CN106680837B - A kind of satellite navigation interference suppression algorithm - Google Patents
A kind of satellite navigation interference suppression algorithm Download PDFInfo
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- CN106680837B CN106680837B CN201611149124.1A CN201611149124A CN106680837B CN 106680837 B CN106680837 B CN 106680837B CN 201611149124 A CN201611149124 A CN 201611149124A CN 106680837 B CN106680837 B CN 106680837B
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- 239000013598 vector Substances 0.000 claims abstract description 41
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- 238000000354 decomposition reaction Methods 0.000 claims abstract description 6
- 238000010606 normalization Methods 0.000 claims description 4
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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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- Radar, Positioning & Navigation (AREA)
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Variable-Direction Aerials And Aerial Arrays (AREA)
Abstract
The present invention provides a kind of satellite navigation interference suppression algorithms, the estimated value for receiving data covariance matrix is calculated first, then feature decomposition operation is carried out to the estimated value for receiving data covariance matrix, obtain characteristic value and corresponding feature vector, minimal eigenvalue is sought into derivative action and corresponding feature vector is multiplied to obtain not normalized weight vector, and it is normalized, superposition processing is weighted to the intermediate frequency digital complex signal received using weight vector, the output signal after obtaining AF panel.The present invention can simultaneously effectively inhibit high reject signal and weak jamming signal.
Description
Technical Field
The invention belongs to the field of satellite navigation anti-interference, and relates to an algorithm for inhibiting a suppression type satellite navigation interference signal.
Background
In the signal processing process of the satellite navigation anti-interference antenna system, an interference suppression algorithm is a core part in the whole digital signal processing. The interference suppression algorithm can be divided into two major categories, namely an adaptive nulling algorithm and an optimal digital multi-beam algorithm.
The typical representation of the adaptive null-steering algorithm is a Power Inversion (PI) algorithm, the algorithm does not need prior information such as satellite azimuth information, the null depth is automatically adjusted along with the intensity of interference Power, and a good suppression effect is achieved on strong interference signals, so that the adaptive null-steering algorithm is widely used in engineering application in the field of satellite navigation anti-interference. However, the disadvantage is that the suppression effect for the weak interference signal is poor, so that the application thereof in the weak interference environment or the environment with both strong interference and weak interference is limited.
The optimal digital multi-beam algorithm adopts a digital multiplexing technology and simultaneously forms a plurality of optimal digital receiving beams in a visual field space. A typical optimal digital beam is a Minimum Variance Distortionless Response (MVDR) beam. The main lobe of each optimal digital beam points to one navigation satellite, and the null is formed in the interference direction in a self-adaptive mode. The optimal digital multi-beam algorithm can improve the signal-to-noise ratio of the system output signal while suppressing interference. However, the algorithm needs prior information assistance such as satellite orientation, array attitude and the like, and is complex in structure, large in calculated amount and difficult to widely use in engineering application.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a satellite navigation interference suppression algorithm, which adopts the noise subspace eigenvector of the normalized received data covariance matrix as the adaptive zero-adaptive weighting vector to suppress the interference component in the received signal, can effectively suppress the weak power interference signal component and the strong power interference in the received signal at the same time, and is easy for engineering realization.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
first, a covariance matrix R of received data is calculatedxIs estimated value ofWherein, x (n) is an intermediate frequency digital complex signal vector received by the array antenna of M array elements at n time, the dimension is M multiplied by 1, and M is more than or equal to 2;the dimension size of (A) is M × M; superscript H is the conjugate transpose operator; n is the signal fast beat number required for calculating the estimated value of the received data covariance matrix, and N is more than or equal to 4M;
secondly, carrying out characteristic decomposition operation on the estimated value of the covariance matrix of the received data by adopting a Jacobi algorithm to obtain a characteristic value lambdamAnd corresponding feature vectors qm，m＝1,2,…,M；
The third step: minimum eigenvalue lambdaminCalculating reciprocal and multiplying with corresponding eigenvector to obtain non-normalized weight vector Has a dimension size of mx 1;
the fourth stepFor the weight vectorNormalization processing is carried out to obtain normalized weight vectorWherein,is composed ofA first element of the weight vector;
the fifth step, adopt the weight vector woptCarrying out weighted superposition processing on the intermediate frequency digital complex signal x (n) received in the first step, and carrying out interference suppression on the output signal
The invention has the beneficial effects that: weight vector w obtained by adopting the inventionoptThe weight component corresponding to the interference characteristic vector is omitted, so that the weighted output signal power only contains noise signal components, and strong interference signals and weak interference signals can be effectively suppressed at the same time.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
Detailed Description
The present invention will be further described with reference to the following drawings and examples, which include, but are not limited to, the following examples.
The algorithm of the invention is realized by the following steps:
the first step is as follows: computing covariance moments of received dataArray RxIs estimated value ofThe calculation expression is as follows:
wherein, x (n) is an intermediate frequency digital complex signal vector received by the array antenna of M (M is more than or equal to 2) array elements at the time of n, and the dimension is M multiplied by 1;the dimension size of (A) is M × M; superscript H is the conjugate transpose operator; n is the signal fast beat number required for calculating the estimated value of the covariance matrix of the received data, and N is required to be more than or equal to 4M.
The second step is that: performing characteristic decomposition operation on the estimated value of the covariance matrix of the received data, wherein the characteristic decomposition is realized by adopting a Jacobi algorithm to obtain a characteristic value lambdam(M-1, 2, …, M) and corresponding feature vector qm(m＝1,2,…,M)。
The third step: minimum eigenvalue lambdaminCalculating reciprocal and multiplying with corresponding eigenvector to obtain non-normalized weight vector
Has a dimension size of M × 1.
The fourth step: for the non-normalized weight vector obtained in the third stepNormalization processing is carried out to obtain normalized weight vector woptThe calculation process is shown as the following formula:
wherein,is composed ofThe first element of the weight vector.
The fifth step: adopting the weight vector w calculated in the fourth stepoptPerforming weighted superposition processing on the intermediate frequency digital complex signal x (n) received in the first step, wherein the calculation process is shown as the following formula:
the superscript H is the conjugate transpose operator, and y is the output signal after interference suppression.
For the power inversion algorithm, the weight vector is calculated as follows:
wherein, wPIThe dimension of the weight vector calculated by the power inversion algorithm is Mx 1;for received data covariance matrix estimationThe dimension of the inverse matrix is M multiplied by M; a is0＝[1,0,…,0]TDimension is M multiplied by 1 for constraint vector; k is the number of interference sources and is less than M; delta2Is the system noise power; w is ajamAnd wnoiseThe signal is divided into two parts of interference signals and noise signals which respectively correspond to weight vectors, and the dimension size is M multiplied by 1.
When a strong interference signal exists outside (the power of the interference signal is far greater than that of the noise signal), the eigenvalue for the interference signal pair is far greater than the corresponding eigenvalue of the noise signal, that is, the eigenvalue for the noise signal pair is far greater than that of the interference signal
As can be seen from equation (6), as the interference power increases, the weight component corresponding to the interference eigenvector in the optimal weight value becomes smaller. Therefore, the power inversion method can form deeper zero in the strong interference direction, and the anti-interference capability is stronger. On the contrary, when the powers of the plurality of interference signals are small (slightly larger than the noise signals), the weight component (the first term on the right side of the second equal sign in the formula (5)) corresponding to the interference feature vector in the optimal weight value is slightly smaller than the weight component (the second term on the right side of the second equal sign in the formula (5)) corresponding to the noise feature vector, and the formed null is shallow, so that the anti-interference capability is poor. And the weight vector w obtained by adopting the inventionoptThe weight component corresponding to the interference characteristic vector is omitted, so that the weighted output signal power only contains noise signal components, and strong interference signals and weak interference signals can be effectively suppressed at the same time.
The method is suitable for interference suppression processing of GPS, BDS and GLONASS satellite navigation systems. The embodiment of the invention is illustrated by taking a 4-unit BD2-B3 frequency point anti-interference antenna as an example for resisting two broadband interferences.
Step 1: computing estimates of 4-ary array received data covariance matricesFast sampling rate N of 256, calculatedIs a Hermit matrix with dimensions 4 × 4, calculated as follows:
step 2: performing characteristic decomposition operation on the estimated value of the covariance matrix of the received data by adopting a Jacobi algorithm to obtain a characteristic value lambdam(m-1, 2, …,4) and corresponding feature vector qm(m＝1,2,…,4)。
And step 3: the minimum eigenvalue lambda obtained by the calculation in the step 2minCalculating reciprocal and multiplying with corresponding eigenvector to obtain non-normalized weight vectorThe calculation process is as follows:
and 4, step 4: for the non-normalized weight vector obtained in step 3Normalization processing is carried out to obtain normalized weight vector woptThe calculation process is shown in formula (3).
And 5: adopting the weight vector w calculated in the step 4optAnd (3) performing weighted superposition processing on the intermediate frequency digital complex signals x (n) received in the first step and outputting the signals, wherein the calculation process is shown as a formula (4).
At this point, the interference suppression processing for the 4-unit BD2-B3 frequency-point received signals is completed.
Claims (1)
1. A satellite navigation interference suppression algorithm, comprising the steps of:
first, a covariance matrix R of received data is calculatedxIs estimated value ofWherein, x (n) is an intermediate frequency digital complex signal vector received by the array antenna of M array elements at n time, the dimension is M multiplied by 1, and M is more than or equal to 2;the dimension size of (A) is M × M; superscript H is the conjugate transpose operator; n is the signal fast beat number required for calculating the estimated value of the received data covariance matrix, and N is more than or equal to 4M;
secondly, carrying out characteristic decomposition operation on the estimated value of the covariance matrix of the received data by adopting a Jacobi algorithm to obtain a characteristic value lambdamAnd corresponding feature vectors qm，m＝1,2,…,M；
The third step: minimum eigenvalue lambdaminCalculating reciprocal and multiplying with corresponding eigenvector to obtain non-normalized weight vector Has a dimension size of mx 1;
the fourth step, to the weight vectorNormalization processing is carried out to obtain normalized weight vectorWherein,is composed ofA first element of the weight vector;
the fifth step, adopt the weight vector woptCarrying out weighted superposition processing on the intermediate frequency digital complex signal x (n) received in the first step, and carrying out interference suppression on the output signal
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| CN103630910A (en) * | 2013-12-13 | 2014-03-12 | 武汉大学 | Anti-interference method of GNSS (global navigation satellite system) receiver equipment |
| CN104536018A (en) * | 2015-01-06 | 2015-04-22 | 中国人民解放军国防科学技术大学 | GNSS multi-satellite unified capture method using array antenna anti-interference technology |
| CN105137454A (en) * | 2015-07-22 | 2015-12-09 | 北京航空航天大学 | Anti-interference algorithm FPGA realization method based on covariance matrix characteristic decomposition and realization device thereof |
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| CN103630910A (en) * | 2013-12-13 | 2014-03-12 | 武汉大学 | Anti-interference method of GNSS (global navigation satellite system) receiver equipment |
| CN104536018A (en) * | 2015-01-06 | 2015-04-22 | 中国人民解放军国防科学技术大学 | GNSS multi-satellite unified capture method using array antenna anti-interference technology |
| CN105137454A (en) * | 2015-07-22 | 2015-12-09 | 北京航空航天大学 | Anti-interference algorithm FPGA realization method based on covariance matrix characteristic decomposition and realization device thereof |
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