Decoherence DOA estimation method and system based on compression mobile platform orthogonal dipole array

Abstract

The invention provides a decoherence DOA estimation method and system based on an orthogonal dipole array of a compressed mobile platform, relates to the field of array signal processing, and solves the problem that a signal subspace and a noise subspace cannot be accurately separated by an existing algorithm due to the fact that a large number of coherent signals exist in incident signals, so that the signal subspace and the noise subspace cannot be accurately separated. And thus, a signal subspace equivalent to a received signal cannot be obtained. The method comprises the following steps: sampling and compressing array receiving data by adopting a signal receiving model; performing polarization smoothing processing on the compressed data to obtain an output covariance matrix, and performing averaging processing to obtain an array average output covariance matrix; carrying out characteristic decomposition on the array average output covariance matrix by adopting a MUSIC algorithm, and solving a corresponding noise subspace; and traversing each angle, calculating a spectral peak graph, and taking an angle corresponding to a peak value as an estimated value. The method is suitable for the technical field of decoherence DOA estimation of multiple coherent signals.

Description

Technical Field

[0001] The present invention belongs to the field of array signal processing, and particularly relates to a method for de - coherent DOA estimation based on a compressed mobile platform orthogonal dipole array. Background Art

[0002] Spatial spectrum estimation technology is one of the main research contents in the field of signal direction - of - arrival (DOA) estimation. It has significant advantages in many aspects such as estimation accuracy and adaptability to antenna array patterns, and has received extensive attention from research scholars. Among them, subspace - based algorithms such as the Multiple Signal Classification (MUSIC) algorithm can detect multiple signals in the same beam. When the incident signals are non - coherent, the traditional MUSIC algorithm can estimate the DOA of the signals by using the orthogonality between the noise subspace and the signal subspace. However, in actual situations, there are often a large number of coherent signals in the direction - finding environment, and the signal subspace corresponding to the array output covariance matrix is rank - deficient. After performing eigenvalue decomposition, the signal subspace and the noise subspace cannot be accurately separated, so that the signal subspace equivalent to the received signal cannot be obtained, resulting in a significant reduction in the performance of such algorithms.

[0003] The spatial smoothing algorithm divides the array into multiple sub - arrays with the same spatial structure, and restores the rank of the matrix by calculating the mean of the output covariance matrices of multiple sub - arrays to achieve signal de - coherence. However, this method has strict requirements on the spatial structure of the array, requiring that each sub - array be exactly the same, otherwise the de - coherence ability deteriorates. This method is equivalent to reducing the number of array elements, losing part of the spatial aperture of the array, and moreover, calculating the covariance matrices of multiple sub - arrays greatly increases the computational complexity.

[0004] A fixed array can be combined with a mobile carrier platform to form a mobile array, thereby further improving its performance through the moving characteristics of the array. Array movement de - coherence is similar to the principle of spatial smoothing. It moves the entire array, and the data received by the moved array is equivalent to the data received by the sub - array, so as to achieve the purpose of obtaining multiple output covariance matrices. And this method can be combined with a high - speed moving platform, such as a seeker. This method has no strict requirements on the spatial structure of the array and does not lose the spatial aperture, but its real - time performance is reduced.

[0005] Compared with scalar arrays, polarization - sensitive arrays can obtain up to six - dimensional information including electric and magnetic fields. The polarization smoothing algorithm takes electric (magnetic) dipoles with the same polarization mode as a sub - array, so any array structure can be applied. Since a full electromagnetic vector sensor has at most six channels, it means that at most six coherent signal sources can be de - coherent. However, a full electromagnetic vector sensor has a serious mutual - coupling effect, which further reduces its de - coherence ability. An incomplete electromagnetic vector sensor is composed of partial electric (magnetic) dipoles, which has a weak mutual - coupling effect, low cost, and is more widely used, but its ability to de - coherent signal sources also decreases. Summary of the Invention

[0006] The present invention aims to solve the problem that due to the existence of a large number of coherent signals in the incident signal, the existing algorithms cannot accurately separate the signal subspace and the noise subspace, resulting in the inability to obtain the signal subspace equivalent to the received signal.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The present invention proposes a decoherence DOA estimation method based on a compressed mobile platform orthogonal dipole array. The DOA estimation method includes the following steps:

[0009] Step S1: Construct a signal reception model according to the orthogonal dipole.

[0010] Step S2: Sample the array reception data using the signal reception model and perform compression processing on the sampled data.

[0011] Step S3: Perform polarization smoothing processing on the compressed data to obtain an output covariance matrix, and perform mean processing to obtain an array average output covariance matrix.

[0012] Step S4: Use the MUSIC algorithm to perform eigen-decomposition on the array average output covariance matrix to obtain the corresponding noise subspace.

[0013] Step S5: Traverse each angle to generate a corresponding spatial phase matrix in the signal reception model, calculate the spectral peak diagram according to the spatial phase matrix, and the angle corresponding to the peak value is the estimated value.

[0014] Further, polarization smoothing and a compressed polarization-sensitive array are introduced into the mobile platform, and an orthogonal dipole array is used to replace the scalar array in the mobile platform to obtain the above-mentioned compressed mobile platform orthogonal dipole array.

[0015] Further, the above-mentioned step S1 is specifically:

[0016] An orthogonal double dipole arranged at equal intervals along the X-axis and Y-axis directions forms a polarization-angle domain rotation-invariant L-shaped regular polarization-sensitive array as the signal reception model.

[0017] Further, the above-mentioned signal reception model is expressed as:

[0018]

[0019] where X(t) is the output signal, S(t) and n(t) are the target signal and the noise signal received at the receiving end respectively, A is the signal steering vector matrix, s1(t), s2(t),..., s M(t) is the M far - field narrow - band signals received by the polarization - sensitive array, which are coherent circular Gaussian fully polarized signals, and ρ1, ρ2, …, ρ M are the attenuation coefficients of the M far - field narrow - band signals.

[0020] Furthermore, the above - mentioned step S2 is specifically as follows:

[0021] When the array moves at equal intervals, the signal - receiving array model is used to sample the array - received data. The initial position of the array is recorded as the 1st array, and after moving once, it is recorded as the 2nd array, and so on. And the data received by each array is compressed through a compression network.

[0022] Furthermore, the above - mentioned compression network includes a phase shifter and an accumulator.

[0023] Furthermore, the above - mentioned polarization smoothing process is specifically: jointly perform smoothing processing in the polarization domain and the spatial domain.

[0024] The method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array described in the present invention can be entirely implemented by computer software. Therefore, correspondingly, the present invention also provides a system for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array. The system includes:

[0025] A storage device for constructing a signal - receiving model according to the orthogonal dipole;

[0026] A storage device for sampling the array - received data using the signal - receiving model and compressing the sampled data;

[0027] A storage device for performing polarization smoothing processing on the compressed data to obtain an output covariance matrix and performing mean processing to obtain an array - averaged output covariance matrix;

[0028] A storage device for performing eigenvalue decomposition on the array - averaged output covariance matrix using the MUSIC algorithm to obtain the corresponding noise subspace;

[0029] A storage device for traversing each angle to generate the corresponding spatial phase matrix in the signal - receiving model, calculating the spectral peak diagram according to the spatial phase matrix, and the angle corresponding to the peak value is the estimated value.

[0030] The present invention also provides a computer - readable storage medium. A computer program is stored on the computer - readable storage medium. When the computer program is run by a processor, it executes the method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array described in any one of the above.

[0031] The present invention also provides a computer device, which includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method for coherent signal DOA estimation based on a compressed mobile platform orthogonal dipole array described in any one of the above items.

[0032] The beneficial effects of the present invention are as follows:

[0033] 1. By introducing polarization smoothing and a compressed polarization-sensitive array into a mobile platform, and using an orthogonal dipole array to replace the scalar array in a traditional mobile platform, the present invention proposes a method for coherent signal DOA estimation based on a compressed mobile platform orthogonal dipole array.

[0034] 2. The present invention proposes a method for coherent signal DOA estimation based on a compressed mobile platform orthogonal dipole array. First, the array composed of multiple pairs of orthogonal dipoles is smoothed in the polarization domain. Then, the data received by the moving array is processed in the time domain to restore the signal covariance matrix to full rank. At the same time, by performing joint decoherence in the polarization domain and the spatial domain, the number of observations required for decoherence of the moving platform is reduced to half of the original, greatly improving the processing efficiency of coherent signal DOA estimation.

[0035] Furthermore, for the DOA estimation method proposed by the present invention, the received data is passed through a compression network to reduce the matrix dimension, so as to reduce the computational complexity. Then, joint smoothing is performed in the polarization domain and the spatial domain to restore the rank of the array reception covariance matrix, realizing accurate estimation of the DOAs of multiple coherent signals.

[0036] Furthermore, compared with traditional decoherence methods, the present invention aims to provide a method for joint polarization smoothing and mobile platform decoherence under a compressed polarization-sensitive array. This method gets rid of the problem that the traditional mobile platform decoherence requires the number of observations to be greater than or equal to the number of coherent signal sources. By performing joint decoherence in the polarization domain and the spatial domain, the number of observations required for decoherence of the moving platform is reduced to half of the original, reducing the computational complexity. And the received data is also processed by a compression network, further reducing the amount of calculation. Without sacrificing the estimation accuracy, the DOA estimation efficiency is greatly increased.

[0037] The present invention is applicable to the technical field of coherent signal DOA estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 is a schematic diagram of the signal reception array model proposed by the present invention;

[0040] Figure 2 is a schematic diagram of the mobile array signal reception model described by the present invention;

[0041] Figure 3 is a schematic diagram of the compression network structure described by the present invention;

[0042] Figure 4 is a schematic diagram of the polarization domain smoothing subarray division model described by the present invention;

[0043] Figure 5 is the coherent signal DOA estimation spectral peak diagram described by the present invention;

[0044] Figure 6 is the comparison diagram of the root mean square error of the DOA estimation of 4 coherent signals described by the present invention. Specific Embodiments

[0045] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0046] The following further details the specific embodiments of the present invention in conjunction with the drawings. The following embodiments will help those skilled in the art further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several changes and improvements can still be made, and these all fall within the protection scope of the present invention.

[0047] Embodiment 1. In this embodiment, by analyzing the principle of MUSIC for estimating the direction of arrival of signals, a method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array is established. This method reduces the matrix dimension of the data received by the array through a compression network, and then jointly smooths it in the polarization domain and the spatial domain to restore the rank of the array receiving covariance matrix, achieving a reduction in computational complexity while accurately estimating the DOA of multiple coherent signals.

[0048] In this embodiment, by introducing polarization smoothing and a compressed polarization-sensitive array into the mobile platform and using an orthogonal dipole array to replace the scalar array in the traditional mobile platform, a method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array is proposed. The DOA estimation method includes the following steps:

[0049] Step S1: Construct a signal reception model according to the orthogonal dipole.

[0050] Step S2: Sample the array received data using the signal reception model, and perform compression processing on the sampled data.

[0051] Step S3: Perform polarization smoothing processing on the compressed data to obtain an output covariance matrix, and perform mean processing to obtain an array average output covariance matrix.

[0052] Step S4: Use the MUSIC algorithm to perform eigenvalue decomposition on the array average output covariance matrix to obtain the corresponding noise subspace.

[0053] Step S5: Traverse each angle to generate the corresponding spatial phase matrix in the signal reception model, calculate the spectral peak diagram according to the spatial phase matrix, and the angle corresponding to the peak value is the estimated value.

[0054] This embodiment proposes a method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array. Through the synergistic effect of the mobile platform and the orthogonal dipole array, the method reduces the requirement for the number of observations, thereby improving the efficiency and accuracy of coherent signal DOA estimation. At the same time, the method uses the data received by an array composed of multiple pairs of orthogonal dipoles to perform smoothing processing in the polarization domain to restore the rank of the signal source covariance matrix. Since the polarization array has strong polarization sensitivity, smoothing in the polarization domain can effectively reduce the correlation between signals and help restore the rank of the covariance matrix; the array is moved by the mobile platform, and for different arrays after movement, the covariance matrix of the received signal after polarization smoothing is calculated. By using a principle similar to spatial smoothing, the average array output covariance matrix of these arrays is calculated. During this process, the covariance matrix of the signal is restored to full rank; the Multiple Signal Classification Algorithm (MUSIC) is used to estimate the DOA of the restored covariance matrix, thereby obtaining the direction information of multiple signal sources. By combining the decoherence techniques in the polarization domain and the smoothed time domain, the algorithm reduces the number of observations required for decoherence of the moving platform to half of the original, thereby significantly improving the efficiency of coherent signal DOA estimation.

[0055] Embodiment 2: This embodiment specifically describes a method for coherent DOA estimation based on a compressed mobile platform orthogonal dipole array described in the above Embodiment 1;

[0056] Step S1: Construct a signal reception model according to the orthogonal dipole;

[0057] Specifically:

[0058] An orthogonal dipole arranged at equal intervals along the X-axis and Y-axis directions forms a polarization-angle domain rotation-invariant L-shaped regular polarization-sensitive array as the signal reception model; the signal received by the receiving end is denoted as the target signal S(t) and the noise signal n(t), and the signal at the output end is X(t).

[0059] The signal reception model is expressed as:

[0060]

[0061] Where X(t) is the signal at the output end, S(t) and n(t) are the target signal and noise signal received by the receiving end respectively, A is the signal steering vector matrix, s1(t), s2(t),..., s M (t) are M far-field narrowband signals received by the polarization-sensitive array, which are coherent circular Gaussian fully polarized signals, ρ1, ρ2,…, ρ M are the attenuation coefficients of M far-field narrowband signals.

[0062] Step S2: Sample the array received data using a signal reception model, and compress the sampled data;

[0063] Specifically:

[0064] When the array moves at equal intervals, sample the array received data using a signal reception model. Denote the initial position of the array as the 1st array, and after moving once, denote it as the 2nd array, and so on. Compress the data received by each array through a compression network to reduce the matrix dimension.

[0065] Step S3: Perform polarization smoothing on the compressed data to obtain an output covariance matrix, and perform mean processing to obtain an array average output covariance matrix;

[0066] Specifically:

[0067] Perform polarization smoothing on the compressed data to obtain an output covariance matrix Move the array N - 1 times, and calculate the mean of the N covariance matrices obtained to obtain the array average output covariance matrix

[0068] Step S4: Use the MUSIC algorithm to perform eigen - decomposition on the array average output covariance matrix to obtain the corresponding noise subspace.

[0069] Step S5: Traverse each angle to generate the corresponding spatial phase matrix in the signal reception model, calculate the spectral peak diagram according to the spatial phase matrix, and the angle corresponding to the peak value is the estimated value.

[0070] Specifically:

[0071] Search within the angle range of interest, traverse each angle to generate the corresponding spatial phase matrix in the signal reception model, and calculate the spectral peak diagram through the following formula;

[0072]

[0073] where, Φ is the compression matrix for compression processing in step S2 above, and U N is the noise subspace obtained in step S4 above.

[0074] Embodiment 3. Refer to Figures 1 to 6 Describe this embodiment. This embodiment gives an overall example of a de - coherent DOA estimation based on a compressed mobile platform orthogonal dipole array described in Embodiment 1 or Embodiment 2;

[0075] The signal reception model is as Figure 1 shown. The polarization - sensitive array consists of N s= A polarization - angle domain rotation - invariant L - shaped regular polarization - sensitive array composed of 12 orthogonal dipoles equally spaced along the X - axis and Y - axis directions. The spacing between adjacent array elements is d = 0.03m. The signal frequency is 5GHz and the signal - to - noise ratio is 10dB. The incident signal parameters (θ, φ, γ, η) of four coherent signal sources are (-25°, -25°, 0.0757, 3.2887), (-10°, -10°, π / 4, π / 4), (5°, 5°, π / 4, π / 4), (15°, 15°, π / 4, π / 4);

[0076] Step 1: Construct a signal reception model. The signal received at the receiving end is the target signal S(t) and the noise signal n(t), and the signal at the output end is X(t).

[0077] Suppose the M far - field narrow - band signals received by the polarization - sensitive array are coherent circular Gaussian fully polarized signals s1(t), s2(t),..., s M (t), then s m (t) can be expressed as s m (t) = ρ m s1(t), m = 1, 2,..., M, where the complex constant ρ m is the attenuation coefficient of s m (t) relative to s1(t), ρ = [ρ1; ρ2; ρ3; ρ4] = [1; e jπ / 6 ; e jπ / 3 ; e jπ / 4 . Thus, the signal output model of the polarization - sensitive array can be obtained as:

[0078]

[0079] where m = 1, 2,…, M; is a 2N s ×M - dimensional signal steering vector matrix, where the m - th column vector is called the steering vector of the m - th signal; S(t) = [s1(t), s2(t),...s M (t)] T is an M - dimensional signal vector, s m (t) is the complex envelope of the m - th signal, and n(t) is a 2N s -dimensional circular Gaussian space - time - polarization white noise vector. Among them, can be expressed as:

[0080]

[0081] where, represents the array space - phase matrix. Suppose the position of the array element is (x n , y n)(n = 1, 2, …, N s ), with the origin as the reference point, the phase delay τ of the m-th source in the n-th array element channel n,m can be expressed as:

[0082]

[0083] Then the n-th element u m,n = exp{-jωτ n,m}, ω = 2πf = 2π(c / λ), where λ is the wavelength of the signal; is denoted as the polarization steering vector, where is the electromagnetic wave propagation matrix, is the polarization vector of the m-th signal, which can be expressed as The electromagnetic wave propagation matrix of the full electromagnetic vector sensor can be expressed as:

[0084]

[0085] In practical applications, usually partial electromagnetic vector information is used. In this embodiment, orthogonal dipoles are used. Thus

[0086]

[0087] Step 2: Sample the array received data when the array moves at equal intervals. Denote the initial position of the array as the 1st array, and after moving once as the 2nd array, and so on. Pass the data received by each array through the compression network. The moving interval is D = 2.5d, as Figure 2 shown.

[0088] Add a combined network module between the polarization sensitive array antenna receiving part and the RF front-end part. Its main body consists of a phase shifter and an accumulator. The function of this module is to compress the received signal. The compression structure is as Figure 3 shown. Let L = 9. The signal obtained after compression can be expressed as

[0089]

[0090] In the formula, Φ is the compression matrix, which is used to represent the function of the combined network, is the noise matrix after compression. Each row in Φ follows a complex Gaussian distribution.

[0091] Denote the initial position of the array as the 1st array, and after moving once as the 2nd array. The total number of translations is N - 1 times. Thus, a total of N arrays can be obtained. Let N = 4.

[0092] When the m-th signal source is incident, the wave path difference τ between the i-th array and the initial array i and the phase difference ξ i are respectively expressed as:

[0093]

[0094] ξ mi = 2πτ mi / λ

[0095] where, is the unit direction vector of the m-th incident signal in the space rectangular coordinate system. l i T is the position coordinate of the central element of the i-th array.

[0096] l i T = [(i - 1)Dcosαcosβ, (i - 1)Dcosαsinβ, (i - 1)Dsinα]

[0097] The mathematical model of the M signals output by the i-th array is:

[0098] X i (t) = A1(θ m , φ m , γ m , η m )P (i-1) S i (t) + N i (t) (i = 1, 2,..., N; m = 1, 2,..., M)

[0099] where, P = diag[exp(-jξ 12 ), exp(-jξ 22 ), …, exp(-jξ M2 )], and P is determined by the spatial position relationship of the array in the specific scenario.

[0100] Step 3: Perform polarization smoothing processing on the compressed data to obtain the output covariance matrix Move the array N - 1 times, and calculate the average of the N covariance matrices obtained to get the average output covariance matrix of the array

[0101] Consider the L-shaped regular polarization-sensitive array as shown Figure 1 . Taking the direction of the array element as the standard, the same scalar arrays are divided into one sub-array, then it can be divided into K = 2 polarization-angle domain matching sub-arrays, as shown Figure 4 . Use the divided sub-arrays to perform polarization domain smoothing.

[0102] According to Figure 4 the counterclockwise order from the negative X-axis direction to the positive Y-axis direction in the array to calculate the steering vector, the signal observation vector X of the k-th subarray (k) (t) can be represented by X (k) (t) = J k X(t) indicates that, where

[0103] J k = [O L×(k-1)L , I L , O L×(K-k)L

[0104] Denote the covariance matrix of the array output as R xx , and the signal covariance matrix as R SS , then the polarization domain smoothing formula is:

[0105]

[0106] To simplify the derivation process, in this embodiment, it is assumed that the array noise after each movement is the same, which is zero-mean additive Gaussian white noise with a variance of , then the covariance matrix of the i-th array is The average representation of the covariance matrices of all arrays can be obtained as:

[0107]

[0108] Step 4: Use the MUSIC algorithm for DOA estimation, perform eigenvalue decomposition on the average output covariance matrix of the array, and find the corresponding noise subspace U n .

[0109] Perform eigenvalue decomposition on the average array output covariance matrix , then:

[0110]

[0111] In the formula, U S is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, while U N is the noise subspace spanned by the eigenvectors corresponding to the small eigenvalues. Due to the orthogonality of the spatial domain steering vector and the noise subspace, we can obtain

[0112]

[0113] Considering the actual situation, perfect orthogonality does not exist, so the DOA is realized by minimum optimization search, that is:

[0114]

[0115] ​Step 5: Traverse each angle, calculate and generate a spectral peak diagram. As Figure 5 shown, the angle corresponding to the peak value is the estimated value.

[0116] Furthermore, in order to verify a de - coherent DOA estimation method based on a compressed mobile platform orthogonal dipole array proposed in the above - mentioned implementation manner, this implementation manner compares the present invention with the de - coherent of an uncompressed mobile platform orthogonal dipole array and the de - coherent of a single moving platform, conducts 200 Monte Carlo experiments, and observes the comparison results of the root - mean - square error of the incident angle estimation of the same number of coherent signal sources under different signal - to - noise ratios.

[0117] Let the number of observations N = 4, the signal - to - noise ratio be set to (5 dB - 20 dB), and conduct T mc = 200 Monte Carlo experiments, observe the root - mean - square error of the incident angle estimation of the same number of coherent signal sources under different signal - to - noise ratios. The results are as Figure 6 shown.

[0118] The root - mean - square error (RMSE) of two - dimensional DOA estimation is defined as follows:

[0119]

[0120] In the formula, T mc represents the total number of Monte Carlo experiments, M represents the number of coherent sources, and are the angle estimation results of the m - th signal in the t - th Monte Carlo experiment, θ m and β m are the actual angles of the m - th signal.

[0121] Analysis of recognition results: As Figure 5 shown, it can be seen that there are obviously four sharp peaks in the spectral peak diagram, and they coincide with the preset DOA values, indicating that under the conditions of 10 dB and the platform moving twice, the present invention has good estimation accuracy for four coherent signal sources. From Figure 6 it can be known that after the signal - to - noise ratio is greater than 8 dB, the de - coherent effect of the present invention is very close to that of the uncompressed mobile platform orthogonal dipole array, and is far better than the de - coherent of a single moving platform. By adding a compression network, the present invention reduces the 12 - channel data to 9 channels. While having good estimation performance, it greatly reduces the computational amount. The simulation results confirm the effectiveness and feasibility of the present invention.

[0122] Embodiment 4. A method for coherent DOA estimation based on an orthogonal dipole array of a compressed mobile platform proposed in the above embodiment can be entirely implemented by computer software. Therefore, correspondingly, this embodiment proposes a system for coherent DOA estimation based on an orthogonal dipole array of a compressed mobile platform, and the system includes:

[0123] A storage device for constructing a signal reception model according to an orthogonal dipole;

[0124] A storage device for sampling the array reception data using the signal reception model and performing compression processing on the sampled data;

[0125] A storage device for performing polarization smoothing processing on the compressed data to obtain an output covariance matrix and performing mean processing to obtain an array average output covariance matrix;

[0126] A storage device for performing eigenvalue decomposition on the array average output covariance matrix using the MUSIC algorithm to obtain the corresponding noise subspace;

[0127] A storage device for traversing each angle to generate a corresponding spatial phase matrix in the signal reception model, calculating a spectral peak diagram according to the spatial phase matrix, and the angle corresponding to the peak value is the estimated value;

[0128] Embodiment 5. This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes a method for coherent DOA estimation based on an orthogonal dipole array of a compressed mobile platform described in any one of the above embodiments.

[0129] Embodiment 6. This embodiment provides a computer device, which includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a method for coherent DOA estimation based on an orthogonal dipole array of a compressed mobile platform described in any one of the above embodiments.

[0130] For the computer device provided in this embodiment, the hardware device of this part is of a general model and is not shown in the form of a diagram. The system includes a processor and a memory. The processor and the memory can be connected through a bus or other means. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, as well as corresponding program instructions / modules. The processor runs the non-transitory software programs, instructions, and modules stored in the memory, thereby executing various functional applications and data processing of the processor to implement the method for coherent DOA estimation based on an orthogonal dipole array of a compressed mobile platform and the steps in the method embodiment.

[0131] The above are only embodiments of the present invention and do not limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array, characterized in that: The method is: S1: Construct a signal reception model based on orthogonal dipoles; S2: using the signal receiving model to sample the array received data and compressing the sampled data; S3: performing polarization smoothing on the compressed data to obtain an output covariance matrix, and performing mean processing to obtain an array average output covariance matrix; S4: Use the MUSIC algorithm to perform eigendecomposition on the array average output covariance matrix and find the corresponding noise subspace; S5: traverse each angle to generate the corresponding spatial phase matrix in the signal reception model, calculate the spectrum peak diagram according to the spatial phase matrix, and the angle corresponding to the peak is the estimated value.

2. The method for decorrelation DOA estimation based on a compressed mobile platform orthogonal dipole array according to claim 1, characterized in that: Polarization smoothing and compressed polarization sensitive arrays are introduced into the mobile platform, and an orthogonal dipole array is used to replace the scalar array in the mobile platform to obtain a compressed mobile platform orthogonal dipole array.

3. The decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array according to claim 1, characterized in that: S1 is specifically: Orthogonal double dipoles equidistantly arranged along the X-axis and the Y-axis are used to form a polarization-angle domain rotationally invariant L-shaped regular polarization sensitive array as a signal receiving model.

4. The decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array according to claim 3, characterized in that: The signal receiving model is expressed as: Where X(t) is the output signal, S(t) and n(t) are the target signal and noise signal received by the receiver, A is the signal steering vector matrix, s1(t), s2(t), ..., s M (t) is the M far-field narrowband signals received by the polarization sensitive array, which are coherent circular Gaussian fully polarized signals, ρ1, ρ2, …, ρ M is the attenuation coefficient of the M far-field narrowband signals.

5. The method for decorrelation DOA estimation based on a compressed mobile platform orthogonal dipole array according to claim 1, characterized in that: S2 is specifically: When the array moves with equal spacing, the signal receiving array model is used to sample the array receiving data. The initial position of the array is recorded as the first array, and the position after moving once is recorded as the second array, and so on. The data received by each array is compressed through a compression network.

6. The decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array according to claim 5, characterized in that: The compression network consists of a phase shifter and an accumulator.

7. The decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array according to claim 1, characterized in that: Polarization smoothing processing is specifically: smoothing processing is performed jointly in the polarization domain and the spatial domain.

8. A decorrelation DOA estimation system based on a compressed mobile platform orthogonal dipole array, characterized in that: The system comprises a storage device for executing the method and steps described in claim 1.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for decorrelation DOA estimation based on a compressed mobile platform orthogonal dipole array according to any one of claims 1 to 7 is executed.

10. A computer device, characterized in that: The device includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a decorrelation DOA estimation method based on a compressed mobile platform orthogonal dipole array as described in any one of claims 1 to 7.

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