CN112578410B - GPS evolution band-limited Gaussian noise interference algorithm based on LMS - Google Patents
GPS evolution band-limited Gaussian noise interference algorithm based on LMS 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
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Abstract
The invention relates to an evolution band-limited Gaussian noise interference algorithm for a GPS based on LMS, which is used for determining an expression function x (nT) of an interference signal; establishing an jammer and GPS receiver model; optimizing a flicker function r (nT) in an expression function x (nT) by adopting an evolutionary algorithm; the degree of influence of the interfering signal on the receiver is determined. The evolutionary band-limited Gaussian noise interference algorithm has more obvious interference effect than band-limited Gaussian noise interference when the number of interference is different.
Description
Technical Field
The invention relates to the technical field of GPS signal interference, in particular to a GPS evolution band-limited Gaussian noise interference algorithm based on LMS (Least mean square ).
Background
A GPS receiver is an instrument that receives global positioning system satellite signals and determines the ground space position. The interference signal is a signal that impairs reception of the useful signal. Signal reception by a hostile GPS receiver is possible through signal interference.
The interference is suppressed, so that useful signals received by the enemy electronic equipment are blurred or completely covered, and the electronic interference cannot work normally. The conventional compressed GPS jammer, by transmitting an interference signal having a higher power than the GPS signal, makes the GPS receiver unable to decode correctly. The zeroing GPS receiver uses an antenna array, and the interference direction is zeroed through a self-adaptive algorithm, so that the zeroing GPS receiver has a remarkable anti-interference effect on the traditional suppression type interference mode.
Currently, most researchers are still researching from the point of view of interfering with a common GPS receiver. In the "band-limited Gaussian noise interference parameter selection analysis of GPS army code signals" published by the 15 th volume and the 6 th phase of the journal of air force engineering university (Nature science edition) in 2014, the best Gaussian white noise interference parameter selection strategy and the method for judging the best interference signals are respectively provided by the 2016 th volume and the like in the 257-267 th phases of the 24 th volume and the 4 th phase of the computer measurement and control.
Prior to 2017, less interference was studied for zeroed GPS. In 2017, li Yawei, in a document of research on interference and anti-interference technology of satellite navigation receivers using array antennas, a pulse interference method for a zero-setting GPS is provided, and the relative time delay between multiple interferences is periodically adjusted to obtain a certain performance gain. However, no research results for improving the interference effect by the optimization algorithm are disclosed.
Disclosure of Invention
The invention aims to provide a GPS evolution band-limited Gaussian noise interference algorithm based on LMS, which can remarkably improve the effect of signal interference.
The technical scheme for solving the technical problems is as follows:
an evolutionary band-limited gaussian noise interference algorithm for LMS-based GPS, comprising the steps of:
step 1: determining an expression function x (nT) of the interference signal;
step 2: establishing an jammer and GPS receiver model;
step 3: optimizing a flicker function r (nT) in an expression function x (nT) by adopting an evolutionary algorithm;
step 4: the degree of influence of the interfering signal on the receiver is determined.
One of the above-mentioned methods is directed to an LMS-based GPS evolutionary band-limited gaussian noise interference algorithm, in which step 1,
the complex domain expression of the single interfering signal x (nT) is shown in the following formula (1);
x(nT)＝r(nT)[f 1 (nT)cos(2πf c nT+β)+jf 2 (nT)sin(2πf c nT+β)] (1)
in the formula (1), f c =1575.42 MHz, representing the GPS L1 signal carrier frequency; beta represents an initial phase; t=1/f s Representing a sampling period; f (f) s Representing the sampling frequency; f (f) 1( nT)) and f 2( nT) represents gaussian white noise after H (Z) filtering; r (nT) represents a flicker function; n represents the sampling point number and j represents the imaginary unit.
The process of establishing the jammer and GPS receiver model in the step 2 comprises the following steps of:
step 201: determining the arrangement mode of an jammer and a GPS receiver;
step 202: determining an expression function r (nT) of an array element receiving signal of the GPS receiver;
step 203: obtaining an optimal value W of the weight vector W by adopting an LMS algorithm opt ；
Step 204: calculating a pattern
In the step 203, an LMS algorithm is adopted to obtain an optimal value W of the weight vector W opt The process of (1) comprises the following steps:
step 2031: calculating weighted average signals y (K) of the rest array element signals except the reference array element;
step 2032: calculating an error signal e (K);
step 2033: the LMS algorithm obtains a weight vector W by utilizing a steepest descent method;
step 2034: obtaining an optimal weight vector W by using an LMS algorithm opt 。
In the step 3, the scintillation function r (nT) in the expression function x (nT) is optimized by optimizing the a (k) sequence by adopting the evolution algorithm; the process of optimizing the a (k) sequence is as follows:
step 301: firstly, initializing parameters of an optimization algorithm;
step 302: for each population member, calculating a variation function update gene of the gene mutation;
step 303: calculating a variation function update gene of the cross variation;
step 304: calculating each member evaluation value score;
step 305: the maximum value of all evaluation values score is calculated.
The beneficial effects of the scheme of the invention are as follows:
the invention relates to a method for determining an expression function x (nT) of an interference signal aiming at a GPS evolution band-limited Gaussian noise interference algorithm based on LMS; establishing an jammer and GPS receiver model; optimizing a flicker function r (nT) in an expression function x (nT) by adopting an evolutionary algorithm; the degree of influence of the interfering signal on the receiver is determined.
According to the invention, the band-limited Gaussian noise interference scintillation function is optimized through the evolution algorithm, so that the convergence of an LMS-based self-adaptive algorithm in the GPS receiver is disturbed, the average null depth of the GPS receiver under equal power is lower, and a better interference effect is realized.
The invention evolves a band-limited Gaussian noise interference algorithm, and has more obvious interference effect than band-limited Gaussian noise interference when different interference numbers are used.
Drawings
Fig. 1 is a schematic diagram of a distribution of jammers that jams a GPS receiver according to the present invention.
Fig. 2 is a model of a zeroed GPS receiver of the present invention.
Fig. 3 is a schematic diagram of a seven-element uniform circular array according to the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
As shown in fig. 1-3, a method for LMS-based GPS evolutionary band-limited gaussian noise interference algorithm comprises the steps of:
step 1: determining an expression function x (nT) of the interference signal;
step 2: establishing an jammer and GPS receiver model;
step 3: optimizing a flicker function r (nT) in an expression function x (nT) by adopting an evolutionary algorithm;
step 4: the degree of influence of the interfering signal on the receiver is determined.
In the step (1) of the above-mentioned process,
the complex domain expression of the single interfering signal x (nT) is shown in the following formula (1);
x(nT)＝r(nT)[f 1 (nT)cos(2πf c nT+β)+jf 2 (nT)sin(2πf c nT+β)] (1)
in the formula (1), f c =1575.42 MHz, representing the GPS L1 signal carrier frequency; beta represents an initial phase; t=1/f s Representing a sampling period; f (f) s Representing the sampling frequency; f (f) 1( nT) and f 2( nT) represents gaussian white noise after H (Z) filtering; r (nT) represents a flicker function; n represents a sampling point number, j represents an imaginary unit (j is the same as the common imaginary unit i);
f i (nT) (i=1, 2) represents gaussian white noise after H (Z) filtering.
The Z domain expression of H (Z) is:
the expression of the flicker function r (nT) is the following expression (2):
in equation (2), the functionRecord F a (0) =0. k=0, 1,2, …; n represents the sampling point number.
In the step 2, the process of establishing the model of the jammer and the GPS receiver comprises the following steps:
step 201: determining the arrangement mode of an jammer and a GPS receiver;
step 202: determining an expression function R (nT) of an array element receiving signal of the GPS receiver;
step 203: obtaining an optimal value W of the weight vector W by adopting an LMS algorithm opt ；
Step 204: calculating a pattern
In the evolutionary band-limited Gaussian noise interference algorithm, the a (k) sequence is optimized through the evolutionary algorithm, and then the flicker function r (nT) is optimized, so that the GPS receiver obtains smaller nulls in the interference direction.
To optimize the a (k) sequence, jammer and GPS receiver models need to be built. A typical jammer and receiver layout is shown in fig. 1. The jammers are arranged in different directions of the GPS receiver. Fig. 1 shows six jammers arranged at different azimuth angles. Suppose that the jammer is N in total J The GPS receiver receives N J And the interference signals.
Let the GPS receiver array element receive signal R (nT) be defined by N s GPS signals, N J The interference signal and the additive white gaussian noise N (nT).
The GPS antenna array is a uniform circular array. Fig. 3 is a seven-element uniform circular array. Selecting a circle center array element as a reference array element, and assuming that the reference array element receives a signal x 0 The expression (t) is:wherein A (t) is the signal amplitude, +.>Is the initial phase of the signal.
The structure of the zero-setting GPS receiver model is shown in fig. 2, signals received by M antenna array elements are subjected to an LMS-based self-adaptive algorithm to obtain an optimal weight W, and the signals obtained through weighting of the optimal weight W are demodulated by the GPS receiver to obtain GPS information. The direction diagram G can be calculated by the optimal weight W. The depth of the nulls from the interfering signal in the pattern is much greater than in the other directions. The degree of influence of the interference signal on the receiver can be evaluated by the null depth of the pattern.
Assume that in a uniform circular array of M array elements, the rest M-1 array elements except for the center array element are respectively expressed as [ x ] 1 (t),x 2 (t),…,x M-1 (t)]The propagation delay between M-1 array elements and the reference array element is [ tau ] 1 ,τ 2 ,…,τ M-1 ]. Considering that for narrowband signal a (t- τ) ≡a (t), the array element received signal can be expressed as x=ax 0 (t). Wherein x= [ X ] 0 (t),x 1 (t),…,x M-1 (t)] T ，A is called the steering vector. Where j is an imaginary unit and ω represents an angular frequency.
For a seven-element uniform circular array, taking the radius of the array element as half wavelength, namely r=lambda/2, the guiding vector of the seven-array element can be obtainedThe expression of (3) is as follows:
in the formula (3), theta is a signal pitch angle,is the signal azimuth as shown in fig. 3. j represents an imaginary unit.
The GPS receiver array element received signal R (nT) is expressed as the following formula (4):
in the formula (4), the amino acid sequence of the compound,a steering vector representing the ith GPS signal, A j A steering vector representing the jth interfering signal, S i (nt) represents the received signal of the ith GPS signal at the reference array element, J j (nT) represents the received signal of the jth interfering signal at the reference element, and N (nT) represents the additive white gaussian noise. Here, j represents the number of interference signals, and is different from j representing imaginary units in the foregoing formulas (1) and (3). Common letter i or j table in view of imaginary unitsIt is shown that +.>Is a representation of (a). Thus, when both cases are expressed in a conventional manner, there are cases where j represents a different meaning. But the person skilled in the art should easily recognize that the index j is represented by a number j and the imaginary unit j is represented by an imaginary unit.
In the step 203, an LMS algorithm is adopted to obtain an optimal value W of the weight vector W opt The process of (1) comprises the following steps:
step 2031: calculating weighted average signals y (k) of the rest array element signals except the reference array element;
step 2032: calculating an error signal e (k);
step 2033: the LMS algorithm obtains a weight vector W by utilizing a steepest descent method;
step 2034: obtaining an optimal weight vector W by using an LMS algorithm opt 。
The LMS algorithm minimizes the mean square error of the filtered output signal with the desired signal. In zero-set GPS, the reference element output signal is taken as the desired signal d (k), i.e. d (k) =x 0 (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite The rest of the array element signals except the reference array element are weighted average y (k) as filtering output signals.
y(k)＝W T X(k) (5)
In the formula (5), X (k) = [ X ] 1 (k),x 2 (k),…,x M-1 (k)] T Weight vector w= [ W ] 1 ,w 2 ,…,w M-1 ] T 。
The calculation formula of the error signal e (k) is as follows (6).
e(k)＝d(k)-y(k) (6)
In the formula (6), the desired signal d (k) is a reference array element output signal, and y (k) is a filtered output signal.
The LMS algorithm obtains the formula (7) of the weight vector using the steepest descent method as follows.
In the formula (7), μ represents an iteration step.Representing the gradient, E represents the mathematical expectation. k=0, 1,2, ….
The LMS algorithm obtains the optimal weight W opt ＝[W 1 W 2 …W M-1 ] T The weight of each array element can be expressed by the following formula (8).
W＝[1,-W 1 ,-W 2 ,…,-W M-1 ] T (8)
In the formula (8), M represents the number of array elements, and in fig. 3, the number of array elements is 7.
Directional diagramIs represented by the following formula (9).
In equation (9), abs is an absolute function.
In the step 3, the scintillation function R (nT) in the expression function x (nT) is optimized by optimizing the a (k) sequence by adopting an evolutionary algorithm; the process of optimizing the a (k) sequence is as follows:
step 301: firstly, initializing parameters of an optimization algorithm;
step 302: for each population member, calculating a variation function update gene of the gene mutation;
step 303: calculating a variation function update gene of the cross variation;
step 304: calculating each member evaluation value score;
step 305: the maximum value of all evaluation values score is calculated.
The evolutionary band-limited Gaussian noise interference algorithm optimizes the a (k) sequence through the evolutionary algorithm, so that a flicker function is optimized, and the GPS receiver obtains smaller nulls in the interference direction.
1. Initializing: selecting the number N of GPS signals s Number of interference signals N J Signal length L, number of elements M of GPS receiver, initializing signal parameter signal carrier frequency f c And sampling frequency f s . For each GPS signal and interference signal, initializing its pitch angle θ and azimuth angleWait, initialize weight W, initialize evolutionary algorithm parameter population membership N, gene length N g Gene variation interval [ L, E]Maximum number of iterations N loop Cross rate α, number of cycles N is evaluated t Initializing Gene a for each population member i (j,n)(1≤i≤N J ，1≤j≤N J ，1≤n≤N g ) Initializing the best evaluation max_score and the corresponding gene max_a, etc.
The following steps 2-5 require a cycle N loop And twice.
2. For each member, calculating a mutation function Mutate (a (j, n)) of the gene mutation to update the gene;
the update gene program is as follows:
for i＝1：N
for j＝1：N J
k 1 ＝randi(1，N g /2)*2-1
k 2 ＝randi(1，N g /2)*2
α i (j，k 1 )＝randi(L，E)
α i (j，k 2 )＝randi(L，E)
end
end
wherein randi (A, B) represents taking a random integer within the interval [ A, B ].
3. Calculating a variation function Cross (a (j, n, alpha) of the Cross variation to update the gene;
the calculation program is as follows:
fork＝1：αN
i 1 ＝randi(1,N)
i 2 ＝randi(1,N)
i 3 ＝randi(1,N g /2)
swap(α i1 (1:N J ,i 3 :i 3 +N g /2-1),α i2 (1:N J ,i 3 :i 3 +N g /2-1))
end
wherein swap (a, B) represents exchanges a and B.
4. Calculating each member evaluation value score=score (a (j, n));
the calculation program is as follows:
fori＝1：N
forj＝1：N t
calculating the matrix R (nT) of the received signals (see equation 4 above)
Taking the first behavior of R (nT) as the desired signal d (k), the remaining behaviors X (k)
Calculation of W by LMS algorithm opt (calculated by the above equation 5, the above equation 6, and the above equation 7)
Calculating a pattern(see above equation 8, above equation 9)
Calculating interference direction average nullsWherein->(1≤j≤N J ) Pitch and azimuth for the interfering signal.
end
score(i)＝mean(s)
end
5. The score maximum is calculated and the corresponding a (j, n) is recorded.
The calculation program is as follows:
[max,I]＝max(scores)
ifmax_score<max
max_α＝α I
end
wherein the random value of the random position of the Mutate variation function pair a (k) is taken in the range interval. Cross functions Cross members of alpha N, exchange half of the data and exchange positions randomly. The Score function first calculates the interference signal and then calculates the optimal weight W by LMS algorithm opt Then return to the direction diagramNull depth in the direction of the interfering signal.
The performance difference of the evolutionary band-limited Gaussian noise interference algorithm and the traditional band-limited Gaussian noise interference algorithm is compared through a simulation experiment in the section. Number of interference signals N in experiments j Testing is carried out for 1 to 6 pieces respectively, and the null depth is taken as the average value of 100 groups of sample data. The remaining parameters are: array element number m=7; antenna array radius r=λ/2; sampling rate f s =3.2 GHz; a single sample data length of 10000; the number of GPS signals ns=1; the pitch angle of the GPS signal is 70, and the azimuth angle is 10; the pitch angle of the interference signal is 70, and the azimuth angles are 40, 70, 100, 150, 250 and 320 respectively; signal to noise ratio S/n= -30dB; dry noise ratio J/n=30 dB. The experimental results are shown in table 1.
TABLE 1 zero notch depth contrast for band limited Gaussian noise interference algorithm and algorithm of the invention
It can be seen from table 1 that the null depth of the conventional band-limited gaussian noise algorithm for the interference direction is basically stable at the interference number of 1-4, and the null depth is obviously improved at the interference number of 5-6. The null depth of the evolutionary band-limited gaussian noise interference algorithm has similar conclusions for different numbers of interference. Compared with the traditional band-limited Gaussian noise interference algorithm, the performance gain of the evolved band-limited Gaussian noise interference algorithm is about 5-9 dB, and the performance difference is increased along with the increase of the number of interference.
The invention provides a zero-setting GPS based on an LMS algorithm, which provides an interference algorithm for optimizing a flicker function through an evolutionary algorithm, and the performance of the algorithm is analyzed through the interference zero-sinking depth. The algorithm has the advantages that through the optimized scintillation function, the convergence of the LMS is disturbed better, the better performance gain is obtained, and the algorithm performance is obviously superior to that of the band-limited Gaussian noise interference algorithm.
According to the invention, the band-limited Gaussian noise interference scintillation function is optimized through the evolution algorithm, so that the convergence of an LMS-based self-adaptive algorithm in the GPS receiver is disturbed, the average null depth of the GPS receiver under equal power is lower, and a better interference effect is realized. Simulation experiment results show that the interference algorithm has more obvious interference effect than band-limited Gaussian noise interference when the interference quantity is different.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (4)
1. An algorithm for LMS-based GPS evolutionary band-limited gaussian noise interference, comprising the steps of:
step 1: determining an expression function x (nT) of the interference signal;
step 2: establishing an jammer and GPS receiver model;
step 3: optimizing a flicker function r (nT) in an expression function x (nT) by adopting an evolutionary algorithm;
step 4: determining the influence degree of the interference signal on the receiver;
in step 3, the scintillation function r (nT) in the expression function x (nT) is optimized by optimizing the a (K) sequence,
the expression of the flicker function r (nT) is:
wherein the function isRecord F a (0) =0, k=0, 1,2, …; n represents the sampling point sequence number;
the process of optimizing the a (K) sequence is as follows:
step 301: firstly, initializing parameters of an optimization algorithm;
step 302: for each population member, calculating a variation function update gene of the gene mutation;
step 303: calculating a variation function update gene of the cross variation;
step 304: calculating each member evaluation value score;
step 305: the maximum value of all evaluation values score is calculated.
2. An LMS-based GPS evolutionary band-limited gaussian noise interference algorithm according to claim 1, wherein: in the step (1) of the above-mentioned process,
the complex domain expression of the single interfering signal x (nT) is shown in the following formula (1);
x(nT)＝r(nT)[f 1 (nT)cos(2πf c nT+β)+jf 2 (nT)sin(2πf c nT+β)] (1)
in the formula (1), f c =1575.42 MHz, representing the GPS L1 signal carrier frequency; beta represents an initial phase; t=1/f s Representing a sampling period; f (f) s Representing the sampling frequency; f (f) 1 (nT)) and f 2 (nT) represents white gaussian noise after H (Z) filtering; r (nT) represents a flicker function; n represents the sampling point number and j represents the imaginary unit.
3. An LMS-based GPS evolutionary band-limited gaussian noise interference algorithm according to claim 1, wherein: in the step 2, the process of establishing the model of the jammer and the GPS receiver comprises the following steps:
step 201: determining the arrangement mode of an jammer and a GPS receiver;
step 202: determining an expression function r (nT) of an array element receiving signal of the GPS receiver;
step 203: obtaining an optimal value W of the weight vector W by adopting an LMS algorithm optt ；
Step 204: calculating a pattern
4. A method for LMS-based GPS evolutionary band-limited gaussian noise interference algorithm according to claim 3, wherein: in the step 203, an LMS algorithm is adopted to obtain an optimal value W of the weight vector W opt The process of (1) comprises the following steps:
step 2031: calculating weighted average signals y (k) of the rest array element signals except the reference array element;
step 2032: calculating an error signal e (k);
step 2033: the LMS algorithm obtains a weight vector W by utilizing a steepest descent method;
step 2034: obtaining an optimal weight vector W by using an LMS algorithm opt 。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5940033A (en) * | 1998-01-20 | 1999-08-17 | The United States Of America As Represented By The Secretary Of The Army | Apparatus, methods and computer program for evaluating multiple null forming antenna processors and jammers |
| US6590528B1 (en) * | 2001-12-05 | 2003-07-08 | Rockwell Collins, Inc. | Low cost interference reduction system for GPS receivers |
| EP3188426A1 (en) * | 2015-12-29 | 2017-07-05 | Thales | Method for controlling adaptive interference in a multi-channel receiver |
| CN107462872A (en) * | 2017-07-03 | 2017-12-12 | 蔡绍滨 | A kind of anti-major lobe suppression algorithm |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ES2688755T3 (en) * | 2004-12-17 | 2018-11-06 | The European Union, Represented By The European Commission | Propagation codes for a satellite navigation system |
| US20070127608A1 (en) * | 2005-12-06 | 2007-06-07 | Jacob Scheim | Blind interference mitigation in a digital receiver |
| US8548106B2 (en) * | 2008-09-23 | 2013-10-01 | Board Of Regents, The University Of Texas System | Crosscorrelation interference mitigating position estimation systems and methods therefor |
| DE102009020216A1 (en) * | 2009-05-07 | 2010-12-09 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Concept for determining an estimate of a location of a receiver |
| FR3038390B1 (en) * | 2015-07-03 | 2019-09-20 | Thales | METHOD FOR LOCATING A SIGNAL INTERFERENCE SOURCE OF A SATELLITE NAVIGATION SYSTEM AND ASSOCIATED SYSTEM |
| US9654158B2 (en) * | 2015-10-20 | 2017-05-16 | The Aerospace Corporation | Circuits and methods for reducing an interference signal that spectrally overlaps a desired signal |
| US10656281B2 (en) * | 2016-11-10 | 2020-05-19 | Cable Television Laboratories, Inc. | Systems and methods for interference detection in shared spectrum channels |
| CN111783301B (en) * | 2020-07-02 | 2024-05-07 | 西北大学 | Design method of anti-interference scattering communication system antenna array |
-
2020
- 2020-12-11 CN CN202011468606.XA patent/CN112578410B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5940033A (en) * | 1998-01-20 | 1999-08-17 | The United States Of America As Represented By The Secretary Of The Army | Apparatus, methods and computer program for evaluating multiple null forming antenna processors and jammers |
| US6590528B1 (en) * | 2001-12-05 | 2003-07-08 | Rockwell Collins, Inc. | Low cost interference reduction system for GPS receivers |
| EP3188426A1 (en) * | 2015-12-29 | 2017-07-05 | Thales | Method for controlling adaptive interference in a multi-channel receiver |
| CN107462872A (en) * | 2017-07-03 | 2017-12-12 | 蔡绍滨 | A kind of anti-major lobe suppression algorithm |
Non-Patent Citations (1)
| Title |
|---|
| GPS接收阵列自适应抗干扰算法性能比较;周云锋;李宏;梁宏;廖万友;;计算机仿真(第01期);69-71 * |
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