A method, device and medium for deambiguating uniform circular array spatial spectrum direction finding

Abstract

The present application discloses a deambiguation method, device and medium for uniform circular array spatial spectrum direction finding, which relates to the field of radar direction finding technology and is used to solve the problem that when the radius of the uniform circular array is greater than half a wavelength, the spatial spectrum produces ambiguous pseudo peaks, resulting in ambiguous DOA estimation. The method includes: rotating and adjusting the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna; obtaining the array signal received by the asymmetric circular array antenna and calculating the corresponding covariance matrix based on the array signal; performing a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimating the corresponding number of information sources through a fuzzy clustering algorithm; determining the MUSIC spatial spectrum matrix based on the eigenvectors of the covariance matrix; normalizing the MUSIC spatial spectrum matrix, and performing spectrum peak search on the processed MUSIC spatial spectrum matrix based on the number of information sources to determine the direction of the signal wave.

Description

Technical Field

[0001] The present application relates to the field of radar direction finding technology, and in particular to a method, device and medium for deambiguating uniform circular array spatial spectrum direction finding. Background Art

[0002] Radar direction finding technology refers to the process of analyzing the characteristic parameters of the acquired electromagnetic signal and using a specific algorithm to accurately determine the direction of arrival of the signal. The mainstream direction finding systems include the following four: time difference direction finding, amplitude comparison direction finding, spatial spectrum direction finding, and interferometer direction finding. Array signal processing is the processing of the array signal received by the antenna. It can not only achieve precise beam control and increase the main lobe gain, but also has excellent anti-interference and spatial resolution performance. Therefore, it has been widely used in many fields such as radar positioning, geological exploration, and astronomical observation.

[0003] As an important branch of array signal processing, spatial spectrum estimation is applied to the estimation of the direction of arrival of the signal, namely DOA (Direction Of Arrival) estimation. The principle is to display the components of the signal in different directions in space through the spatial spectrum, and then estimate the direction, quantity and other parameters of the electromagnetic wave signal. Compared with the traditional direction finding technology, the spatial spectrum direction finding method significantly improves the accuracy and resolution of angle measurement, and the most important thing is that it can distinguish multiple signals incident from different directions. Early beamforming algorithms could not separate multiple signals in the same beam due to the existence of the "Rayleigh limit" in the spatial domain. In order to solve this problem, in 1986, American researchers Schmidt RO and others first published the MUSIC (Multiple Signal Classification) algorithm. This algorithm broke through the limitations of the array structure and the "Rayleigh limit" and used the eigenvalue decomposition results of the antenna array element receiving data to separate the signal subspace and the noise subspace in the received data. The orthogonality between the two can form a strong spectrum peak, and the direction of the signal is determined by determining the position of the spectrum peak.

[0004] Most spatial spectrum direction finding algorithms are designed based on uniform linear arrays, but such arrays are prone to angular ambiguity within an azimuth angle range of more than 120°, and can only achieve one-dimensional direction finding. To this end, some scholars have studied a variety of new array structures, including uniform circular arrays, to achieve two-dimensional high-precision direction finding of signals within an azimuth range of 360°. Among them, Ma Changlin, Peng Yingning and others proposed a spatial spectrum direction finding algorithm based on a uniform circular array, which converts the circular array into a pattern space, extracts the noise manifold to form a spatial spectrum, and then estimates the signal direction. In summary, the spatial spectrum direction finding method of a uniform circular array is favored in engineering applications because it can simultaneously estimate the azimuth and elevation angles of the incident signal and can perform high-precision direction finding within the entire circumference. However, the contradiction between the angular measurement accuracy and angular ambiguity of the spatial spectrum direction finding of a uniform circular array has not been well resolved. When the radius of the uniform circular array is greater than half a wavelength, a blurred pseudo peak will be generated in the spatial spectrum, resulting in DOA estimation ambiguity and a decrease in direction finding performance. Summary of the invention

[0005] The embodiments of the present application provide a method, device and medium for deambiguating uniform circular array spatial spectrum direction finding, which are used to solve the following technical problem: when the radius of the uniform circular array is greater than half the wavelength, blurred pseudo peaks will be generated in the spatial spectrum, resulting in DOA estimation ambiguity and degradation of direction finding performance.

[0006] In a first aspect, an embodiment of the present application provides a method for deambiguating uniform circular array spatial spectrum direction finding, characterized in that the method includes: rotating and adjusting the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna; acquiring an array signal received by the asymmetric circular array antenna, and calculating a corresponding covariance matrix based on the array signal; performing a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimating the corresponding number of information sources through a fuzzy clustering algorithm; determining a MUSIC spatial spectrum matrix based on the eigenvectors of the covariance matrix; normalizing the MUSIC spatial spectrum matrix, and performing a spectrum peak search on the processed MUSIC spatial spectrum matrix based on the number of information sources to determine the direction of the signal wave.

[0007] In one implementation of the present application, the uniform circular array antenna is an 8-element uniform circular array antenna, and each element is numbered from element 1 to element 8 in a counterclockwise direction, and element 1 is located on the positive half axis of the x-axis; the uniform circular array antenna is rotationally adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, specifically including: rotating a first preset element counterclockwise by a first adjustment angle, and rotating a second preset element counterclockwise by a second adjustment angle; preferably, the first preset element is element 2, the first adjustment angle is 15 degrees, and the second preset element is element 8, and the second adjustment angle is 15 degrees.

[0008] In one implementation of the present application, after the uniform circular array antenna is rotated and adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, the method further includes: determining the coordinate information of each array element based on a preset array element coordinate calculation rule; wherein the array element coordinate calculation rule is represented by the following formula:

[0009]

[0010]

[0011]

[0012]

[0013] in, For the Array elements, For the Array element coordinate, For the Array element coordinate, For the Array element z coordinate, is the distance from the array element to the axis, For the Array elements and The angle of the positive axis, is the number of array elements.

[0014] In one implementation of the present application, the array signal is represented by the following formula:

[0015]

[0016] Expressed in vector form:

[0017]

[0018] in, is the number of far-field narrowband signals incident on the asymmetric circular array antenna, is the array signal, dimension, is the far-field narrowband signal, dimension, is the Gaussian white noise of the array, dimension, is the steering vector matrix, Dimension, that is:

[0019]

[0020] Steering vector It is expressed as:

[0021]

[0022] in, , is the speed of light, is the signal wavelength, is the signal delay;

[0023] The position coordinates of each array element are , the incident azimuth of the spatial narrowband signal is , the elevation angle of the spatial narrowband signal is Signal delay It is expressed by the following formula:

[0024]

[0025] It can be seen that the array steering vector matrix is ​​expressed by the following formula:

[0026]

[0027] The corresponding covariance matrix is ​​calculated based on the array signal, specifically including:

[0028] The covariance matrix of the array signal is calculated using the maximum likelihood estimation method, which is expressed by the following formula:

[0029]

[0030] in, Array signal, is the conjugate transposed matrix of the array signal.

[0031] In one implementation of the present application, the covariance matrix is ​​subjected to eigenvalue decomposition, which is represented by the following formula:

[0032]

[0033] in, is the covariance matrix of the received far-field narrowband signal, is the conjugate transpose of the array-directed vector matrix, is the Gaussian white noise variance, is the identity matrix, and The covariance matrices are The eigenvalues ​​and corresponding eigenvectors of and Satisfies the following inequality:

[0034] .

[0035] In one implementation of the present application, the corresponding number of information sources is estimated through a fuzzy clustering algorithm, specifically including: processing a preset number of characteristic values ​​through a preset sample generation rule to obtain a preset number of group cluster samples; performing fuzzy clustering operations on the preset number of group cluster samples respectively to obtain a preset number of information sources to be estimated; calculating the average value of the preset number of information sources to be estimated, and determining the average value as the number of information sources.

[0036] In one implementation of the present application, a MUSIC spatial spectrum matrix is ​​determined according to an eigenvector of a covariance matrix, specifically comprising: constructing a noise subspace according to the eigenvector of the covariance matrix, using the principle that a steering vector in a signal subspace is orthogonal to a noise subspace vector, and constructing a MUSIC algorithm spatial spectrum estimation model based on the noise subspace; performing a two-dimensional traversal based on a preset azimuth angle range and elevation angle range using a MUSIC algorithm spatial spectrum estimation formula to determine the MUSIC spatial spectrum matrix; wherein the MUSIC algorithm spatial spectrum estimation model is represented by the following formula:

[0037]

[0038]

[0039] in, is the noise subspace.

[0040] In one implementation of the present application, a MUSIC spatial spectrum matrix is ​​normalized, and according to the number of information sources, a spectrum peak search is performed on the processed MUSIC spatial spectrum matrix to determine the direction of the signal wave, specifically including: normalizing the spatial spectrum matrix to obtain a normalized matrix, and taking the normalized matrix to the power of the number of array elements to obtain a power matrix; and performing a spectrum peak search on the power matrix to determine the direction of the signal wave.

[0041] In a second aspect, an embodiment of the present application further provides a deambiguation device for uniform circular array spatial spectrum direction finding, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: rotate and adjust the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna; obtain an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal; perform eigenvalue decomposition on the covariance matrix a preset number of times to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimate the corresponding number of information sources through a fuzzy clustering algorithm; determine a MUSIC spatial spectrum matrix based on the eigenvectors of the covariance matrix; normalize the MUSIC spatial spectrum matrix, and perform a spectrum peak search on the processed MUSIC spatial spectrum matrix based on the number of information sources to determine the direction of the signal wave.

[0042] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for deambiguating uniform circular array spatial spectrum direction finding, storing computer executable instructions, characterized in that the computer executable instructions are configured to: rotate and adjust the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna; obtain an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal; perform eigenvalue decomposition of the covariance matrix a preset number of times to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimate the corresponding number of information sources through a fuzzy clustering algorithm; determine a MUSIC spatial spectrum matrix based on the eigenvectors of the covariance matrix; normalize the MUSIC spatial spectrum matrix, and perform a spectrum peak search on the processed MUSIC spatial spectrum matrix based on the number of information sources to determine the direction of the signal wave.

[0043] The embodiments of the present application provide a method, device and medium for deambiguating uniform circular array spatial spectrum direction finding. By adjusting the array structure of the uniform circular array, an asymmetric circular array antenna is constructed, so that the steering vectors of the false angle and the real angle differ greatly, thereby suppressing the fuzzy pseudo peaks. By performing a normalized exponentiation operation on the spatial spectrum matrix of the MUSIC algorithm, the fuzzy pseudo peaks are further suppressed, thereby solving the contradiction between the angle measurement accuracy and angle ambiguity of the existing uniform circular array spatial spectrum direction finding. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1A flow chart of a method for deambiguating uniform circular array spatial spectrum direction finding provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of an 8-element circular array structure provided in an embodiment of the present application;

[0047] Figure 3 A spatial spectrum diagram of uniform circular array direction finding based on MUSIC algorithm in a prior art solution provided in an embodiment of the present application;

[0048] Figure 4 A direction finding spatial spectrum provided in an embodiment of the present application;

[0049] Figure 5 A multi-signal source direction finding spatial spectrum provided in an embodiment of the present application;

[0050] Figure 6 A curve showing a change in root mean square error and azimuth angle of a direction finding provided in an embodiment of the present application;

[0051] Figure 7 A curve of a change in a root mean square error of a direction finding and a pitch angle provided in an embodiment of the present application;

[0052] Figure 8 A schematic diagram of the internal structure of a uniform circular array spatial spectrum direction finding deambiguation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0054] The embodiments of the present application provide a method, device and medium for deambiguating uniform circular array spatial spectrum direction finding, which are used to solve the following technical problem: when the radius of the uniform circular array is greater than half the wavelength, blurred pseudo peaks will be generated in the spatial spectrum, resulting in DOA estimation ambiguity and degradation of direction finding performance.

[0055] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0056] Figure 1 This is a flow chart of a method for deambiguating uniform circular array spatial spectrum direction finding provided in an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for deambiguating uniform circular array spatial spectrum direction finding, which specifically includes the following steps:

[0057] Step 101: Rotate and adjust the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna.

[0058] It should be noted that if Figure 2 As shown, the uniform circular array antenna in the embodiment of the present application is a uniform circular array with 8 elements, and the elements are numbered from element 1 to element 8 in a counterclockwise direction, and element 1 is located at the positive half axis of the x-axis.

[0059] In one embodiment of the present application, a uniform circular array antenna is rotationally adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, specifically including: rotating a first preset array element counterclockwise by a first adjustment angle, and rotating a second preset array element counterclockwise by a second adjustment angle.

[0060] Preferably, the first preset array element is array element 2, the first adjustment angle is 15 degrees, the second preset array element is array element 8, and the second adjustment angle is 15 degrees.

[0061] In one embodiment of the present application, after the uniform circular array antenna is rotated and adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, the method further includes: determining the coordinate information of each array element based on a preset array element coordinate calculation rule; wherein the array element coordinate calculation rule is represented by the following formula:

[0062]

[0063]

[0064]

[0065]

[0066] in, For the Array elements, For the Array element coordinate, For the Array element coordinate, For the Array element z coordinate, is the distance from the array element to the axis, For the Array elements and The angle of the positive axis, is the number of array elements.

[0067] Step 102: Acquire an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal.

[0068] In one embodiment of the present application, after determining the coordinate information of each array element based on a preset array element coordinate calculation rule, it is necessary to obtain an array signal received by the asymmetric circular array antenna and calculate a corresponding covariance matrix based on the array signal.

[0069] In one embodiment, assume that N far-field narrowband signals are incident on an asymmetric circular array antenna. The complex envelope of the narrowband signal is as follows:

[0070]

[0071] in, , , are the phase, angular frequency and amplitude of the received signal respectively.

[0072] In Moment, The signal received by each array element is as follows:

[0073]

[0074] in, To arrive The first The delay of a signal relative to the reference element, For the The signal is The gain on the array element, For the Gaussian white noise of the array elements.

[0075] If non-ideal factors such as channel mutual coupling and inconsistency are not considered and the gain of each array element is normalized, the array The signals received at each moment are expressed as follows:

[0076] It is expressed by the following formula:

[0077]

[0078] Expressed in vector form:

[0079]

[0080] in, is the number of far-field narrowband signals incident on the asymmetric circular array antenna, is the array signal, dimension, is the far-field narrowband signal, dimension, is the Gaussian white noise of the array, dimension, is the steering vector matrix, Dimension, that is:

[0081]

[0082] Steering vector It is expressed as:

[0083]

[0084] in, , is the speed of light, is the signal wavelength, is the signal delay;

[0085] The position coordinates of each array element are , the incident azimuth of the spatial narrowband signal is , the elevation angle of the spatial narrowband signal is Signal delay It is expressed by the following formula:

[0086]

[0087] It can be seen that the array steering vector matrix is ​​expressed by the following formula:

[0088]

[0089] It should be noted that, in theory, the corresponding covariance matrix based on the array signal calculation is calculated using the following formula:

[0090]

[0091] In practice, the maximum likelihood estimation method is generally used to calculate the covariance matrix of array data.

[0092] In one embodiment of the present application, calculating the corresponding covariance matrix based on the array signal specifically includes:

[0093] The covariance matrix of the array signal is calculated using the maximum likelihood estimation method, which is expressed by the following formula:

[0094]

[0095] in, Array signal, is the conjugate transposed matrix of the array signal.

[0096] Step 103: Perform a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimate the corresponding number of information sources by using a fuzzy clustering algorithm.

[0097] In one embodiment of the present application, the covariance matrix is ​​subjected to eigenvalue decomposition, which is represented by the following formula:

[0098]

[0099] in, is the covariance matrix of the received far-field narrowband signal, is the conjugate transpose of the array-directed vector matrix, is the Gaussian white noise variance, is the identity matrix, and The covariance matrices are The eigenvalues ​​and corresponding eigenvectors of and Satisfies the following inequality:

[0100] .

[0101] In one embodiment of the present application, the corresponding number of information sources is estimated by a fuzzy clustering algorithm, which specifically includes: processing a preset number of characteristic values ​​through a preset sample generation rule to obtain a preset number of group cluster samples; performing fuzzy clustering operations on the preset number of group cluster samples respectively to obtain a preset number of information sources to be estimated; calculating the average value of the preset number of information sources to be estimated, and determining the average value as the number of information sources.

[0102] In one embodiment, the eigenvalues ​​of the variance matrix are , all eigenvalues ​​are divided by the minimum eigenvalue to obtain , all eigenvalues ​​minus the minimum eigenvalue to get , taking the above three types of data as a sample :

[0103]

[0104] but The sample population is:

[0105]

[0106] Number of samples taken is 100, that is, the array covariance data is characteristically decomposed 100 times, and these 100 groups of sample data form a set . The sample collection Perform clustering operations to find the number of classes corresponding to the signal feature values ​​in each group of samples, and average these 100 numbers to get the estimated number of signal sources. The specific process of fuzzy clustering is as follows:

[0107] (1) Construct a sample set using the results of eigenvalue decomposition , set the initial value of the parameter, the initial number of clusters is , the fuzzy factor is , the maximum number of iterations is , and the minimum value is , is the cluster center of the signal class, the initial value You can set any random number. is the cluster center of the noise class, the initial value Any random number can be set;

[0108] (2) Calculate the membership function of the sample :

[0109]

[0110] in, is the Euclidean norm, ;

[0111] (3) Update the two types of cluster centers , , , then the optimization function is updated as follows:

[0112]

[0113] (4) Subtract the two optimization functions to determine whether the stopping condition is met , when satisfied, the loop ends and the membership is output , , otherwise let , re-execute step (2);

[0114] (5) Determine the corresponding sample by membership Whether it belongs to the signal class set, when When Belongs to the signal class set. Ultimately, the number of samples contained in the set is the number of signal sources.

[0115] Step 104: Determine the MUSIC space spectrum matrix according to the eigenvectors of the covariance matrix.

[0116] In one embodiment of the present application, a MUSIC spatial spectrum matrix is ​​determined according to an eigenvector of a covariance matrix, specifically comprising: constructing a noise subspace according to the eigenvector of the covariance matrix by utilizing the principle that a steering vector in a signal subspace is orthogonal to a noise subspace vector, and constructing a MUSIC algorithm spatial spectrum estimation model based on the noise subspace; performing a two-dimensional traversal based on a preset azimuth angle range and elevation angle range using a MUSIC algorithm spatial spectrum estimation formula to determine the MUSIC spatial spectrum matrix; wherein the MUSIC algorithm spatial spectrum estimation model is represented by the following formula:

[0117]

[0118]

[0119] in, is the noise subspace.

[0120] In one embodiment, after the covariance matrix is ​​eigendecomposed, the eigenvalues ​​and eigenvectors of the signal part and the noise part are obtained, where the signal eigenvalue is , the noise characteristic value is (MN) According to the eigenvalue corresponding to the eigenvector, the eigenvector corresponding to the noise part constitutes the noise subspace Under ideal conditions, the steering vector of the signal subspace is orthogonal to the vector of the noise subspace:

[0121]

[0122] In the above formula, the noise is complex Gaussian white noise. , Linear independence.

[0123] The spatial spectrum estimation formula of the MUSIC algorithm can be derived as follows:

[0124]

[0125] In actual engineering, the steering vector of the signal subspace is not completely orthogonal to the noise subspace, and there is a certain error. Then the result of the above formula will approach zero, that is, a spectrum peak is generated in the direction of the signal wave, while the spatial spectrum value is small in other directions without signals. Perform two-dimensional traversal of the azimuth angle 0~90° and the pitch angle 0~360°, with an angle interval of 0.2 degrees. Each pair of angle combinations corresponds to a spatial spectrum value, forming a spatial spectrum matrix .

[0126] Step 105: normalize the MUSIC spatial spectrum matrix, and perform spectrum peak search on the processed MUSIC spatial spectrum matrix according to the number of signal sources to determine the signal incoming direction.

[0127] In one embodiment of the present application, a MUSIC spatial spectrum matrix is ​​normalized, and according to the number of information sources, a spectrum peak search is performed on the processed MUSIC spatial spectrum matrix to determine the direction of the signal wave, specifically including: normalizing the spatial spectrum matrix to obtain a normalized matrix, taking the normalized matrix to the power of the number of array elements to obtain a power matrix; and performing a spectrum peak search on the power matrix to determine the direction of the signal wave.

[0128] In one embodiment, the processing of the MUSIC spatial spectrum is as follows:

[0129] (1) Spatial spectrum matrix Perform normalization to get the normalized matrix , and then the matrix Taking the power of the number of array elements gives the matrix The purpose is to keep the peak value at 1, and the other spectrum values ​​less than 1 will become smaller and smaller, which is equivalent to accumulating the peak value and suppressing the pseudo peak;

[0130] (2) For the matrix Search for peaks when and When The peak value of the azimuth and elevation angles at this time is the The incident angle of the signal;

[0131] (3) Find the After the peak value is reached, the current peak value is set to 0 to form a new matrix , and then perform the same operation on the matrix, a total of times, get The incident angle of the signal.

[0132] Simulation 1: Simulation verifies the direction finding and deambiguation performance of the technical solution of the present invention.

[0133] An 8-element uniform circular array is used with a circular array radius of 0.15m, a signal-to-noise ratio of 10dB, a single signal incident azimuth and elevation angle of (70°, 20°), a fast array number of 64, and a signal carrier frequency of 12GHz, that is, a wavelength of 0.025m. Obviously, when the circular array radius is greater than half the wavelength, the uniform circular array will have first-order direction finding ambiguity.

[0134] a. Using the uniform circular array spatial spectrum direction finding method based on the MUSIC algorithm in the existing technical solution, the direction finding results are as follows Figure 3 shown.

[0135] b. Using the spatial spectrum direction finding method in the technical solution of the present invention, adjusting the 8-element uniform circular array structure, the direction finding results are as follows Figure 4 shown.

[0136] contrast Figure 5 , Figure 6 It can be seen that the maximum spectrum peak angles of the two technical solutions are both (70°, 20°), that is, the measured signal incident azimuth and elevation angles are accurate; however, the maximum spectrum peak and the maximum pseudo peak in the prior art solution differ by only about 4 dB, and when searching for the spectrum peak, it is easy to take the maximum pseudo peak angle as the real angle. In the technical solution of the present invention, the maximum spectrum peak and the maximum pseudo peak differ by about 103 dB, and the difference between the spectrum values ​​at other positions and the maximum spectrum peak is significantly increased. Therefore, by using the adjusted non-uniform circular array, and after normalization and exponentiation operations, the pseudo peak can be suppressed and the direction finding ambiguity can be effectively resolved.

[0137] Simulation 2: Simulation verifies the multi-signal source direction finding performance of the technical solution of the present invention.

[0138] An adjusted 8-element non-uniform circular array is used with a circular array radius of 0.15m, a signal-to-noise ratio of 10dB, 3 incoherent signal sources, incident azimuth and elevation angles of (40°, 25°), (60°, 30°), and (70°, 45°), a fast row number of 64, and a signal carrier frequency of 12GHz.

[0139] Using the spatial spectrum direction finding method in the technical solution of the present invention, the direction finding result is as follows Figure 5 As shown. Figure 5 It can be seen that the spatial spectrum has three peaks, and the corresponding angles are consistent with the signal incident angle. Therefore, the technical solution of the present invention can distinguish multiple signals and has the ability of multi-signal direction finding.

[0140] Simulation 3: Simulation verifies the pitch and azimuth omnidirectional direction finding performance of the technical solution of the present invention.

[0141] An adjusted 8-element non-uniform circular array is used with a circular array radius of 0.15m, a signal-to-noise ratio of 10dB, an elevation angle range of 2-88° when the signal incident azimuth angle is 10°, an azimuth angle range of 2-358° when the signal incident elevation angle is 10°, a fast row number of 64, and a signal carrier frequency of 12GHz.

[0142] Using the spatial spectrum direction finding method in the technical solution of the present invention, the curve of the change of the root mean square error of the angle measurement with the azimuth angle is as follows: Figure 6 As shown in the figure, the curve of the RMS angle error changing with the pitch angle is as follows: Figure 7 As shown. Figure 6 , 7 It can be seen that the RMS values ​​of the angle measurement errors of the technical solution of the present invention are mostly below 0.5° at azimuth angles of 2-358° and elevation angles of 2-88°, and have good omnidirectional direction finding performance. The above are the method embodiments proposed in this application.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a uniform circular array spatial spectrum direction finding deambiguation device, whose structure is as follows: Figure 8 shown.

[0144] Figure 8 This is a schematic diagram of the internal structure of a uniform circular array spatial spectrum direction finding deambiguation device provided in an embodiment of the present application. Figure 8 As shown, the device includes:

[0145] at least one processor 801;

[0146] and, a memory 802 communicatively connected to the at least one processor;

[0147] The memory 802 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 801 to enable at least one processor 801 to:

[0148] Rotating and adjusting the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna;

[0149] Acquire an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal;

[0150] Performing a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimating the corresponding number of information sources through a fuzzy clustering algorithm;

[0151] According to the eigenvector of the covariance matrix, the MUSIC space spectrum matrix is ​​determined;

[0152] The MUSIC spatial spectrum matrix is ​​normalized, and according to the number of signal sources, the processed MUSIC spatial spectrum matrix is ​​searched for spectrum peaks to determine the direction of the signal wave.

[0153] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for deambiguating uniform circular array spatial spectrum direction finding, storing computer executable instructions, wherein the computer executable instructions are set as:

[0154] Rotating and adjusting the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna;

[0155] Acquire an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal;

[0156] Performing a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimating the corresponding number of information sources through a fuzzy clustering algorithm;

[0157] According to the eigenvector of the covariance matrix, the MUSIC space spectrum matrix is ​​determined;

[0158] The MUSIC spatial spectrum matrix is ​​normalized, and according to the number of signal sources, the processed MUSIC spatial spectrum matrix is ​​searched for spectrum peaks to determine the direction of the signal wave.

[0159] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0160] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0165] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0166] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0167] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0168] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0169] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for deambiguating uniform circular array spatial spectrum direction finding, characterized in that: The method comprises: Rotating and adjusting the uniform circular array antenna based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna; Acquire an array signal received by the asymmetric circular array antenna, and calculate a corresponding covariance matrix based on the array signal; Performing a preset number of eigenvalue decompositions on the covariance matrix to obtain a preset number of eigenvalues ​​and corresponding eigenvectors, and estimating the corresponding number of information sources by a fuzzy clustering algorithm; Determining a MUSIC spatial spectrum matrix according to the eigenvectors of the covariance matrix; Normalizing the MUSIC spatial spectrum matrix, and performing spectrum peak search on the processed MUSIC spatial spectrum matrix according to the number of signal sources to determine the signal incoming direction; The uniform circular array antenna is an 8-element uniform circular array antenna, wherein the elements are numbered from element 1 to element 8 in a counterclockwise direction, and element 1 is located at the positive half axis of the x-axis; The uniform circular array antenna is rotated and adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, specifically including: Rotate the first preset array element counterclockwise by a first adjustment angle, and rotate the second preset array element counterclockwise by a second adjustment angle; Among them, the first preset array element is array element 2, the first adjustment angle is 15 degrees, the second preset array element is array element 8, and the second adjustment angle is 15 degrees; The number of corresponding information sources is estimated through fuzzy clustering algorithm, including: The preset number of characteristic values ​​are processed by a preset sample generation rule to obtain a preset number of group cluster samples; Performing fuzzy clustering operations on the preset number of group cluster samples respectively to obtain a preset number of information sources to be estimated; Calculating an average value of the preset number of information sources to be estimated, and determining the average value as the number of information sources; The MUSIC spatial spectrum matrix is ​​normalized, and according to the number of signal sources, a spectrum peak search is performed on the processed MUSIC spatial spectrum matrix to determine the signal incoming direction, specifically including: Normalizing the spatial spectrum matrix to obtain a normalized matrix, and raising the normalized matrix to the power of the number of array elements to obtain a power matrix; A spectrum peak search is performed on the exponentiation matrix to determine the direction of the signal wave.

2. The method for deambiguating uniform circular array spatial spectrum direction finding according to claim 1, characterized in that: After the uniform circular array antenna is rotationally adjusted based on a preset asymmetric adjustment rule to construct an asymmetric circular array antenna, the method further includes: Based on the preset array element coordinate calculation rules, determine the coordinate information of each array element; The calculation rule of array element coordinates is expressed by the following formula: in, For the Array element, For the Array element coordinate, For the Array element coordinate, For the Array element z coordinate, is the distance from the array element to the axis, For the Array elements and The angle of the positive axis, is the number of array elements.

3. The method for deambiguating uniform circular array spatial spectrum direction finding according to claim 2, characterized in that: The array signal is expressed by the following formula: Expressed in vector form: in, is the number of far-field narrowband signals incident on the asymmetric circular array antenna, is the array signal, dimension, is the far-field narrowband signal, dimension, is the Gaussian white noise of the array, dimension, is the steering vector matrix, Dimension, that is: Steering vector It is expressed as: in, , is the speed of light, is the signal wavelength, is the signal delay; The position coordinates of each array element are , the incident azimuth of the spatial narrowband signal is , the elevation angle of the spatial narrowband signal is When the signal delay It is expressed by the following formula: It can be seen that the array steering vector matrix is ​​expressed by the following formula: Calculating the corresponding covariance matrix based on the array signal specifically includes: The covariance matrix of the array signal is calculated using the maximum likelihood estimation method, which is expressed by the following formula: in, Array signal, is the conjugate transposed matrix of the array signal.

4. The method for deambiguating uniform circular array spatial spectrum direction finding according to claim 3, characterized in that: The covariance matrix is ​​subjected to eigenvalue decomposition, which is expressed by the following formula: in, is the covariance matrix of the received far-field narrowband signal, is the conjugate transpose of the array-directed vector matrix, is the Gaussian white noise variance, is the identity matrix, and The covariance matrices are The eigenvalues ​​and corresponding eigenvectors of and Satisfies the following inequality: 。 5. The method for deambiguating uniform circular array spatial spectrum direction finding according to claim 4, characterized in that: Determining the MUSIC spatial spectrum matrix according to the eigenvector of the covariance matrix specifically includes: According to the eigenvector of the covariance matrix, the noise subspace is constructed by utilizing the principle that the steering vector in the signal subspace is orthogonal to the noise subspace vector, and based on the noise subspace, a MUSIC algorithm spatial spectrum estimation model is constructed; Based on the preset azimuth angle range and elevation angle range, a two-dimensional traversal is performed using the MUSIC algorithm spatial spectrum estimation formula to determine the MUSIC spatial spectrum matrix; Among them, the spatial spectrum estimation model of the MUSIC algorithm is expressed by the following formula: in, is the noise subspace.

6. A deambiguation device for uniform circular array spatial spectrum direction finding, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the deambiguation method for uniform circular array spatial spectrum direction finding as described in any one of claims 1-5.

7. A non-volatile computer storage medium for deambiguating uniform circular array spatial spectrum direction finding, storing computer executable instructions, characterized in that: When the computer executable instructions are executed, the ambiguity resolution method for uniform circular array spatial spectrum direction finding according to any one of claims 1 to 5 is implemented.

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