific satellite positioning suppression interference mode i,p K.ltoreq.n, as represented by the formula (5):
and step S2.3, repeating the steps S2.1 and S2.2, and traversing each satellite positioning suppression interference mode until the obtained discrete characteristic data set covers all interference signal types and interference signal intensities.
4. The method of 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 suppression interference modes, includes:
step S3.1, for discrete feature data set H i,p Interpolation is carried out, and a discrete characteristic data set H is removed i,p A in data k Data points > lnn-1, resulting in an extended discrete feature data set I i,p As represented by formula (6):
wherein ,(ck ,d k ) Representing the discrete feature data set H i,p C, obtaining extended discrete characteristic data after interpolation calculation k ＝a 1 +(k-1)δ，c k Not more than ln n-1, delta represents interpolation granularity, m represents extended discrete feature data set I obtained after interpolation i,p The total number of data elements contained;
step S3.2, using Logistic function to obtain extended discrete feature data set I i,p Performing initial fitting as represented by formula (7):
wherein ,representing the general form of the Logistic function, wherein tau, gamma and eta are model parameters, phi represents a specified function space, < ->Representing the expansion of the discrete feature dataset I by initial fitting i,p The obtained Logistic model is used for obtaining the Logistic model,comprising corresponding model fitting parameters-> Wherein the letter R represents the fitting result corresponding to the initial fitting;
step S3.3, setting a fitting weight setFor the extended discrete feature data set I according to the model parameters obtained by fitting i,p In c) k The magnitude of the value determines the fitting weight beta k As represented by formula (8):
wherein ,αhigh 、α low Respectively represent the fitting weights beta k Upper and lower boundary feature values of (a);
step S3.4, according to the obtained fitting weight set B i,p The Logistic function is adopted to obtain the extended discrete characteristic data set I i,p Performing fine fitting as represented by formula (9):
wherein ,representing the expansion of discrete feature dataset I by fine fitting i,p The obtained Logistic model, +.>Comprising corresponding model fitting parameters->Where the letter P denotes the fitting result corresponding to the fine fitting.
5. The method of claim 1, wherein the constructing the interference pattern feature vector based on fitting parameters of different suppression interference patterns, and the mapping model between the interference pattern feature vector set and the interference attribute set, comprises:
Step S4.1Calculating Logistic model obtained by fine fitting under different satellite positioning suppression interference modesModel value +.>Model value +.>Corresponding model parameters->Co-construction of interference pattern feature vector χ i,p As represented by formula (10):
step S4.2, repeating step 4.1, traversing each satellite positioning suppression interference mode, and collecting interference mode feature vectors obtained by different satellite positioning suppression interference modes to form an interference mode feature vector set { χ } i,p Combined with each satellite positioning suppression interference mode corresponding interference signal attribute set lambda i,p Establishing an interference mode characteristic vector set { χ } by taking an interference signal type i and an interference signal strength p as indexes i,p Set of { lambda } and interference signal properties i,p Mapping model lambda between } i,p ＝f(χ i,p ) F is a mapping function.
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Citations (4)
| 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 |
-
2021
- 2021-03-01 CN CN202110224899.5A patent/CN112946695B/en active Active
Patent Citations (4)
| 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)
| 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》.2019,第58卷(第4期),第2770 - 2781页. * |
| 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》.2017,(第04期),第I136-213页. * |
| 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》.2019,第16卷(第4期),第437-445页. * |
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