CN107907854A - DOA estimation new methods under impulse noise environment - Google Patents
DOA estimation new methods under impulse noise environment Download PDFInfo
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Abstract
For more preferable impulse noise mitigation and the interference with band signal, invention defines a kind of new cyclic autocorrelation function, propose it is a kind of it is new relevant MUSIC algorithms are circulated based on Sigmoid, and in the DOA estimations that the algorithm is applied under Stable distritation noise.Simulation result shows, it is proposed by the present invention based on SCC MUSIC compared with the FLOM MUSIC algorithms of classical MUSIC algorithms, the CRCO MUSIC based on class joint entropy and base Fractional Lower Order Moments, more preferable estimated result can be obtained, there is more obvious advantage especially under high impulse noise circumstance.
Description
Technical Field
The invention belongs to the field of communication and information systems, and particularly relates to a novel DOA estimation method in an impulse noise environment.
Background
DOA (Direction of Arrival) estimation is one of the basic problems in array signal processing, and is widely used in the fields of radar, sonar, radio communication, and the like. Multiple Signal Classification (MUSIC) algorithms can achieve super-resolution estimation of DOA, but traditional algorithms mostly assume that background noise follows gaussian distribution. In fact, the noise may be strongly impulsive due to the influence of natural factors (such as atmospheric noise, sea clutter, etc.) and human factors (such as electromagnetic equipment such as motors, etc.), and it is more appropriate to describe the noise by using Alpha stable distribution. Unlike gaussian distributed random variables, alpha stably distributed random variables do not have finite second moments, and the traditional MUSIC method is no longer applicable.
To suppress the influence of Alpha stable distribution noise, zhangjinfeng et al proposed a DOA estimation method based on Fractional low Order Statistics (flo). Although the method obtains a better estimation effect, certain limitations exist: firstly, the order p must satisfy 1< -p < alpha or 0< -p < alpha/2, secondly, if the prior knowledge of alpha is not available or the value can not be estimated correctly, the improper value of the order can cause the performance of the algorithm to be reduced or even be invalid, so the estimation effect of the characteristic index alpha can influence the performance of the algorithm. In order to overcome the limitations, zhang jin Feng et al propose a DOA estimation method based on class M estimation and a CRCO-MUSIC algorithm based on class correlation entropy. Although the above algorithm has better anti-noise performance and signal applicability, it mainly has better effect on weak impulse suppression, and if α <1 is in a high impulse noise environment, the algorithm performance is significantly reduced.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in the working environment of systems such as wireless communication and the like, various noises and interferences exist, so that a target signal received by a receiver is often submerged in the noises and the interferences, and in order to better inhibit the influence of impulse noises and same frequency interferences and improve the robustness of a DOA estimation algorithm, the invention provides a DOA estimation method in the impulse noise environment.
The technical scheme adopted by the invention for solving the technical problem is as follows: a new DOA estimation method based on Sigmoid cyclic correlation under an impulse noise environment is provided, and the method comprises the following steps:
step 1: establishing a signal receiving model
Considering that L far-field narrow-band signals are incident on a uniform linear array with an array element number of M, under the assumption that a signal source is a narrow band, a mathematical model of a received signal can be expressed as:
X(t)＝A(θ)S(t)+N(t) (1)
wherein, X (t) = [ X ] 1 (t),…,x M (t)] T Is an observed signal vector;is a cyclic frequencyIncident signal vector of rate ε, K a &L, other L-K a The signals have different cyclic frequencies or do not have cyclostationary characteristics;is the incident signal direction vector of interest, andθ k the incidence angle of the kth signal source; n (t) = [ N = 1 (t),n 2 (t),…n M (t)] T The array noise is an M multiplied by 1 dimensional noise data vector of the array, the noise is additive noise which follows S alpha S distribution, the noise of each array element is independent, and the noise and the signal are independent;
and 2, step: sigmoid-Cyclic Correlation-MUSIC algorithm
Defining a new circular correlation function based on the characteristic inspiration of the cyclostationary signal, and recording the new circular correlation function asThe definition formula is as follows:
wherein the content of the first and second substances,<·> t represents a time average;
two ends of the formula (1) are subjected to Sigmoid cyclic correlation to obtain a Sigmoid cyclic covariance matrix of the received signal
Wherein, the first and the second end of the pipe are connected with each other,si as incident signalgmoid cyclic autocorrelation array, σ 2 Is the ambient noise power; since the incident signal is not circularly correlated with the noise and the noise does not have a cyclostationary property, i.e. the noise contributes zero to the circular autocorrelation matrix, i.e. σ 2 I＝0；
Equation (3) can be obtained by singular value decomposition:
the unitary matrixes U and V respectively represent a left singular matrix and a right singular matrix, corresponding singular vectors are obtained by column vectors in the singular matrixes, singular values are obtained by elements on diagonal lines in the diagonal matrixes S, and signals and noise are circularly uncorrelated, so that the singular matrixes U and V respectively represent a left singular matrix and a right singular matrix, and the corresponding singular vectors are obtained by column vectors in the singular matrixes S and the singular values are obtained by elements on the diagonal lines in the diagonal matrixes SNamely thatIn only K a A non-zero singular value, the rest M-K a The singular value is zero, and the unitary matrix U and V are correspondingly divided into two parts:
U＝[U S U N ] V＝[V S V N ] (5)
wherein U is S And V S From K a Singular vector components, U, corresponding to non-zero singular values N And V N The singular vectors corresponding to zero singular values form; by U S Or V S The subspace formed by the column vectors is the signal subspace composed of U N Or V N The subspace formed by the column vectors of (a) is a noise subspace; the signal subspace and the noise subspace satisfy an orthogonal relationship, then:
the direction vector a (θ) is linearly uncorrelated, and the steering vector of the signal subspace is also orthogonal to the noise space, which can be obtained from equations (3) and (6), that is:
a H (θ)U N ＝0 (7)
due to the presence of noise, a (θ) and U N Can not be completely orthogonal, so that the DOA estimation is actually realized by spectral peak search, namely the spectral estimation formula of the Sigmoid Cyclic correction MUSIC algorithm is
And (5) performing spectrum search on the formula (8) to obtain the DOA estimation based on the SCC-MUSIC algorithm.
The invention provides a DOA estimation method in an impulse noise environment. The algorithm is simple and effective, can simultaneously resist the influence of Alpha stable distribution noise and same frequency interference, and can still realize accurate estimation of DOA under strong pulse noise.
Drawings
FIG. 1 is a graph of algorithm performance as a function of GSNR;
FIG. 2 is a graph of algorithm performance versus fast beat number;
FIG. 3 is a graph of algorithm performance as a function of characteristic index;
fig. 4 is the angular resolution of the algorithm.
Wherein: (a) are DOA estimation success rates; both (b) and (b) are DOA estimated RMSE.
Detailed Description
The invention is further described with reference to the following figures and detailed description of embodiments.
A DOA estimation method under impulse noise environment comprises the following steps:
step 1: establishing a signal receiving model
Considering that L far-field narrow-band signals are incident on a uniform linear array with an array element number of M, under the assumption that a signal source is a narrow band, a mathematical model of a received signal can be expressed as:
X(t)＝A(θ)S(t)+N(t) (1)
wherein, X (t) = [ X = 1 (t),…,x M (t)] T Is an observed signal vector;for an incident signal vector of cyclic frequency epsilon, K a &L, other L-K a The individual signals have different cyclic frequencies or do not have a cyclostationary property;is the incident signal direction vector of interest, andθ k the angle of incidence of the kth signal source; n (t) = [ N = 1 (t),n 2 (t),…n M (t)] T The array noise is an M multiplied by 1 dimensional noise data vector of the array, the noise is additive noise which follows S alpha S distribution, the noise of each array element is independent, and the noise and the signal are independent;
step 2: sigmoid-Cyclic Correlation-MUSIC algorithm
The Sigmoid transform is a common nonlinear transform and has wide application, and is defined as follows:
the Sigmoid transform has the following properties for the S α S distribution with the position parameter μ = 0.
Properties 1: if x (t) follows a S α S distribution with position parameter μ =0, sigmoid [ x (t) ] is a symmetric distribution with zero mean.
Properties 2: if x (t) follows the distribution of S.alpha.S with the position parameter μ =0 and the dispersion coefficient γ>, 0, then: | | Sigmoid [ x (t)]|| α >, 0, and Sigmoid [ x (t)]The mean value is zero.
Properties 3: if x (t) follows the S α S distribution of the position parameter μ =0, then Sigmoid [ x (t) ] has limited second order statistics and the mean is zero (i.e., sigmoid [ x (t) ] is a second order moment process).
Conventional delay estimation methods suffer performance degradation when there is interference in the received signal that overlaps the source signal spectrum. In view of the fact that many signals used in communication systems are cyclostationary signals, scholars have proposed a DOA estimation method based on the cyclostationary characteristics of the signals. Generally, because the interference and the signal have different cyclic frequencies and the noise does not have the cyclostationary characteristic, the cyclic DOA estimation method based on the cyclostationary characteristic has stronger anti-noise and same-frequency-band interference capability than the traditional DOA estimation method, and has better estimation effect even under the condition of low signal-to-noise ratio related interference. Therefore, based on the characteristic elicitation of the cyclostationary signal, a new Cyclic Correlation (SCC) function is defined and recorded asThe formula is defined as follows:
wherein, the first and the second end of the pipe are connected with each other,<·> t representing a time average.
Two ends of the formula (1) are subjected to Sigmoid cyclic correlation to obtain a Sigmoid cyclic covariance matrix of the received signal
Wherein, the first and the second end of the pipe are connected with each other,sigmoid cyclic autocorrelation array, sigma, for the incident signal 2 Is the ambient noise power; due to incidenceThe signal is not cyclically correlated with the noise and the noise does not have a cyclostationary property, i.e. the contribution of the noise to the cyclic autocorrelation matrix is zero, i.e. σ 2 I＝0；
The Sigmoid cyclic correlation MUSIC algorithm is different from the conventional MUSIC algorithm, the Sigmoid cyclic autocorrelation matrix of the observed signal does not meet the Hamilter matrix, and the singular value decomposition is generally used for replacing the characteristic value decomposition of the conventional MUSIC algorithm to obtain the noise subspace and the signal subspace of the signal.
Equation (3) can be obtained by singular value decomposition:
the unitary matrix U and V respectively represent left singular matrix and right singular matrix, corresponding singular vector is obtained from column vector in singular matrix, singular value is obtained from diagonal element in diagonal matrix S, signal and noise are circulation irrelevantNamely, it isIn only K a A non-zero singular value, the rest M-K a The singular value is zero, and the unitary matrix U and V are correspondingly divided into two parts:
U＝[U S U N ] V＝[V S V N ] (5)
wherein U is S And V S From K a Singular vector components, U, corresponding to non-zero singular values N And V N The singular vectors corresponding to zero singular values form; by U S Or V S The subspace formed by the column vectors is a signal subspace consisting of U N Or V N The subspace formed by the column vectors of (a) is a noise subspace; the signal subspace and the noise subspace satisfy an orthogonal relationship, then:
the direction vector a (θ) is linearly uncorrelated, and the steering vector of the signal subspace is also orthogonal to the noise space, which can be obtained from equations (3) and (6), that is:
a H (θ)U N ＝0 (7)
due to the presence of noise, a (θ) and U N Can not be completely orthogonal, so that the DOA estimation is actually realized by searching spectral peaks, namely the spectral estimation formula of a Sigmoid Cyclic MUSIC algorithm is
And (4) carrying out spectrum search on the formula (8) to obtain the DOA estimation based on the SCC-MUSIC algorithm.
The beneficial effects of the invention can be further illustrated by the following simulations:
the experimental conditions are as follows:
the alpha stable distribution noise has no finite second moment, so the signal-to-noise ratio of S alpha S process in the invention adopts generalized signal-to-noise ratio (GSNR)To describe it. Wherein gamma (gamma)>, 0) represents the dispersion coefficient of the S.alpha.S noise,representing the signal power. The experiment adopts 8 array element uniform linear arrays, the array element spacing is c/2 epsilon, and epsilon is the cycle frequency. The incident source is two far-field BPSK signals, the incident angle of a target Signal (SOI) is 10 degrees, the carrier frequency is 150MHz, the incident angle of an interference Signal (SNOI) is 50 degrees, and the carrier frequency is 100MHz.
The invention uses two indexes to evaluate the performance of the algorithm: DOA estimation success rate and Root Mean Square Error (RMSE). This DOA estimation is considered successful when the incidence angle estimation errors of 2 sources do not exceed 3 °. The estimation success rate is the ratio of the number of estimation successes to the number of random experiments. RMSE of DOA estimation is defined as
Wherein, L is the number of estimated successes,and withIs the parameter θ 1 And theta 2 An estimate of (d). The results are statistically obtained from 300 random experiments.
The invention simultaneously compares the classic MUSIC algorithm, the FLOM-MUSIC algorithm based on fractional low-order moment, the CRCO-MUSIC algorithm based on similar correlation entropy and the SCC-MUSIC algorithm.
The experimental contents are as follows:
experiment 1: effect of GSNR on algorithm performance. Fig. 1 gives the estimation results under different GSNRs. Wherein the noise characteristic index α =1.5 and the number of snapshots N =100. It can be seen that all algorithms improve significantly with the increase of GSNR. Compared with other 4 algorithms, the classical MUSIC algorithm has poorer performance in a stable distributed noise environment. Under the environment of low signal-to-noise ratio, the SCC-MUSIC algorithm and the CRCO-MUSIC algorithm can obtain higher estimation success rate, and the SCC-MUSIC algorithm has lower estimation error; and under the environment of high signal-to-noise ratio, the SCC-MUSIC algorithm has better performance.
Experiment 2: impact of fast beat number on algorithm performance. Fig. 2 shows the effect of the number of snapshots N on the performance of the algorithm. S α S noise figure α =1.5 and gsnr was set to 4dB. It can be found that in addition to the classical MUSIC algorithm, the RMSE of all 4 other algorithms decreases with the increase of the number of fast beats, wherein the estimation success probabilities of the SCC-MUSIC, the CRCO-MUSIC and the ACO-MUSIC 3 algorithms are similar, but the SCC-MUSIC algorithm can obtain a lower estimation error.
Experiment 3: the influence of the noise figure of merit is stably distributed. Fig. 3 shows the estimation results of the algorithm under different noise figure indexes, wherein the fast beat number N =100 and the gsnr is 4dB. It can be seen that the algorithm has very significant advantages in a high impulsive noise environment. Furthermore, consistent with the phenomena of fig. 2 and 3, the noise figure of merit a has less impact on the performance of the algorithm herein.
Experiment 4: angular resolution. Fig. 4 shows the angular resolution capability of the different algorithms. Angle of incidence theta of source 1 1 Changing from 0 to 18, the angle of incidence of the source 2 is fixed at θ 2 =20 °, noise figure α =1.5, gsnr 4dB. As can be seen from the figure, the minimum angular difference that the algorithm can resolve is 6 °, which is superior to other algorithms. Furthermore, it can be found that the estimation success rate of the method is higher when the angle difference between the two sources is small, and the method has a weak advantage in RMSE when the angle difference between the two sources is large.
Claims (1)
1. A novel DOA estimation method under an impulse noise environment is characterized by comprising the following steps:
step 1: establishing a signal reception model
Considering that L far-field narrow-band signals are incident on a uniform linear array with an array element number of M, under the assumption that a signal source is a narrow band, a mathematical model of a received signal can be expressed as:
X(t)＝A(θ)S(t)+N(t) (1)
wherein, X (t) = [ X ] 1 (t),…,x M (t)] T Is an observed signal vector;for an incident signal vector of cyclic frequency epsilon, K a &L, other L-K a The individual signals have different cyclic frequencies or do not have a cyclostationary property;is the incident signal direction vector of interest, andθ k the incidence angle of the kth signal source; n (t) = [ N = 1 (t),n 2 (t),…n M (t)] T The array noise is an M multiplied by 1 dimensional noise data vector of the array, the noise is additive noise which follows S alpha S distribution, the noise of each array element is independent, and the noise and the signal are independent;
step 2: sigmoid-Cyclic Correlation-MUSIC algorithm
Defining a new circular correlation function based on the characteristic inspiration of the cyclostationary signal, and recording the new circular correlation function asThe definition formula is as follows:
wherein the content of the first and second substances,<·> t represents a time average;
two ends of the formula (1) are subjected to Sigmoid cyclic correlation to obtain a Sigmoid cyclic covariance matrix of the received signal
Wherein the content of the first and second substances,sigmoid cyclic autocorrelation array, sigma, for incident signals 2 Is the ambient noise power; since the incident signal is not circularly correlated with the noise and the noise does not have a cyclostationary property, i.e. the noise contributes zero to the circular autocorrelation matrix, i.e. σ 2 I＝0；
Equation (3) can be obtained by singular value decomposition:
the unitary matrixes U and V respectively represent a left singular matrix and a right singular matrix, corresponding singular vectors are obtained by column vectors in the singular matrixes, singular values are obtained by elements on diagonal lines in the diagonal matrixes S, and signals and noise are circularly uncorrelated, so that the singular matrixes U and V respectively represent a left singular matrix and a right singular matrix, and the corresponding singular vectors are obtained by column vectors in the singular matrixes S and the singular values are obtained by elements on the diagonal lines in the diagonal matrixes SNamely, it isIn only K a A non-zero singular value, the rest M-K a The singular value is zero, and the unitary matrix U and V are correspondingly divided into two parts:
U＝[U S U N ] V＝[V S V N ] (5)
wherein U is S And V S From K a Singular vector components, U, corresponding to non-zero singular values N And V N The singular vectors corresponding to zero singular values form; from U S Or V S The subspace formed by the column vectors is the signal subspace composed of U N Or V N The subspace formed by the column vectors of (a) is a noise subspace; the signal subspace and the noise subspace satisfy an orthogonal relationship, then:
the direction vector a (θ) is linearly uncorrelated, and the steering vector of the signal subspace is also orthogonal to the noise space, which can be obtained from equations (3) and (6), that is:
a H (θ)U N ＝0 (7)
a (theta) and U due to the presence of noise N Can not be completely orthogonal, so that the DOA estimation is actually realized by spectral peak search, namely the spectral estimation formula of the Sigmoid Cyclic correction MUSIC algorithm is
And (5) performing spectrum search on the formula (8) to obtain the DOA estimation based on the SCC-MUSIC algorithm.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108597016A (en) * | 2018-04-19 | 2018-09-28 | 西安交通大学 | Torr-M-Estimators basis matrix robust estimation methods based on joint entropy |
| CN108957387A (en) * | 2018-05-21 | 2018-12-07 | 西安电子科技大学 | A kind of satellite-signal two-dimentional angle estimation method and system |
| CN108957416A (en) * | 2018-04-17 | 2018-12-07 | 大连大学 | Linear frequency-modulated parameter estimating method based on fractional order power spectral density under impulse noise environment |
| CN109188344A (en) * | 2018-08-23 | 2019-01-11 | 北京邮电大学 | Based on mutually circulation correlation MUSIC algorithm information source number and arrival bearing's angular estimation method under impulse noise environment |
| CN114563763A (en) * | 2022-01-21 | 2022-05-31 | 青海师范大学 | Underwater sensor network node distance measurement positioning method based on return-to-zero neurodynamics |
| CN118018372A (en) * | 2024-04-09 | 2024-05-10 | 中国人民解放军海军工程大学 | Improved MSK carrier wave estimation method based on generalized cyclic correlation entropy |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001324556A (en) * | 2000-05-12 | 2001-11-22 | Matsushita Electric Ind Co Ltd | Arrival bearing estimating device |
| CN106054123A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Sparse L-shaped array and two-dimensional DOA estimation method thereof |
| CN106483493A (en) * | 2016-09-13 | 2017-03-08 | 电子科技大学 | A kind of sparse double parallel linear array and estimating two-dimensional direction-of-arrival method |
| CN106526530A (en) * | 2016-09-30 | 2017-03-22 | 天津大学 | Propagation operator-based 2-L type array two-dimensional DOA estimation algorithm |
| CN106569172A (en) * | 2016-10-13 | 2017-04-19 | 北京邮电大学 | Two-dimensional doa estimation method |
| CN107255796A (en) * | 2017-07-25 | 2017-10-17 | 西安交通大学 | Arrowband near-field signals source localization method under a kind of non-uniform noise |
-
2017
- 2017-10-31 CN CN201711040857.6A patent/CN107907854B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001324556A (en) * | 2000-05-12 | 2001-11-22 | Matsushita Electric Ind Co Ltd | Arrival bearing estimating device |
| CN106054123A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Sparse L-shaped array and two-dimensional DOA estimation method thereof |
| CN106483493A (en) * | 2016-09-13 | 2017-03-08 | 电子科技大学 | A kind of sparse double parallel linear array and estimating two-dimensional direction-of-arrival method |
| CN106526530A (en) * | 2016-09-30 | 2017-03-22 | 天津大学 | Propagation operator-based 2-L type array two-dimensional DOA estimation algorithm |
| CN106569172A (en) * | 2016-10-13 | 2017-04-19 | 北京邮电大学 | Two-dimensional doa estimation method |
| CN107255796A (en) * | 2017-07-25 | 2017-10-17 | 西安交通大学 | Arrowband near-field signals source localization method under a kind of non-uniform noise |
Non-Patent Citations (1)
| Title |
|---|
| HANBING WANG 等: "DOA Estimation Based on Sparse Signal Recovery Utilizing", 《JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING》 * |
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| CN108957416B (en) * | 2018-04-17 | 2022-03-08 | 大连大学 | Linear frequency modulation signal parameter estimation method under impulse noise environment |
| CN108597016A (en) * | 2018-04-19 | 2018-09-28 | 西安交通大学 | Torr-M-Estimators basis matrix robust estimation methods based on joint entropy |
| CN108957387A (en) * | 2018-05-21 | 2018-12-07 | 西安电子科技大学 | A kind of satellite-signal two-dimentional angle estimation method and system |
| CN108957387B (en) * | 2018-05-21 | 2022-06-21 | 西安电子科技大学 | Method and system for estimating two-dimensional arrival angle of satellite signal |
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| US11658752B1 (en) | 2022-01-21 | 2023-05-23 | Qinghai Normal University | Node positioning method for underwater wireless sensor network (UWSN) based on zeroing neural dynamics (ZND) |
| CN118018372A (en) * | 2024-04-09 | 2024-05-10 | 中国人民解放军海军工程大学 | Improved MSK carrier wave estimation method based on generalized cyclic correlation entropy |
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