CN117590429B - L-shaped array-based multi-spoofing signal incoming wave direction detection method and system - Google Patents
L-shaped array-based multi-spoofing signal incoming wave direction detection method and system Download PDFInfo
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Abstract
The invention provides a method and a system for detecting incoming wave directions of multiple deception signals based on an L-shaped array, wherein the method comprises the steps of collecting navigation signals in a deception interference environment in real time through the L-shaped array to obtain a to-be-processed received signal; performing direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain a current signal source azimuth information estimation value; and tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value. The method can simply, efficiently and accurately detect the incoming wave directions of the plurality of deception signals, and simultaneously accurately track and distinguish the plurality of deception signals with crossing pitch angle and azimuth angle tracks, thereby effectively improving the application effect of the satellite navigation technology.
Description
Technical Field
The invention relates to the technical field of signal detection, in particular to a method and a system for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array.
Background
With the continuous development and perfection of satellite navigation technology, GNSS technology has penetrated into various aspects of people's life, such as GNSS has exerted unique value in aspects of logistics transportation, autopilot, high-precision mapping, geographic investigation, weather prediction, accurate time service, and the like, and accordingly, higher requirements on GNSS positioning accuracy and reliability have also been put forward. However, with the complexity of the use environment and the enhancement of the anti-jamming technology, the GNSS is very prone to generate erroneous positioning results, so that the anti-jamming technology research on the GNSS is very important.
Although the existing deception signal detection scheme realizes detection of certain deception signal scenes to a certain extent, the existing deception signal detection scheme has certain application defects in the aspects of detection accuracy, application convenience and use cost, and can not simultaneously distinguish and track a plurality of deception signals, for example, an observation value mutation is used as a signal intensity detection method of deception interference signals by monitoring signal intensity of an antenna array in real time, effects are lost when the deception signals have relatively stable SNR by an interference party, and false alarm rate is high due to the influence of multipath, antenna types and the like; the signal absolute power detection method for monitoring and screening whether the signal contains deception interference or not by utilizing different characteristics of the signal absolute power is characterized in that a special hardware device is added at the front end of the radio frequency; the method for detecting the residual signal by reconstructing the deception jamming signal and removing the deception jamming signal from the receiving signal requires higher hardware cost to support additional special channels and storage space; and forming a multi-antenna array model by the mobile receiver, and requiring random movement of the receiver antenna in a synthetic array spoofing detection method for using signals with higher correlation degree than the identification received signals as spoofing interference signals, which is troublesome.
Disclosure of Invention
The invention aims to provide a multi-spoofing signal incoming wave direction detection method based on an L-shaped array, which is characterized in that azimuth information of different spoofing signals is obtained by using the L-shaped array, and then tracking detection is carried out on the multiple spoofing signals by combining Kalman filtering using a joint probability data interconnection algorithm, so that the application defect of the existing spoofing signal detection scheme is overcome, the multiple spoofing signal incoming wave directions can be simply, efficiently and accurately detected, meanwhile, the multiple spoofing signals with crossing pitch angles and azimuth tracks can be accurately tracked and distinguished, and the application effect of a satellite navigation technology is effectively improved.
In order to achieve the above object, it is necessary to provide a method, a system, a computer device, and a storage medium for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array.
In a first aspect, an embodiment of the present invention provides a method for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array, where the method includes the following steps:
acquiring navigation signals in a deception jamming environment in real time through an L-shaped array to obtain a to-be-processed received signal; the to-be-processed receiving signals comprise an X-axis receiving signal and a Z-axis receiving signal;
Performing direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain a current signal source azimuth information estimation value; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle;
and tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value.
Further, the L-shaped array comprises a first uniform linear array and a second uniform linear array which are mutually orthogonal in a xoz plane; the first uniform linear array and the second uniform linear array comprise a plurality of array elements which are distributed at equal intervals; the common reference array element of the L-shaped array is arranged at the original point where the first uniform linear array and the second uniform linear array intersect.
Further, the received signal to be processed is expressed as:
in the method, in the process of the invention,
s(n)＝[s1(n),s2(n),L,sK(n)]T
A(φ)＝[a(φ1),a(φ2),L,a(φK)]
A(θ)＝[a(θ1),a(θ2),L,a(θK)]
Wherein X (n) and Z (n) represent an X-axis reception signal and a Z-axis reception signal, respectively; s (n) represents an incident signal vector; a (phi) and w x (n) respectively represent the flow pattern matrix of the X-axis and the corresponding noise vector; a (θ) and w z (n) represent the flow pattern matrix and corresponding noise vector, respectively, of the Z-axis; k represents the total number of incident signals; θ i and φ i represent the pitch angle and azimuth angle, respectively, of the ith spoof signal; [] T denotes a transpose.
Further, the spoofing jamming signal model is expressed as:
wherein s (n) represents an incident signal vector; sate n,m denotes the mth real satellite signal in the nth direction; spoof n,p denotes the nth spoofing signal in the nth direction; m and P represent the total number of true satellite signals and the total number of rogue signals in the nth direction, respectively.
Further, the step of estimating the direction of arrival of the received signal to be processed according to the preset spoofing interference signal model to obtain the current signal source azimuth information estimated value includes:
obtaining a received signal covariance matrix according to the received signal to be processed;
decomposing the covariance matrix of the received signals to obtain a corresponding signal subspace and a corresponding noise subspace;
obtaining a corresponding MUSIC spatial spectrum according to the signal subspace and the noise subspace;
and carrying out spectral peak search on the MUSIC spatial spectrum to obtain the current signal source azimuth information estimated value.
Further, the step of tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-time spoofing signal azimuth information estimated value comprises the following steps:
according to the shape of the preset correlation gate, respectively centering on the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source, and establishing a corresponding spoofing signal correlation gate;
Tracking and measuring by adopting a Kalman filtering algorithm according to the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source to obtain corresponding spoofing signal measurement data;
Obtaining a corresponding deception signal confirmation matrix according to the deception signal measurement data, the deception signal association gate and a basic assumption of a joint probability data interconnection algorithm;
obtaining association probability according to the deception signal confirmation matrix, and carrying out signal association probability calculation on each deception signal measurement data according to the association probability to obtain corresponding deception signal measurement probability;
And calculating a corresponding next-time spoofing signal azimuth information estimated value according to the spoofing signal measurement probability.
Further, the next-time spoofing signal azimuth information estimated value is expressed as:
Wherein, An azimuth information estimated value at the next time of the t-th spoofing signal at the k time is represented; m (k) represents the total number of spoofing signal measurement data at time k; beta jt represents the associated probability of the measurement data j at the time k and the spoofing signal t; And the azimuth information of the t-th deception signal in the k-moment measurement data j is shown.
In a second aspect, an embodiment of the present invention provides a system for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array, the system comprising:
The signal receiving module is used for acquiring navigation signals in the deception jamming environment in real time through the L-shaped array to obtain a receiving signal to be processed; the to-be-processed receiving signals comprise an X-axis receiving signal and a Z-axis receiving signal;
The azimuth estimation module is used for carrying out direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain an estimated value of azimuth information of a current signal source; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle;
And the signal tracking module is used for tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value.
In a third aspect, embodiments of the present invention further provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.
The application provides a method, a system, a computer device and a storage medium for detecting incoming wave directions of multiple deception signals based on an L-shaped array, which are used for realizing the technical scheme that navigation signals in a deception interference environment are collected in real time through the L-shaped array to obtain a to-be-processed received signal comprising an X-axis received signal and a Z-axis received signal, the direction of arrival estimation is carried out on the to-be-processed received signal according to a preset deception interference signal model to obtain a current signal source azimuth information estimated value comprising a pitch angle and an azimuth angle of a real satellite signal and the deception signal, and a joint probability data interconnection algorithm and a Kalman filtering algorithm are adopted to track and measure each deception signal according to the current signal source azimuth information estimated value to obtain a corresponding next-moment deception signal azimuth information estimated value. Compared with the prior art, the L-shaped array-based multi-deception signal incoming wave direction detection method obtains azimuth information of different deception signals by using the L-shaped array, and then carries out tracking detection on the deception signals by combining Kalman filtering using a joint probability data interconnection algorithm, so that the method can simply, efficiently and accurately detect the incoming wave directions of the deception signals, and simultaneously accurately track and distinguish the deception signals crossing pitch angle and azimuth angle tracks, and further effectively improve the application effect of satellite navigation technology.
Drawings
Fig. 1 is a schematic diagram of an application scenario of a multi-spoofing signal incoming wave direction detection method based on an L-shaped array in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array in an embodiment of the invention;
FIG. 3 is a schematic diagram of an L-array for receiving signals according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the use of an oval correlation gate in an embodiment of the invention;
FIG. 5 is a schematic diagram of a system for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array in accordance with an embodiment of the present invention;
fig. 6 is an internal structural view of a computer device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantageous effects of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples, and it is apparent that the examples described below are part of the examples of the present application, which are provided for illustration only and are not intended to limit the scope of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The L-shaped array-based multi-spoofing signal incoming wave direction detection method provided by the invention can be understood as a spoofing signal detection method for accurately and efficiently tracking a plurality of simultaneous spoofing signals by combining a Kalman filtering algorithm introducing a joint probability data interconnection algorithm after constructing a received signal model based on an L-shaped array model and combining a preset spoofing interference signal model and carrying out two-dimensional DOA (Direction of arrival) estimation on the pitch angle and the azimuth angle of the spoofing signals based on the received signal model, and can be applied to a terminal or a server shown in figure 1. The terminal may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers and portable wearable devices, and the server may be implemented by a separate server or a server cluster formed by a plurality of servers. The server can carry out efficient and accurate multi-spoofing signal measurement tracking by adopting the L-shaped array-based multi-spoofing signal incoming wave direction detection method framework according to actual application requirements, and the obtained tracking result is used for subsequent research of the server or is transmitted to a terminal for a terminal user to check and analyze; the following embodiments will explain in detail a multi-spoof signal incoming wave direction detecting method based on an L-type array of the present invention.
In one embodiment, as shown in fig. 2, there is provided a method for detecting incoming wave directions of multiple spoofing signals based on an L-shaped array, comprising the steps of:
S11, acquiring navigation signals in a deception jamming environment in real time through an L-shaped array to obtain a to-be-processed receiving signal; wherein the L-shaped array is shown in FIG. 3 and comprises a first uniform linear array and a second uniform linear array which are mutually orthogonal in a xoz plane; the first uniform linear array and the second uniform linear array comprise a plurality of array elements which are distributed at equal intervals; the common reference array element of the L-shaped array is arranged at the original point where the first uniform linear array and the second uniform linear array intersect; correspondingly, the navigation signal (to-be-processed received signal) with interference received by the L-shaped array comprises an X-axis received signal and a Z-axis received signal; it should be noted that, the received signal to be processed in this embodiment may be understood as a navigation signal obtained by performing corresponding preprocessing on an original received signal according to an application requirement, and a specific preprocessing method is not limited herein; the received signal to be processed acquired through the L-shaped array real-time acquisition can be expressed as:
in the method, in the process of the invention,
s(n)＝[s1(n),s2(n),L,sK(n)]T
A(φ)＝[a(φ1),a(φ2),L,a(φK)]
A(θ)＝[a(θ1),a(θ2),L,a(θK)]
Wherein X (n) and Z (n) represent an X-axis reception signal and a Z-axis reception signal, respectively; s (n) represents an incident signal vector; a (phi) and w x (n) respectively represent the flow pattern matrix of the X-axis and the corresponding noise vector; a (θ) and w z (n) represent the flow pattern matrix and corresponding noise vector, respectively, of the Z-axis; k represents the total number of incident signals; θ i and φ i represent the pitch angle and azimuth angle, respectively, of the ith spoof signal; [] T denotes a transpose; it should be noted that, in FIG. 3Representing the projection of the ith spoofing signal on the xoy plane;
The array model of the received signals adopted in the embodiment is expanded from a uniform linear array model to an L-shaped array model, DOA estimation can be carried out on the azimuth angle and the pitch angle simultaneously through a simple structure, and estimation performance can be effectively improved.
S12, estimating the direction of arrival of the received signal to be processed according to a preset deception jamming signal model to obtain a current signal source azimuth information estimated value; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle; the spoofing interference signal model may be understood as an incident signal model in a navigation environment where spoofing interference exists, and in this embodiment, an application scenario where multiple spoofing signals exist simultaneously is considered, and a corresponding incident signal vector is expressed as:
wherein s (n) represents an incident signal vector; sate n,m denotes the mth real satellite signal in the nth direction; spoof n,p denotes the nth spoofing signal in the nth direction; m and P represent the total number of true satellite signals and the total number of rogue signals in the nth direction, respectively.
It should be noted that, after s (n) in the to-be-processed received signal acquired through the L-shaped array real-time acquisition is replaced by the incident signal vector given based on the spoofing interference signal model, the direction of arrival estimation is performed; the direction of arrival estimation in this embodiment can be understood as DOA estimation based on MUSIC spatial spectrum, a spatial spectrum function is constructed by using orthogonality of signal subspace and noise subspace, and then parameters of estimated signals are searched through spectrum peaks, so that the direction finding resolution is high, and the accuracy of DOA estimation of multipath signals can be effectively ensured; specifically, the step of estimating the direction of arrival of the received signal to be processed according to the preset spoofing interference signal model to obtain the current signal source azimuth information estimation value includes:
Obtaining a received signal covariance matrix according to the received signal to be processed; the received signals to be processed include the X-axis received signals and the Z-axis received signals as described above, and based on this, an array matrix of the received signals of the entire L-shaped array can be obtained as follows:
y(n)＝B(φ,θ)s(n)+wxz(n)
in the method, in the process of the invention,
B(φ,θ)＝[A(φ)TA(θ)T]T
wxz(n)＝[wx(n)Twz(n)T]T
Wherein y (n) represents an array matrix corresponding to the received signal to be processed; b (phi, theta) and w xz (n) represent the array flow pattern matrix and noise vector, respectively; s (n) represents an incident signal vector;
the received signal covariance matrix corresponding to the available array matrix is:
Ry＝E[y(n)y(n)H]
Wherein R y represents the received signal covariance matrix;
decomposing the covariance matrix of the received signals to obtain a corresponding signal subspace and a corresponding noise subspace; the decomposition process of the covariance matrix of the received signal can be realized by referring to the prior art, and a final decomposition expression can be obtained as follows:
Wherein E s and E w represent a signal subspace and a noise subspace, respectively; Λ s and Λ w represent diagonal matrices constructed from eigenvalues corresponding to each column vector of the signal subspace and the noise subspace, respectively;
obtaining a corresponding MUSIC spatial spectrum according to the signal subspace and the noise subspace; the MUSIC spatial spectrum acquisition process can be understood as constructing a spatial spectrum function by utilizing orthogonality of a signal subspace and a noise subspace, and specifically:
The orthogonality between the noise subspace and the signal subspace is obtained by:
wherein B (θ, φ) is a certain column vector in B (θ, φ); Representing the conjugate transpose of the noise subspace.
And then the MUSIC spatial spectrum is obtained as follows:
Wherein P (θ, φ) represents a pitch angle θ and an azimuth angle Corresponding MUSIC spatial spectrum;
Performing spectral peak search on the MUSIC spatial spectrum to obtain the current signal source azimuth information estimated value; the current signal source azimuth information estimated value can be understood to include the estimated values of the pitch angles and the azimuth angles of all real satellite signals and all deception signals obtained through searching.
In practical application, if only one spoofing signal is obtained, after the estimated values of the pitch angle θ and the azimuth angle Φ of the spoofing signal at the current moment are obtained through the steps of the method, the predicted values of the pitch angle and the azimuth angle at the next moment can be estimated by using kalman filtering directly based on a system state equation and an observation equation as shown below:
the system state equation is expressed as:
x(k|k-1)＝F(k)x(k-1)+W(k)
in the method, in the process of the invention,
Wherein F (k) and W (k) respectively represent a system state transition matrix at the moment k and corresponding noise; x (k-1) represents a system state vector at time k-1; t represents a system observation time interval; omega θ and omega φ are angular velocities;
The observation equation is expressed as:
Z(k)＝H(k)x(k)+V(k)
in the method, in the process of the invention,
Wherein, H (k) and V (k) respectively represent the system measurement matrix at k time and the corresponding noise.
Considering that the above-mentioned classical kalman filtering can realize the effective tracking of pitch angle and azimuth angle of a single deception signal, but facing the application scenario that there are multiple deception signals at the same time, the embodiment preferably adopts the following kalman filtering that introduces the joint probability data interconnection algorithm to simultaneously perform the differentiated tracking of multiple deception signals because the pairing situation of deception signals and pitch angle and azimuth angle can not be determined, and the kalman filtering loses effect, and the problem that the individual deception signals can not be differentially tracked is solved.
S13, tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value; the Kalman filtering algorithm based on the joint probability data interconnection algorithm can be understood as a target tracking algorithm which is characterized in that a deception signal association gate is established by taking a pitch angle and an azimuth angle which are obtained through previous iteration estimation as the center, whether the deception signal association gate falls in a certain deception signal association gate is used as a basis for distinguishing the measurement value of deception signals, and the measurement data of each deception signal are screened, so that the Kalman filtering effect is recovered; specifically, the step of tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-time spoofing signal azimuth information estimated value includes:
According to the shape of the preset correlation gate, respectively centering on the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source, and establishing a corresponding spoofing signal correlation gate; the preset associated door shape can be selected according to actual application requirements, and can be circular, elliptical, rectangular or the like, and is not particularly limited herein;
tracking and measuring by adopting a Kalman filtering algorithm according to the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source to obtain corresponding spoofing signal measurement data; the acquisition process of the spoofing signal measurement data is as follows:
calculating the predicted state of the t-th spoofing signal:
xt(k|k-1)＝Ft(k)xt(k-1)
Calculating a predicted covariance of the t-th spoofing signal:
Pt(k|k-1)＝Ft(k)Pt(k-1)[Ft(k)]T
Wherein P t (k-1) is a covariance matrix;
calculating an innovation vector of the t-th deception signal:
dt(k)＝Z(k)-H(k)xt(k|k-1)
Calculating the innovation vector covariance of the t-th spoofing signal:
St(k)＝H(k)Pt(k-1)[H(k)]T+R(t)
wherein R (t) is a measurement noise covariance matrix;
Calculating the innovation confirmation of the t-th deception signal:
gt(k)＝[dt(k)]T[St(k)]-1dt(k)
Wherein g t (k) is a vector with length of m (k), and corresponds to the innovation of each j measurement data, if the innovation of the j measurement data is smaller than the threshold value of the associated gate of the deceptive signal t, the j measurement is indicated to fall into the associated gate of the t deceptive signal;
Obtaining a corresponding deception signal confirmation matrix according to the deception signal measurement data, the deception signal association gate and a basic assumption of a joint probability data interconnection algorithm; the data interconnection assumption mainly includes two points: 1) Each measurement is derived from a unique spoofing signal, i.e., either one measurement is derived from a spoofing signal or from clutter; 2) For a spoof signal, at most one measurement is sourced; if a spoofing signal is likely to match multiple measurements, only one true measurement is left, and the other measurements are all determined to be false; correspondingly, the spoofing signal confirmation matrix can be understood as a correlation matrix between the measured data and the spoofing signal based on the measured data of each spoofing signal and the data interconnection assumption; based on the example of the oval correlation gate of the spoof signal shown in fig. 4, the correlation gate of the spoof signal 1 and the correlation gate of the spoof signal 2 intersect, and the measurement data 1 and the measurement data 3 are respectively in the correlation gate of the spoof signal 1 and the correlation gate of the spoof signal 2, and the measurement data 2 is in the intersection area of the correlation gate of the spoof signal 1 and the correlation gate of the spoof signal 2, the union of the correlation gate of the spoof signal 1 and the correlation gate of the spoof signal 2 needs to be regarded as a "gather", and represented by a binary confirmation matrix e= { E jt }: if e jt is equal to zero, it means that the measurement data j does not originate from the spoofing signal t, otherwise, it means that the measurement data j originates from the spoofing signal t; in the validation matrix, the first column is used to represent "spoof signal 0", because each measurement data is likely to be clutter, so the validation matrix E has a first column of all 1's; then, for the case of three measurement data of two spoofing signals shown in fig. 4, based on the above analysis, it is easy to know that its corresponding spoofing signal confirmation matrix is:
Obtaining association probability according to the deception signal confirmation matrix, and carrying out signal association probability calculation on each deception signal measurement data according to the association probability to obtain corresponding deception signal measurement probability; after the cheat signal confirmation matrix is obtained by the method, the corresponding interconnection matrix can be obtained by splitting the cheat signal confirmation matrix, and then the association probability is obtained by calculating according to the interconnection matrix; the specific process is as follows:
Splitting the cheating signal confirmation matrix according to the following two principles to obtain a corresponding interconnection matrix set: 1) Each row of each interconnection matrix has and can only have one "1" from the corresponding row of the fraud signal validation matrix such that the first of the aforementioned data interconnection assumptions is satisfied; 2) The number of 1's in each column of each interconnection matrix is less than or equal to 1, except for the first column, so that the second assumption in the data interconnection assumptions is satisfied; the effective application of the probability data interconnection algorithm can be ensured through the interconnection matrix obtained based on the two principle disassembly; the corresponding fraud confirmation matrix of fig. 4 may obtain the following set of 8 interconnection matrices according to the split principle described above:
Based on the obtained interconnection matrix set, a corresponding association event can be obtained, for example, the ith association event at the k moment is marked as theta i(k)＝{ωit }, wherein omega it represents the ith interconnection matrix corresponding to the t-th spoofing signal; the corresponding association probability can be calculated by the following formula:
in the method, in the process of the invention,
Wherein c represents a normalization constant; z k and Z k-1 represent spoofing signal measurement data corresponding to time k and time k-1, respectively; lambda represents clutter density in the region and C represents the number of metrology data in the associated gate of the fraud t; m (k) represents the total number of spoofing signal measurement data at time k; The probability that the spoofing signal t at the moment k receives the measurement data j is represented;
after the correlation probability is obtained through the method steps, the corresponding deception signal measurement probability obtained by combining the corresponding interconnection matrix calculation can be obtained and expressed as:
Wherein β jt (k) represents the probability of correlation of the measured data j with the fraud signal t; n (k) represents the number of interconnected matrixes; representing a corresponding interconnection matrix;
Calculating a corresponding azimuth information estimated value of the spoofing signal at the next moment according to the spoofing signal measurement probability; wherein, the azimuth information estimated value of the deception signal at the next moment, namely the state vector of the deception signal is expressed as:
Wherein, An azimuth information estimated value at the next time of the t-th spoofing signal at the k time is represented; m (k) represents the total number of spoofing signal measurement data at time k; beta jt represents the associated probability of the measurement data j at the time k and the spoofing signal t; Azimuth information of the t-th spoofing signal in the k-moment measurement data j is represented;
Based on the state vector of the obtained spoofing signal, a corresponding target covariance matrix can be calculated:
Wherein P (k) represents a target covariance matrix corresponding to the k moment; d t (k) represents the innovation vector of the t-th spoofing signal at the k moment; s t (k) represents the innovation vector covariance of the t-th spoofing signal at time k.
It should be noted that, the process description given by the above steps is only one iteration, and in practical application, continuous and effective tracking measurement on each spoofed signal can be realized within a preset time period according to the actual application scene requirement; in the embodiment, the azimuth angle and the pitch angle of the deception signal are modeled, and the Kalman filtering is used for prediction and updating, so that accurate tracking can be ensured when the relative positions of the deception signal and the receiver are continuously changed, the problem that a plurality of navigation deception targets cannot be tracked simultaneously by classical Kalman filtering is effectively solved by adding a joint probability data interconnection algorithm, and accurate distinguishing tracking can be still performed on different deception signals when tracks of a plurality of deception signals are crossed.
According to the embodiment of the application, the navigation signals in the deception jamming environment are acquired in real time by adopting the L-shaped array to obtain the to-be-processed receiving signals comprising the X-axis receiving signals and the Z-axis receiving signals, the arrival direction of the to-be-processed receiving signals is estimated according to the preset deception jamming signal model to obtain the current signal source azimuth information estimated value comprising the pitch angle and the azimuth angle of the real satellite signals and the deception signals, and the multi-deception signal incoming wave direction detection scheme for tracking and measuring each deception signal to obtain the corresponding next-moment deception signal azimuth information estimated value is adopted according to the current signal source azimuth information estimated value by adopting the joint probability data interconnection algorithm and the Kalman filtering algorithm.
Although the steps in the flowcharts described above are shown in order as indicated by arrows, these steps are not necessarily executed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders.
In one embodiment, as shown in fig. 5, there is provided an L-array based multiple spoof incoming wave direction detection system, the system comprising:
The signal receiving module 1 is used for acquiring navigation signals in a deception jamming environment in real time through the L-shaped array to obtain a receiving signal to be processed; the to-be-processed receiving signals comprise an X-axis receiving signal and a Z-axis receiving signal;
The azimuth estimation module 2 is used for carrying out direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain an estimated value of azimuth information of a current signal source; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle;
and the signal tracking module 3 is used for tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value.
For specific limitation of the multi-spoofing signal incoming wave direction detecting system based on the L-shaped array, reference may be made to the limitation of the multi-spoofing signal incoming wave direction detecting method based on the L-shaped array, and corresponding technical effects may be equally obtained, which is not described herein. The modules in the L-shaped array-based multi-spoof incoming wave direction detection system can be implemented in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 6 shows an internal structural diagram of a computer device, which may be a terminal or a server in particular, in one embodiment. As shown in fig. 6, the computer device includes a processor, a memory, a network interface, a display, a camera, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements an L-array based multi-spoof incoming wave direction detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those of ordinary skill in the art that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer devices to which the present inventive arrangements may be applied, and that a particular computing device may include more or fewer components than shown, or may combine some of the components, or have the same arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when the computer program is executed.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the above method.
In summary, the method and the system for detecting the incoming directions of the multiple spoofing signals based on the L-shaped array provided by the embodiment of the invention realize the scheme for detecting the incoming directions of the multiple spoofing signals in the corresponding incoming directions of the next spoofing signals based on the incoming directions of the multiple spoofing signals of the L-shaped array, acquire the received signals to be processed including the X-axis received signals and the Z-axis received signals in real time by adopting the L-shaped array, carry out direction estimation on the received signals to be processed according to a preset spoofing interference signal model, acquire the current signal source direction information estimated value including the pitch angle and the azimuth angle of the true satellite signals and the spoofing signals, and track and measure the corresponding incoming directions of the multiple spoofing signals of the next spoofing signals according to the current signal source direction information estimated value by adopting a joint probability data interconnection algorithm and a Kalman filtering algorithm.
In this specification, each embodiment is described in a progressive manner, and all the embodiments are directly the same or similar parts referring to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. It should be noted that, any combination of the technical features of the foregoing embodiments may be used, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few preferred embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the application. It should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present application, and such modifications and substitutions should also be considered to be within the scope of the present application. Therefore, the protection scope of the patent of the application is subject to the protection scope of the claims.
Claims (9)
1. An L-shaped array-based multi-spoof signal incoming wave direction detection method is characterized by comprising the following steps of:
acquiring navigation signals in a deception jamming environment in real time through an L-shaped array to obtain a to-be-processed received signal; the to-be-processed receiving signals comprise an X-axis receiving signal and a Z-axis receiving signal;
Performing direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain a current signal source azimuth information estimation value; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle;
tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value;
The step of tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value comprises the following steps:
according to the shape of the preset correlation gate, respectively centering on the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source, and establishing a corresponding spoofing signal correlation gate;
Tracking and measuring by adopting a Kalman filtering algorithm according to the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source to obtain corresponding spoofing signal measurement data;
Obtaining a corresponding deception signal confirmation matrix according to the deception signal measurement data, the deception signal association gate and a basic assumption of a joint probability data interconnection algorithm;
obtaining association probability according to the deception signal confirmation matrix, and carrying out signal association probability calculation on each deception signal measurement data according to the association probability to obtain corresponding deception signal measurement probability;
And calculating a corresponding next-time spoofing signal azimuth information estimated value according to the spoofing signal measurement probability.
2. The L-array based multiple spoof signal incoming wave direction detecting method of claim 1, wherein the L-array comprises first and second uniform linear arrays orthogonal to each other in a xoz plane; the first uniform linear array and the second uniform linear array comprise a plurality of array elements which are distributed at equal intervals; the common reference array element of the L-shaped array is arranged at the original point where the first uniform linear array and the second uniform linear array intersect.
3. The L-array based multiple spoof signal incoming wave direction detecting method of claim 1, wherein the received signal to be processed is represented as:
in the method, in the process of the invention,
s(n)＝[s1(n),s2(n),…,sK(n)]T
A(φ)＝[a(φ1),a(φ2),…,a(φK)]
A(θ)＝[a(θ1),a(θ2),…,a(θK)]
Wherein X (n) and Z (n) represent an X-axis reception signal and a Z-axis reception signal, respectively; s (n) represents an incident signal vector; a (phi) and w x (n) respectively represent the flow pattern matrix of the X-axis and the corresponding noise vector; a (θ) and w z (n) represent the flow pattern matrix and corresponding noise vector, respectively, of the Z-axis; k represents the total number of incident signals; θ i and φ i represent the pitch angle and azimuth angle, respectively, of the ith spoof signal; [] T denotes a transpose.
4. The L-array based multiple spoof signal incoming wave direction detection method of claim 3, wherein the spoof signal model is represented as:
wherein s (n) represents an incident signal vector; sate n,m denotes the mth real satellite signal in the nth direction; spoof n,p denotes the nth spoofing signal in the nth direction; m and P represent the total number of true satellite signals and the total number of rogue signals in the nth direction, respectively.
5. The method for detecting incoming directions of multiple spoofing signals based on an L-shaped array of claim 4, wherein the step of estimating the direction of arrival of the received signal to be processed according to a predetermined spoofing signal model to obtain the current signal source azimuth information estimate comprises:
obtaining a received signal covariance matrix according to the received signal to be processed;
decomposing the covariance matrix of the received signals to obtain a corresponding signal subspace and a corresponding noise subspace;
obtaining a corresponding MUSIC spatial spectrum according to the signal subspace and the noise subspace;
and carrying out spectral peak search on the MUSIC spatial spectrum to obtain the current signal source azimuth information estimated value.
6. The L-array based multiple spoof incoming wave direction detection method of claim 1, wherein the next time spoof azimuth information estimate is expressed as:
Wherein, An azimuth information estimated value at the next time of the t-th spoofing signal at the k time is represented; m (k) represents the total number of spoofing signal measurement data at time k; beta jt represents the associated probability of the measurement data j at the time k and the spoofing signal t; And the azimuth information of the t-th deception signal in the k-moment measurement data j is shown.
7. An L-array based multiple spoof signal incoming wave direction detection system, the system comprising:
The signal receiving module is used for acquiring navigation signals in the deception jamming environment in real time through the L-shaped array to obtain a receiving signal to be processed; the to-be-processed receiving signals comprise an X-axis receiving signal and a Z-axis receiving signal;
The azimuth estimation module is used for carrying out direction of arrival estimation on the received signal to be processed according to a preset deception jamming signal model to obtain an estimated value of azimuth information of a current signal source; the current signal source azimuth information comprises azimuth information of real satellite signals and deception signals; the azimuth information comprises a pitch angle and an azimuth angle;
The signal tracking module is used for tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value by adopting a Kalman filtering algorithm based on a joint probability data interconnection algorithm to obtain a corresponding next-moment spoofing signal azimuth information estimated value;
The kalman filtering algorithm based on the joint probability data interconnection algorithm is used for tracking and measuring each spoofing signal according to the current signal source azimuth information estimated value to obtain a corresponding next-time spoofing signal azimuth information estimated value, and the kalman filtering algorithm comprises the following steps:
according to the shape of the preset correlation gate, respectively centering on the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source, and establishing a corresponding spoofing signal correlation gate;
Tracking and measuring by adopting a Kalman filtering algorithm according to the azimuth information of each spoofing signal in the azimuth information estimation value of the current signal source to obtain corresponding spoofing signal measurement data;
Obtaining a corresponding deception signal confirmation matrix according to the deception signal measurement data, the deception signal association gate and a basic assumption of a joint probability data interconnection algorithm;
obtaining association probability according to the deception signal confirmation matrix, and carrying out signal association probability calculation on each deception signal measurement data according to the association probability to obtain corresponding deception signal measurement probability;
And calculating a corresponding next-time spoofing signal azimuth information estimated value according to the spoofing signal measurement probability.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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