DOA estimation method based on INCK in impulse noise environment

Abstract

The invention discloses a DOA estimation method based on INCK in an impulse noise environment. The method comprises the following steps: receiving a signal through a uniform linear array antenna to obtain received signal information # imgabs0 #; the method comprises the following steps: calculating a local information entropy # imgabs1 # under single snapshot by a local entropy calculation formula according to received signal information, calculating an infinite norm under single snapshot by combining an infinite norm calculation formula, and constructing a semi-infinite norm Cauchy kernel and a full infinite norm Cauchy kernel; adjusting the action size of the two Cauchy kernels according to the size of the local information entropy # imgabs2 #, and establishing a weight # imgabs3 # corresponding to each piece of received signal information according to the action size of the two Cauchy kernels; and performing weighting processing on the # imgabs4 # to obtain a # imgabs5 #, constructing a covariance matrix # imgabs7 # for the # imgabs6 #, and performing DOA estimation by adopting a rotation invariant subspace algorithm. According to the method, the effect of the infinite norm Cauchy kernel is adjusted through the local information entropy # imgabs8 #, the abnormal value in the received signal is suppressed, and effective estimation of the signal DOA under the alpha stable distribution impulse noise is realized.

Description

Technical Field

[0001] The present invention belongs to the field of Direction Of Arrival (DOA) estimation, and in particular relates to an INCK-based DOA estimation method in an impulse noise environment. Background Art

[0002] In recent years, the innovative application of information entropy theory in the field of array signal processing has attracted widespread attention in the academic community. It provides a new theoretical framework for DOA estimation by quantifying the uncertainty characteristics of the signal space. Traditional DOA estimation methods generally construct covariance matrices based on second-order statistics, and their theories are based on the strong assumption that environmental noise obeys Gaussian distribution. However, in actual engineering scenarios, affected by complex electromagnetic interference, multipath effects and sudden noise sources, the statistical characteristics of noise often show significant non-Gaussian characteristics. In such high-impulse noise environments, the high-order statistics of the received signal have serious divergence phenomena, resulting in the covariance matrix that the traditional methods rely on showing pathological characteristics, which in turn causes a sharp drop in DOA estimation accuracy or even algorithm failure. Further research shows that although the existing robustness improvement schemes based on second-order moments (such as fractional low-order moment method) can alleviate the impact of impulse noise under certain conditions, their performance is limited by the identifiability of the noise prior parameters and lacks the ability to adaptively adjust the non-stationary random process of the noise statistical characteristics. Therefore, how to achieve robust DOA estimation without noise prior information under the background of strong impulse noise has become a core challenge that needs to be broken through in the field of array signal processing. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention provides a DOA estimation method based on INCK in an impulse noise environment. Specifically, INCK refers to Infinity Norm Cauchy Kernel based on local entropy, that is, the infinite norm Cauchy kernel of local entropy.

[0004] The present invention provides a DOA estimation method based on INCK in an impulse noise environment, comprising the following steps: Step (1): Receive the signal through the uniform linear array antenna to obtain the received signal information. ; Step (2), based on the received signal information, calculate the local information entropy under a single snapshot using the local entropy calculation formula ; Step (3), according to the received signal information, the infinite norm of a single snapshot is calculated by the infinite norm calculation formula, and a semi-infinite norm Cauchy kernel and a full infinite norm Cauchy kernel are constructed; Step (4), based on the obtained local information entropy , semi-infinite norm Cauchy kernel and full infinite norm Cauchy kernel, construct each received signal information The corresponding weight ; Step (5): According to the obtained weight, the received signal information Weighted processing is performed to obtain , and Constructing the covariance matrix , and then use the ESPRIT / MUSIC algorithm to estimate DOA.

[0005] Furthermore, the array antenna of the uniform linear array structure in step (1) is composed of M The array elements are composed of array elements, and the array element spacing is ,in , is the carrier wavelength.

[0006] Furthermore, the received signal information in step (1) for:

[0007] in, is the direction matrix, is the direction vector, j is an imaginary unit, M is the number of array elements, For the k The angles at which the signal sources are located, is an impulse noise term that obeys a symmetric characteristic exponential α stable distribution, and ; is the signal vector, where Indicates k A signal, t For quick shots ( ).

[0008] Furthermore, the local information entropy in step (2) For the t The local information entropy of a snapshot:

[0009]

[0010] in, For the i The first t A quick shot of the signal information; To take the absolute value.

[0011] Furthermore, the step (3) includes the following steps: 3-1. Calculate the infinity norm for each snapshot:

[0012] in To take the maximum value among them.

[0013] 3-2. Construct the Cauchy kernel based on the infinite norm of each snapshot obtained:

[0014] in is the semi-infinite norm Cauchy kernel; is the fully infinite norm Cauchy kernel.

[0015] Furthermore, the weight W in step (4) is:

[0016] in is the hyperbolic tangent function.

[0017] Furthermore, the signal information after weighted processing in step (5) for:

[0018] Among them, the size of the signal information Y is the same as the size of the received signal information x; construct the signal information after weighted processing The covariance matrix of :

[0019] in, represents the conjugate transpose.

[0020] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention uses the Local Entropy-Infinite Norm Cauchy Kernel (INCK) based on local entropy to improve the accuracy of DOA estimation in harsh environments; in step (4), the weights are constructed using local entropy and the Cauchy kernel , the received signal is weighted After processing, the impulse noise in the received signal is suppressed without requiring prior information about the noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the uniform linear array structure of the present invention.

[0022] Figure 2 When 5 signal sources are incident on the array, =1.5. Schematic diagram of the peak search obtained from a single MC experiment.

[0023] Figure 3 When two signal sources are incident on the array, =1.5. Schematic diagram of the peak search obtained from a single MC experiment.

[0024] Figure 4 When three signal sources are incident on a uniform linear array, M=10, 1000 MC experiments are run using the method of the present invention and other algorithms. =1.5, schematic diagram of RMSE performance under different generalized signal-to-noise ratio conditions.

[0025] Figure 5 When three signal sources are incident on a uniform linear array, =0.8, M=10, and running M=101000 MC experiments. Schematic diagram of RMSE performance of the method of the present invention and other algorithms under different snapshot numbers.

[0026] Figure 6 When three signal sources are incident on a uniform linear array, =1.2, M=10, and 1000 MC experiments are run. Schematic diagram of RMSE performance of the method of the present invention and other algorithms under different snapshot numbers.

[0027] Figure 7 When three signal sources are incident on a uniform linear array, where M=10, 1000 MC experiments are run using the proposed method and other algorithms at different characteristic indexes. Schematic diagram of RMSE performance under different conditions. DETAILED DESCRIPTION

[0028] The present invention is further described in detail below with reference to the accompanying drawings.

[0029] Most DOA estimation methods use the second-order statistics of the Gaussian noise model. However, in practical situations (such as radar echoes, low-frequency atmospheric noise, and underwater acoustic signal research), the noise is composed of irregular pulses or spikes with short duration and large amplitude, and the traditional second-order statistics are no longer applicable. This kind of impulse noise can usually be modeled using the α-stable distribution, which has good applicability and its characteristic function is It can be expressed as: ; ; ; in, is the variable of the characteristic function, is the characteristic index, j is an imaginary unit, is the dispersion parameter, and its meaning is consistent with the variance of the Gaussian distribution; is the skewness parameter, is a positional parameter. The distribution is symmetrical when Stable distributed noise ( , Symmetric Alpha-Stable Distribution Noise).

[0030] The present invention provides a DOA estimation method based on INCK in an impulse noise environment, which specifically includes the following steps: Step (1): Receive the signal through the uniform linear array antenna to obtain the received signal information. .

[0031] like Figure 1 The array antenna structure shown is composed of M The uniform linear array of array elements is composed of the first sensor as the reference element, and the array element spacing is ,in , is the carrier wavelength. Assume K The DOAs are Narrowband signal Incidence on Figure 1 On the nested linear array shown, the array receiving signal can be expressed as:

[0032] in, is the direction matrix, is the direction vector, j is an imaginary unit, M is the number of array elements, For the k The angles at which the signal sources are located, It is subject to the symmetric characteristic index The impulse noise term of the stable distribution, and ; is the signal vector, where Indicates k A signal, t For quick shots ( ).

[0033] Step (2), based on the received signal information, calculate the local information entropy under a single snapshot using the local entropy calculation formula .

[0034] Local information entropy Calculated by the following formula: ; ; in, For the i The first t A quick shot of the signal information; To take the absolute value.

[0035] Step (3): according to the received signal information, the infinite norm of a single snapshot is calculated by the infinite norm calculation formula, and a semi-infinite norm Cauchy kernel and a full infinite norm Cauchy kernel are constructed.

[0036] Compute the infinity norm for each snapshot: ; Construct the Cauchy kernel based on the infinite norm obtained for each snapshot: ; in is the semi-infinite norm Cauchy kernel; is the fully infinite norm Cauchy kernel.

[0037] Step (4), based on the obtained And the Cauchy kernel, construct the weight W corresponding to each received signal information.

[0038] According to the local information entropy obtained in step (2) and step (3) and infinite norm Cauchy kernel, the local information entropy Through the hyperbolic tangent function, we get the weight of the semi-infinite norm Cauchy kernel a , construct the weight corresponding to each received signal information : ; in is the hyperbolic tangent function.

[0039] Step (5): According to the obtained weight information, Weighted processing is performed to obtain , and Constructing the covariance matrix , and then the ESPRIT algorithm is used to estimate DOA.

[0040] The weighted signal information for: ; Among them, the size of Y is the same as the size of x; construct the signal information after weighted processing The covariance matrix of : ; in, represents the conjugate transpose.

[0041] right Perform eigenvalue decomposition: ; in, , , is the noise variance; .Pick The characteristic value of K The larger eigenvalues ​​constitute the signal subspace estimate and are divided into and Two parts, constructing the matrix And perform eigenvalue decomposition, and then Decompose into Submatrix of : ; ; ; in, represents the inverse matrix. Then calculate The eigenvalue of , and then calculate the DOA estimate by the formula Values: ; in To invert a trigonometric function, Indicates calculating the angle corresponding to the complex number.

[0042] In order to verify the effect of the above method, multiple simulation experiments were conducted in this embodiment, and the experimental performance was analyzed. In an impulse noise environment, the generalized signal-to-noise ratio is defined as: ; in is the signal vector, is the dispersion parameter, is the expectation operator.

[0043] The performance estimation criterion is the joint root mean square error (RMSE) defined as: ; in, For the The Monte Carlo process k The accurate estimate of the DOA of each source is For the k is the angle at which the signal sources are located, represents the number of information sources, and MC represents the number of Monte Carlo tests.

[0044] The present invention (INCK-ESPRIT) is compared with existing methods, namely: Phase Fractional Low-Order Moment ESPRIT (PFLOM-ESPRIT) method, Signed Covariance Matrix ESPRIT (SCM-ESPRIT) method, Infinity-Norm Normalized ESPRIT (IN-ESPRIT) method and Infinity-Norm ESPRIT based on Correlation Entropy (Co-IN-ESPRIT) method.

[0045] Figure 2 When A signal source is incident on the array, and the DOA is ,exist , Snap , Schematic diagram of the spectrum peak search obtained by running only one MC experiment using the present invention. Figure 2 It can be seen that the present invention can obtain accurate DOA estimation.

[0046] Figure 3 When A signal source is incident on the array, and the DOA is ,exist , Snap , Schematic diagram of the spectrum peak search obtained by running only one MC experiment using the present invention. Figure 3 It can be seen that the present invention can accurately perform DOA estimation for two very close signal sources.

[0047] Figure 4 is , Snap In the case of , the algorithm performance is compared under different GSNRs. 1000 MC experiments are run. The azimuth angles of the three signal sources are .from Figure 4 It can be seen that under the condition of lower generalized signal-to-noise ratio, the present invention has better DOA estimation performance.

[0048] Figure 5 is , In the case of , the algorithm performance is compared under different snapshot numbers. 1000 MC experiments are run. The azimuth angles of the three sources are .from Figure 5 It can be seen that the performance of the present invention improves with the increase of the number of snapshots. Under high pulse conditions, the estimation performance of the present invention is better than other estimation methods for the same snapshot.

[0049] Figure 6 is , In the case of , the algorithm performance is compared under different snapshot numbers. 1000 MC experiments are run. The azimuth angles of the three sources are .from Figure 6 It can be seen that the performance of the present invention improves with the increase of the number of snapshots. Under the same snapshot conditions, the estimation performance of the present invention is better than other estimation methods.

[0050] Figure 7 is , Snap In the case of , the performance of the algorithms under different characteristic index conditions is compared. 1000 MC experiments are run, and the azimuth angles of the three sources are .from Figure 7 It can be seen that the performance of the present invention increases with the characteristic index The same Under these conditions, the estimation performance of the method of the present invention is better.

[0051] In summary, from the analysis of the simulation effect diagram, it can be seen that the DOA estimation method based on INCK in an impulse noise environment proposed in the present invention realizes accurate DOA estimation in an impulse noise environment.

[0052] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. The DOA estimation method based on INCK in impulse noise environment is characterized by: The following steps are involved: Step (1): Receive the signal through the uniform linear array antenna to obtain the received signal information. ; Step (2), based on the received signal information, calculate the local information entropy under a single snapshot using the local entropy calculation formula ; Step (3), according to the received signal information, the infinite norm of a single snapshot is calculated by the infinite norm calculation formula, and a semi-infinite norm Cauchy kernel and a full infinite norm Cauchy kernel are constructed; Step (4), based on the obtained local information entropy , semi-infinite norm Cauchy kernel and full infinite norm Cauchy kernel, construct each received signal information The corresponding weight ; Step (5): According to the obtained weight, the received signal information Weighted processing is performed to obtain , and Constructing the covariance matrix , and then use the ESPRIT / MUSIC algorithm to estimate DOA.

2. The DOA estimation method based on INCK in an impulse noise environment according to claim 1, characterized in that: The array antenna of the uniform linear array structure in step (1) is composed of M The array elements are composed of array elements, and the array element spacing is ,in , is the carrier wavelength.

3. The DOA estimation method based on INCK in an impulse noise environment according to claim 1 or 2, characterized in that: Step (1) receiving signal information for: ； in, is the direction matrix, is the direction vector, j is an imaginary unit, M is the number of array elements, For the k The angles at which the signal sources are located, is an impulse noise term that obeys a symmetric characteristic exponential α stable distribution, and ; is the signal vector, where Indicates k A signal, t For quick shots, .

4. The DOA estimation method based on INCK in an impulse noise environment according to claim 3, characterized in that: The local information entropy in step (2) For the t The local entropy of a snapshot: ； ； in, For the i The first t A quick shot of the signal information; To take the absolute value.

5. The DOA estimation method based on INCK in an impulse noise environment according to claim 4, characterized in that: The step (3) comprises the following steps: 3-1. Calculate the infinity norm for each snapshot: ； Where max{} is the maximum value; 3-2. Construct the Cauchy kernel based on the infinite norm of each snapshot obtained: ； in is the semi-infinite norm Cauchy kernel; is the fully infinite norm Cauchy kernel.

6. The DOA estimation method based on INCK in an impulse noise environment according to claim 5, characterized in that: The weights in step (4) for: ； in is the hyperbolic tangent function.

7. The DOA estimation method based on INCK in an impulse noise environment according to claim 6, characterized in that: The signal information after weighted processing in step (5) for: ； Among them, the size of the signal information Y is the same as the size of the received signal information x; construct the signal information after weighted processing The covariance matrix of : ； in, represents the conjugate transpose.

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