CN109283553A - A kind of seven array element satellite navigation anti-interference methods - Google Patents
A kind of seven array element satellite navigation anti-interference methods Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
Abstract
The present invention proposes a kind of satellite navigation anti-interference method of seven array-element antennas, the satellite navigation jamproof system of seven array-element antennas is built first, it include: anti-jamming signal processing model building module (1), autocorrelation matrix seeks module (2), Optimality equations construct module (3), adaptive iteration recursion module (4), right value update module (5).This method uses adaptive iteration operation, avoids the inversion operation of input signal autocorrelation matrix, reduces operand, solves the problems, such as that conventional iterative algorithm convergence rate and steady-state error cannot meet simultaneously.This method process is clear, and formula calculates simply, is able to carry out real-time calculating, meets the requirement of engineer application.
Description
Technical field
The present invention relates to a kind of anti-interference method, especially a kind of anti-interference side for seven array element satellite navigation systems
Method.
Background technique
In satellite navigation system, in order to fight enemy's human interference, military weapon precision strike capability is improved, is needed
Add AF panel module in satellite navigation system front end.There are two ways to previous: one is use space domain self-adapted processing side
Method automatically adjusts the shape of antenna radiation pattern, and effective zeroing is carried out directly on interference radiating way and is inhibited, this method is by freedom
The limitation of degree needs more aerial arrays, volume is too big when there is a large amount of interference；Another kind is using at space-time adaptive
Reason method increases freedom degree by time tap in the case where not increasing bay, can inhibit largely to interfere,
This processing method needs to solve the inverse of covariance matrix, and tap number is more, and matrix dimension is bigger, and inverse operation complexity is higher,
Real-time cannot be guaranteed.Adaptive iteration operation can solve this problem, but traditional iterative algorithm cannot meet simultaneously
Fast convergence rate, the small requirement of steady-state error.
Summary of the invention
It is an object of that present invention to provide a kind of seven array element satellite navigation anti-interference methods, using adaptive iteration operation, solution
Certainly previous anti-interference method data storage capacity is big, and computation complexity is high, the problem of real-time difference, while improving convergence rate,
Reduce steady-state error.
A kind of specific steps of seven array element satellite navigation anti-interference method are as follows:
The first step builds seven array element satellite navigation jamproof systems
Seven array element satellite navigation jamproof systems, comprising: anti-jamming signal processing model building module, autocorrelation matrix are asked
Modulus block, Optimality equations building module, adaptive iteration recursion module and right value update module.
The function of anti-jamming signal processing model building module are as follows: establish seven array element anti-jamming signals processing model, obtain
Anti-interference process exports expression formula.
Autocorrelation matrix seeks the function of module are as follows: obtains input signal autocorrelation matrix；
The function of Optimality equations building module are as follows: utilize linearly constrained minimum variance, building optimizes equation；
The function of adaptive iteration recursion module are as follows: utilize adaptive iteration operation, simplify best initial weights；
The function of right value update module are as follows: step factor is corrected using normalization operation, updates weight.
Second step anti-jamming signal processing model building module establishes seven array element anti-jamming signals processing model
Seven array element anti-interference antenna of satellite navigation battle arrays share seven array elements, there is a 6 rank FIR filtering behind each array element channel
Device, filter factor are respectively wm1, wm2, wm3, wm4, wm5, wm6；Seven array-element antennas receive signal and are expressed as x1(n) ..., x7
(n), then it is x that the FIR after array element, which filters each multiplier input signals,m1(n)=xm(n), xm2(n)=xm(n-1) ... ..., xm6(n)
=xm(n-5).Wherein m indicates that array element channel number, n indicate that discrete time, value are (0 ,+∞).
Indicate that input signal matrix is with x
X=[x11,x12,...,x16,x21,x22,...,x26,...,x71,x72,...,x76]T (1)
Processor weight vectors are indicated with 76 × 1 dimensional vector w, then
W=[w11,w12,...,w16,w21,...,w26,...,w71,...,w76]T (2)
The output valve of anti-interference process is
Y=wHx (3)
Third step autocorrelation matrix seeks module and obtains input signal autocorrelation matrix
Autocorrelation matrix seeks module and carries out calculation processing according to formula (4), obtains autocorrelation matrix Rxx:
In formula, RxxIndicate autocorrelation matrix；X (n)=[x11(n) x12(n)...x26(n)...x76(n)]T, when indicating n-th
The input signal at quarter；N indicates number of snapshots needed for calculating autocorrelation matrix.
The convergence rate of iterative algorithm is determined by the degree of scatter of input signal autocorrelation matrix R characteristic value.Matrix Rxx's
Characteristic value is able to reflect the intensity and number of interference, when not interfering with, RxxThe dispersion degree of characteristic value is minimum, restrains most fast；When
There are interference, and when the power spectrum density of input array has very big dynamic, will be difficult to restrain.Autocorrelation matrix is
The Hermitian matrix of nonnegative definite has complex conjugate symmetry, can simplify calculating process, in such a way that sliding solves, extracts
The information of multiple snaps is calculated.
4th step Optimality equations construct module building and optimize equation
Optimality equations construct module according to linearly constrained minimum variance, and seven array element anti-jamming signals processing model is retouched
State the optimization problem for following formula
P in formulaoutIndicate that output power, S indicate guidance cues, RxxIndicate that input signal autocorrelation matrix, w indicate weighting
Vector.Set guidance cues S vector value: s11=s12=...=s16=1, s21=s22=...=s76=0.
The weighting coefficient w of the first array element of weight vector is acquired under constraint condition11=w12=...=w16=1.
5th step adaptive iteration recursion module simplifies best initial weights using adaptive iteration operation
Adaptive iteration recursion module presets an initial value w (0) first, so that w is since w (0) along PoutReduced side
Best initial weights w is adjusted to adaptiveopt。PoutThe most fast direction reduced is its negative gradient direction, in conjunction with constraint condition wHS=
1, obtain recurrence formula (6):
W (n) indicates the weighted vector value at the n-th moment in formula；W (n+1) indicates the weighted vector value at the (n+1)th moment；It indicates
Step factor；▽wPoutIndicate output power gradient；
The value of recursion coefficient a is adjusted, in order to meet constraint condition wH(n+1) S=1 is then:
Bring the value of formula (8) a into recursive expression, then
Obtain the best initial weights recursive expression based on linearly constrained minimum variance
6th step right value update module corrects step factor and updates weight
Right value update module seeks weight using normalization variable step operation, and calculating process is as follows:
Firstly, the material calculation factor
In formulaIndicate constant coefficient；Δ μ indicates the step factor after normalization；Rxx HIndicate the conjugation of autocorrelation matrix.
Step factor in formula (10)Be one control iterative algorithm convergence rate and steady-state error it is normal
Amount: big constant coefficient is selectedConvergence is fast, but shakes big；Select small constant coefficientConvergence is slow, but shakes small.Selection is suitable
Constant coefficientGuarantee the convergence of iterative algorithm.In addition the range that increase stable step-length, needs to reduce input signal power
Maximum value is composed, the power spectrum for adjusting input signal keeps it more flat, i.e. albefaction input signal, reduces input autocorrelation matrix
Characteristic value divergence, therefore the step factor in more new formula (10) is formula (11), using normalization scale gene, removal
Correlation between input sample point.
Then, weight error is calculated
Δ w (n)=Δ μ (n) Rxx·w(n) (12)
Δ w (n) indicates that the weight Error vector magnitude at the n-th moment, Δ μ (n) indicate the step factor at the n-th moment in formula；
Weight error is carried out with interative computation and is constantly updated, and can tend to restrain as early as possible after larger fluctuation, the condition of convergence
It is unrelated with the characteristic value of input signal, it eliminates since noise increases caused by input weight vector is excessive, increases the dynamic of algorithm
State input range improves the convergence rate of iterative algorithm.
Finally, calculating updated weight
W (n+1)=w (n)-Δ w (n) (13)
W (n+1) is the weight vectors at the n-th moment in formula, and w (n) and Δ w (n) are the weight vectors and weight at the (n+1)th moment
Error vector.
So far, seven array element satellite navigation anti-interference methods are completed.
The present invention uses adaptive iteration operation, avoids input signal autocorrelation matrix inversion operation, reduces operation
Amount, solves the problems, such as that conventional iterative algorithm convergence rate and steady-state error cannot meet simultaneously.This method process is clear, formula
It calculates simply, is able to carry out real-time calculating, meets the requirement of engineer application.
Detailed description of the invention
Flow chart described in Fig. 1 seven array element satellite navigation anti-interference methods of one kind；
1. anti-jamming signal processing 2. autocorrelation matrix of model building module seeks 3. Optimality equations of module building module
4. 5. right value update module of adaptive iteration recursion module.
Specific embodiment
A kind of specific steps of seven array element satellite navigation anti-interference method are as follows:
The first step builds seven array element satellite navigation jamproof systems
Seven array element satellite navigation jamproof systems, comprising: anti-jamming signal handles model building module 1, autocorrelation matrix
Seek module 2, Optimality equations building module 3, adaptive iteration recursion module 4 and right value update module 5.
The function of anti-jamming signal processing model building module 1 are as follows: establish seven array element anti-jamming signals processing model, obtain
Anti-interference process exports expression formula.
Autocorrelation matrix seeks the function of module 2 are as follows: obtains input signal autocorrelation matrix；
The function of Optimality equations building module 3 are as follows: utilize linearly constrained minimum variance, building optimizes equation；
The function of adaptive iteration recursion module 4 are as follows: utilize adaptive iteration operation, simplify best initial weights；
The function of right value update module 5 are as follows: step factor is corrected using normalization operation, updates weight.
Second step anti-jamming signal processing model building module 1 establishes seven array element anti-jamming signals processing model
Seven array element anti-interference antenna of satellite navigation battle arrays share seven array elements, there is a 6 rank FIR filtering behind each array element channel
Device, filter factor are respectively wm1, wm2, wm3, wm4, wm5, wm6；Seven array-element antennas receive signal and are expressed as x1(n) ..., x7
(n), then it is x that the FIR after array element, which filters each multiplier input signals,m1(n)=xm(n), xm2(n)=xm(n-1) ... ..., xm6(n)
=xm(n-5).Wherein m indicates that array element channel number, n indicate that discrete time, value are (0 ,+∞).
Indicate that input signal matrix is with x
X=[x11,x12,...,x16,x21,x22,...,x26,...,x71,x72,...,x76]T (1)
Processor weight vectors are indicated with 76 × 1 dimensional vector w, then
W=[w11,w12,...,w16,w21,...,w26,...,w71,...,w76]T (2)
The output valve of anti-interference process is
Y=wHx (3)
Third step autocorrelation matrix seeks module 2 and obtains input signal autocorrelation matrix
Autocorrelation matrix seeks module 2 and carries out calculation processing according to formula (4), obtains autocorrelation matrix Rxx:
In formula, RxxIndicate autocorrelation matrix；X (n)=[x11(n) x12(n) ...x26(n) ...x76(n)]T, indicate n-th
The input signal at moment；N indicates number of snapshots needed for calculating autocorrelation matrix.
The convergence rate of iterative algorithm is determined by the degree of scatter of input signal autocorrelation matrix R characteristic value.Matrix Rxx's
Characteristic value is able to reflect the intensity and number of interference, when not interfering with, RxxThe dispersion degree of characteristic value is minimum, restrains most fast；When
There are interference, and when the power spectrum density of input array has very big dynamic, will be difficult to restrain.Autocorrelation matrix is
The Hermitian matrix of nonnegative definite has complex conjugate symmetry, can simplify calculating process, in such a way that sliding solves, extracts
The information of multiple snaps is calculated.
4th step Optimality equations construct the building of module 3 and optimize equation
Optimality equations construct module 3 according to linearly constrained minimum variance, and seven array element anti-jamming signals are handled model
It is described as the optimization problem of following formula
P in formulaoutIndicate that output power, S indicate guidance cues, RxxIndicate that input signal autocorrelation matrix, w indicate weighting
Vector.Set guidance cues S vector value: s11=s12=...=s16=1, s21=s22=...=s76=0.
The weighting coefficient w of the first array element of weight vector is acquired under constraint condition11=w12=...=w16=1.
5th step adaptive iteration recursion module 4 simplifies best initial weights using adaptive iteration operation
Adaptive iteration recursion module 4 presets an initial value w (0) first, so that w is since w (0) along PoutReduce
Direction-adaptive is adjusted to best initial weights wopt。PoutThe most fast direction reduced is its negative gradient direction, in conjunction with constraint condition wHS
=1, obtain recurrence formula (6):
W (n) indicates the weighted vector value at the n-th moment in formula；W (n+1) indicates the weighted vector value at the (n+1)th moment；It indicates
Step factor；▽wPoutIndicate output power gradient；
The value of recursion coefficient a is adjusted, in order to meet constraint condition wH(n+1) S=1 is then:
Bring the value of formula (8) a into recursive expression, then
Obtain the best initial weights recursive expression based on linearly constrained minimum variance
6th step right value update module 5 corrects step factor and updates weight
Right value update module 5 seeks weight using normalization variable step operation, and calculating process is as follows:
Firstly, the material calculation factor
In formulaIndicate constant coefficient；Δ μ indicates the step factor after normalization；Rxx HIndicate the conjugation of autocorrelation matrix.
Step factor in formula (10)Be one control iterative algorithm convergence rate and steady-state error it is normal
Amount: big constant coefficient is selectedConvergence is fast, but shakes big；Select small constant coefficientConvergence is slow, but shakes small.Selection is suitable
Constant coefficientGuarantee the convergence of iterative algorithm.In addition the range that increase stable step-length needs to reduce input signal power spectrum
Maximum value, the power spectrum for adjusting input signal keep it more flat, i.e. albefaction input signal, reduce the spy of input autocorrelation matrix
Value indicative divergence, therefore the step factor in more new formula (10) is formula (11), using normalization scale gene, is removed defeated
Enter the correlation between sampled point.
Then, weight error is calculated
Δ w (n)=Δ μ (n) Rxx·w(n) (12)
Δ w (n) indicates that the weight Error vector magnitude at the n-th moment, Δ μ (n) indicate the step factor at the n-th moment in formula；
Weight error is carried out with interative computation and is constantly updated, and can tend to restrain as early as possible after larger fluctuation, the condition of convergence
It is unrelated with the characteristic value of input signal, it eliminates since noise increases caused by input weight vector is excessive, increases the dynamic of algorithm
State input range improves the convergence rate of iterative algorithm.
Finally, calculating updated weight
W (n+1)=w (n)-Δ w (n) (13)
W (n+1) is the weight vectors at the n-th moment in formula, and w (n) and Δ w (n) are the weight vectors and weight at the (n+1)th moment
Error vector.
So far, seven array element satellite navigation anti-interference methods are completed.
Claims (3)
1. a kind of seven array element satellite navigation anti-interference methods, it is characterised in that specific steps are as follows:
The first step builds seven array element satellite navigation jamproof systems
Seven array element satellite navigation jamproof systems, comprising: anti-jamming signal processing model building module (1), autocorrelation matrix are asked
Modulus block (2), Optimality equations building module (3), adaptive iteration recursion module (4) and right value update module (5)；
Anti-jamming signal handles the function of model building module (1) are as follows: establishes seven array element anti-jamming signals processing model, obtains anti-
Interference processing output expression formula；
Autocorrelation matrix seeks the function of module (2) are as follows: obtains input signal autocorrelation matrix；
Optimality equations construct the function of module (3) are as follows: utilize linearly constrained minimum variance, building optimizes equation；
The function of adaptive iteration recursion module (4) are as follows: utilize adaptive iteration operation, simplify best initial weights；
The function of right value update module (5) are as follows: step factor is corrected using normalization operation, updates weight；
Seven array element anti-jamming signals processing model is established in second step anti-jamming signal processing model building module (1)
Seven array element anti-interference antenna of satellite navigation battle arrays share seven array elements, there is a 6 rank FIR filters behind each array element channel,
Filter factor is respectively wm1, wm2, wm3, wm4, wm5, wm6；Seven array-element antennas receive signal and are expressed as x1(n) ..., x7(n),
It is x that then the FIR after array element, which filters each multiplier input signals,m1(n)=xm(n), xm2(n)=xm(n-1) ... ..., xm6(n)=xm
(n-5)；Wherein m indicates that array element channel number, n indicate that discrete time, value are (0 ,+∞)；
Indicate that input signal matrix is with x
X=[x11,x12,...,x16,x21,x22,...,x26,...,x71,x72,...,x76]T (1)
Processor weight vectors are indicated with 76 × 1 dimensional vector w, then
W=[w11,w12,...,w16,w21,...,w26,...,w71,...,w76]T (2)
The output valve of anti-interference process is
Y=wHx (3)
Third step autocorrelation matrix seeks module (2) and obtains input signal autocorrelation matrix
Autocorrelation matrix seeks module (2) and carries out calculation processing according to formula (4), obtains autocorrelation matrix Rxx:
In formula, RxxIndicate autocorrelation matrix；X (n)=[x11(n) x12(n) ...x26(n) ...x76(n)]T, indicated for the n-th moment
Input signal；N indicates number of snapshots needed for calculating autocorrelation matrix；
The convergence rate of iterative algorithm is determined by the degree of scatter of input signal autocorrelation matrix R characteristic value；Matrix RxxFeature
Value is able to reflect the intensity and number of interference, when not interfering with, RxxThe dispersion degree of characteristic value is minimum, restrains most fast；Work as presence
Interference, and the power spectrum density of input array have very big dynamic when, will be difficult to restrain；Autocorrelation matrix is non-negative
Fixed Hermitian matrix has complex conjugate symmetry, can simplify calculating process, in such a way that sliding solves, extracts multiple
The information of snap is calculated；
4th step Optimality equations construct module (3) building and optimize equation
Optimality equations construct module (3) according to linearly constrained minimum variance, and seven array element anti-jamming signals processing model is retouched
State the optimization problem for following formula
P in formulaoutIndicate that output power, S indicate guidance cues, RxxIndicate that input signal autocorrelation matrix, w indicate weight vectors；
5th step adaptive iteration recursion module (4) simplifies best initial weights using adaptive iteration operation
Adaptive iteration recursion module (4) presets an initial value w (0) first, so that w is since w (0) along PoutReduced side
Best initial weights w is adjusted to adaptiveopt；PoutThe most fast direction reduced is its negative gradient direction, in conjunction with constraint condition wHS=
1, obtain recurrence formula (6):
W (n) indicates the weighted vector value at the n-th moment in formula；W (n+1) indicates the weighted vector value at the (n+1)th moment；Indicate step-length
The factor；▽wPoutIndicate output power gradient；
The value of recursion coefficient a is adjusted, in order to meet constraint condition wH(n+1) S=1 is then:
Bring the value of formula (8) a into recursive expression, then
Obtain the best initial weights recursive expression based on linearly constrained minimum variance
6th step right value update module (5) corrects step factor and updates weight
Right value update module (5) seeks weight using normalization variable step operation, and calculating process is as follows:
Firstly, the material calculation factor
In formulaIndicate constant coefficient；Δ μ indicates the step factor after normalization；Rxx HIndicate the conjugation of autocorrelation matrix；
Step factor in formula (10)It is the constant of control an iterative algorithm convergence rate and steady-state error:
Select big constant coefficientConvergence is fast, but shakes big；Select small constant coefficientConvergence is slow, but shakes small；Selection is suitable normal
CoefficientGuarantee the convergence of iterative algorithm；In addition the range that increase stable step-length needs to reduce input signal power spectrum most
Big value, the power spectrum for adjusting input signal keep it more flat, i.e. albefaction input signal, reduce the feature of input autocorrelation matrix
It is worth divergence, therefore the step factor in more new formula (10) is formula (11), using normalization scale gene, removal input
Correlation between sampled point；
Then, weight error is calculated
Δ w (n)=Δ μ (n) Rxx·w(n) (12)
Δ w (n) indicates that the weight Error vector magnitude at the n-th moment, Δ μ (n) indicate the step factor at the n-th moment in formula；
Weight error with interative computation carry out and constantly update, can tend to restrain as early as possible after larger fluctuation, the condition of convergence with it is defeated
The characteristic value for entering signal is unrelated, eliminates since noise increases caused by input weight vector is excessive, the dynamic for increasing algorithm is defeated
Enter range, improves the convergence rate of iterative algorithm；
Finally, calculating updated weight
W (n+1)=w (n)-Δ w (n) (13)
W (n+1) is the weight vectors at the n-th moment in formula, and w (n) and Δ w (n) are the weight vectors and weight error at the (n+1)th moment
Vector；
So far, seven array element satellite navigation anti-interference methods are completed.
2. seven array element satellite navigation anti-interference method as described in claim 1, it is characterised in that Optimality equations structure in the 4th step
During modeling block (3) building optimization equation, guidance cues S vector value is set are as follows: s11=s12=...=s16=1, s21
=s22=...=s76=0, the weighting coefficient w of the first array element of weight vector is acquired under constraint condition11=w12=...=w16=
1。
3. a kind of seven array element satellite navigation jamproof systems, characterized by comprising: anti-jamming signal handles model building module
(1), autocorrelation matrix seeks module (2), Optimality equations building module (3), adaptive iteration recursion module (4) and right value update
Module (5)；
Anti-jamming signal handles the function of model building module (1) are as follows: establishes seven array element anti-jamming signals processing model, obtains anti-
Interference processing output expression formula；
Autocorrelation matrix seeks the function of module (2) are as follows: obtains input signal autocorrelation matrix；
Optimality equations construct the function of module (3) are as follows: utilize linearly constrained minimum variance, building optimizes equation；
The function of adaptive iteration recursion module (4) are as follows: utilize adaptive iteration operation, simplify best initial weights；
The function of right value update module (5) are as follows: step factor is corrected using normalization operation, updates weight.
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| CN110850445B (en) * | 2019-11-21 | 2024-04-05 | 中国人民解放军63961部队 | Pulse interference suppression method based on space-time sampling covariance inversion |
| CN112769469A (en) * | 2021-01-23 | 2021-05-07 | 成都振芯科技股份有限公司 | Method and device for controlling operation array element number based on beam forming |
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