Conformal array direction finding method and system based on mixed resolution quantification

Abstract

The invention provides a conformal array direction finding method and system based on mixed resolution quantification, relates to the field of DOA (direction of arrival) estimation in a passive radar, and aims to solve the problem of high resource consumption of a conformal array during DOA estimation. Therefore, the deployment problem during direction finding under the condition of limited space of unmanned aerial vehicle positioning or vehicle-mounted radar and the like is solved. The method comprises the following steps: designing a conformal array structure; constructing a model of a receiving antenna receiving signal and a transmitting antenna transmitting signal to obtain receiving data; carrying out high-precision quantization and low-precision quantization data receiving by adopting the designed structure, and processing the low-precision quantization data according to an additive Gaussian noise model to obtain receiving data under mixed resolution quantization; performing covariance decomposition on the received data under the mixed resolution quantification; and performing spectral peak search on the mixed resolution quantized data after covariance decomposition, and estimating DOA information and polarization information. The direction finding method is suitable for the conformal array.

Description

Technical Field

[0001] The present invention belongs to the technical field of direction of arrival (DOA) estimation in passive radar, and particularly relates to a direction finding method based on hybrid resolution quantization and applicable to conformal arrays. Background Technique

[0002] A conformal array refers to an array in which antennas are attached to the surface of a carrier. It has the characteristics of saving space, making full use of the aperture, meeting the requirements of aerodynamics, being easy to achieve wide-angle scanning, and reducing the radar cross section. It has broad application prospects in the fields of aviation, communication, radar, etc. The research on DOA estimation and polarization parameter estimation methods is one of the important tasks to promote the continuous engineering application of conformal array technology.

[0003] The DOA estimation problem is of great significance in the field of array signal processing and has extensive applications in fields such as automotive radar, sonar, and wireless communication. In the case of using high-bandwidth signals, it involves high-precision analog-to-digital converters that require a large amount of power consumption and hardware costs. However, as the quantization bits and sampling rate increase, the power consumption and hardware costs of analog-to-digital converters (ADCs) increase exponentially. Using a one-bit analog-to-digital converter (ADC) is a promising technology to alleviate the above ADC problems. One-bit sampling based on a time-varying threshold scheme can eliminate the ambiguity between the signal amplitude and noise variance. However, a pure one-bit ADC system has problems such as large rate loss, especially in high signal-to-noise ratio states and dynamic range problems, that is, strong targets can mask weak targets. Summary of the Invention

[0004] The purpose of the present invention is to provide a direction finding method for a conformal array based on hybrid resolution quantization, which is used to reduce the problem of large resource consumption existing in DOA estimation of conformal arrays, thereby solving the deployment problem in direction finding under space-limited conditions such as UAV positioning or vehicle-mounted radar.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] The present invention proposes a direction finding method for a conformal array based on hybrid resolution quantization, and the method includes the following steps:

[0007] Step S1: Design the conformal array structure;

[0008] Step S2: Respectively construct models for the signals received by the receiving antennas and the signals transmitted by the transmitting antennas, and obtain the received data according to the transmitting signal model and the receiving signal model;

[0009] Step S3: Receive high-precision quantization and low-precision quantization data using the structure designed in Step S1, and process the low-precision quantization data according to the additive Gaussian noise model to obtain the received data under mixed-resolution quantization;

[0010] Step S4: Perform covariance decomposition on the received data under mixed-resolution quantization;

[0011] Step S5: Perform spectral peak search on the mixed-resolution quantization data after covariance decomposition to estimate DOA information and polarization information.

[0012] Further, the above Step S1 is specifically as follows:

[0013] Design the conformal array arrangement form of m antennas, design the positions of low-resolution receiving channels, and design the low-resolution bit calculation, and calculate the corresponding quantization factor α according to the low-resolution bits.

[0014] Further, the received signals of the above receiving antennas include the target signal S(t) and the noise signal n(t);

[0015] The transmitted signals of the transmitting antennas include frequency, direction of arrival, and signal source polarization parameters.

[0016] Further, the above received data is expressed as:

[0017]

[0018] where s k (t) represents the k-th narrowband signal, represents the steering vector of the k-th signal composed of spatial response and polarization response, and A is the signal steering vector matrix.

[0019] Further, the received data under the above mixed-resolution quantization is expressed as:

[0020]

[0021] where A L and A H respectively represent the low- and high-resolution signal steering vector matrices, n L (t) and n H (t) respectively represent the low- and high-resolution noise signals, w Q (t) represents the quantization noise uncorrelated with the low-resolution quantization data x L (t).

[0022] Further, the above Step S4 is specifically as follows:

[0023] Calculate the covariance of the data of k snapshots received The covariance Perform eigenvalue decomposition, where the eigenvectors corresponding to the n largest eigenvalues are The eigenvectors corresponding to the (m - K) smallest eigenvalues are

[0024] Furthermore, the specific steps of the above step S5 are as follows:

[0025] Divide the two-dimensional area to be searched into grid points. According to the array information, calculate the part of the steering vector corresponding to each grid point excluding polarization, and multiply the channel corresponding to the low-resolution quantization by the corresponding quantization factor α to obtain D θ,φ , calculate the corresponding value. The angle corresponding to the point with the largest value is the estimated DOA, and then calculate the corresponding polarization parameters.

[0026] A conformal array direction finding method based on mixed-resolution quantization proposed by the present invention can be fully implemented by computer software. Therefore, the present invention proposes a conformal array direction finding system based on mixed-resolution quantization. The direction finding system includes:

[0027] A storage device for designing the conformal array structure;

[0028] A storage device for respectively constructing models of the signals received by the receiving antennas and the signals transmitted by the transmitting antennas, and obtaining the received data according to the transmitting signal model and the receiving signal model;

[0029] A storage device for receiving high-precision quantization and low-precision quantization data using the structure designed in step S1, and processing the low-precision quantization data according to the additive Gaussian noise model to obtain the received data under mixed-resolution quantization;

[0030] A storage device for performing covariance decomposition on the received data under mixed-resolution quantization;

[0031] A storage device for performing spectral peak search on the mixed-resolution quantization data after covariance decomposition to estimate the DOA information and polarization information.

[0032] The present invention 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 conformal array direction finding method based on mixed-resolution quantization described in any one of the above.

[0033] The present invention 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 conformal array direction finding method based on mixed-resolution quantization described in any one of the above.

[0034] The beneficial effects of the present invention are as follows:

[0035] The present invention proposes a direction finding algorithm for a hybrid resolution receiving structure applicable to a conformal array. By designing the conformal array structure, high- and low-precision quantization data are received. At the same time, the low-precision quantization data are processed according to the additive Gaussian noise model to obtain the received data under hybrid resolution quantization. The received data under hybrid resolution quantization is used for DOA estimation, which solves the problem of large resource consumption in DOA estimation of the conformal array, thus effectively solving the antenna arrangement problem in a limited space, providing a wider field of view, reducing resource consumption, and providing a new solution for direction finding under the conditions of miniaturization and low power consumption.

[0036] Furthermore, since the existing rank-deficient MUSIC algorithm is mainly applied to the processing of high-precision ADC quantization data. Therefore, the present invention processes the signal after hybrid resolution quantization, enabling the rank-deficient MUSIC algorithm to process the received data under hybrid resolution quantization, so that the rank-deficient MUSIC algorithm can still be used in the case of hybrid resolution quantization reception.

[0037] The present invention relates to a direction finding method applicable to a conformal array. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order 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, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic diagram of an eight-element uniform circular array proposed by the present invention, where Fig. (a) is a perspective view and Fig. (b) is a front view;

[0040] Figure 2 It is the spatial spectrum under different quantization structures of the present invention, where Fig. (a) is the spatial spectrum of the hybrid resolution quantization structure and Fig. (b) is the 1-bit quantization spatial spectrum;

[0041] Figure 3 It is the polarization spectrum under different quantization structures of the present invention, where Fig. (a) is the polarization spectrum under the hybrid resolution quantization structure and Fig. (b) is the polarization spectrum under 1-bit quantization;

[0042] Figure 4 It is the statistical chart of the DOA and polarization information estimation accuracy under different quantization structures of the present invention, where Fig. (a) is the DOA estimation accuracy and Fig. (b) is the polarization parameter estimation accuracy;

[0043] Figure 5 It is the statistics of the DOA and polarization information estimation accuracy under different quantization channel selections described in the present invention. Among them, Figure (a) is the DOA estimation accuracy, and Figure (b) is the polarization parameter estimation accuracy. Specific embodiments

[0044] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as 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 hindering the description of the present application.

[0045] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0046] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The following embodiments will help those skilled in the art to 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 belong to the protection scope of the present invention.

[0047] Embodiment 1. The conformal array can effectively conform to the shape of the carrier platform. This not only solves the deployment problem during direction finding under space-limited conditions but also provides a wider field of view. However, in applications such as UAV positioning or vehicle-mounted radar, the available space for the antenna array is often limited, and the computing resources are also restricted. Therefore, miniaturization and low power consumption are urgent challenges to be solved. The conformal array can effectively solve the miniaturization problem, and a direction-finding method for a conformal array based on hybrid-resolution quantization proposed in this embodiment can further reduce resource consumption, having important practical engineering value.

[0048] The direction-finding method for a conformal array based on hybrid-resolution quantization includes the following steps:

[0049] Step S1: Design the conformal array structure;

[0050] Step S2: Respectively construct models for the signals received by the receiving antennas and the signals transmitted by the transmitting antennas, and obtain the received data according to the transmitted signal model and the received signal model;

[0051] Step S3: Receive high-precision quantization and low-precision quantization data using the structure designed in Step S1, and process the low-precision quantization data according to the additive Gaussian noise model to obtain the received data under hybrid resolution quantization;

[0052] Step S4: Perform covariance decomposition on the received data under hybrid resolution quantization;

[0053] Step S5: Perform spectral peak search on the hybrid resolution quantization data after covariance decomposition to estimate the DOA information and polarization information.

[0054] Since when the antenna actually receives data, the ADC needs to convert the analog signal into a digital signal and then perform subsequent signal processing. When using a high-precision (high-resolution) ADC (more than a dozen-bit quantization), the number of data bits is large and the resource occupancy is high. While when using a low-precision ADC (such as single-bit quantization), the data precision is not high enough, which brings errors to subsequent processing. Therefore, this embodiment proposes a conformal array direction finding method based on hybrid resolution quantization. By designing the conformal array structure, high- and low-precision quantization data are received, and at the same time, the low-precision quantization data is processed according to the additive Gaussian noise model to obtain the received data under hybrid resolution quantization; the received data under hybrid resolution quantization is used for DOA estimation, which solves the problem of large resource consumption in DOA estimation of the conformal array, thus effectively solving the problem of antenna array layout in a limited space, providing a wider field of view, reducing resource consumption, and providing a new solution for direction finding under the conditions of miniaturization and low power consumption.

[0055] Furthermore, since the existing rank-deficient MUSIC algorithm is mainly applied to the processing of high-precision ADC quantization data. Therefore, in the present invention, by processing the signal after hybrid resolution quantization, the rank-deficient MUSIC algorithm can process the received data under hybrid resolution quantization, so that the rank-deficient MUSIC algorithm can still be used in the case of hybrid resolution quantization reception.

[0056] Embodiment 2. Refer to Figures 1 to 5 To describe this embodiment, this embodiment specifically describes a conformal array direction finding method based on hybrid resolution quantization proposed in Embodiment 1;

[0057] Step S1: Design the conformal array structure;

[0058] Specifically:

[0059] Design the conformal array arrangement of m antennas, design the positions of the low-resolution receiving channels, and design the low-resolution bit calculation, and calculate the corresponding quantization factor α according to the low-resolution bits. Since it is necessary to conform to the surface of the carrier, short dipole antennas are generally used for conformal arrays. The signal model described in this embodiment is applicable to any array. Since the cylindrical conformal antenna array has a good field of view, therefore, Figure 1 Taking the eight-element circular array described as an example for illustration, the radius of the eight-element circular array is 0.1 m. Select the 1st, 3rd, 5th, and 7th antennas as the positions of the low-resolution receiving channels, perform low-resolution quantization on the data received by them, and use the remaining antennas as the positions of the high-resolution receiving channels, and perform high-resolution quantization on the data received by them.

[0060] Step S2: Construct the models of the signals received by the receiving antennas and the signals transmitted by the transmitting antennas respectively, and obtain the received data according to the transmitting signal model and the receiving signal model;

[0061] Specifically:

[0062] Construct the models of the signals received by the receiving antennas and the signals transmitted by the transmitting antennas. Design K transmitting-end signal models including frequency, direction of arrival, and signal source polarization parameters. The signals received by the receiving end include the target signal S(t) and the noise signal n(t).

[0063] Assume that the transmitted signal S(t) is a narrowband signal and the noise n(t) is Gaussian thermal noise. According to the principle of array signal processing, the received signal data is expressed as:

[0064]

[0065] Among them, s k (t) represents the kth narrowband signal, represents the steering vector of the kth signal composed of the spatial response and the polarization response, which is a function of the DOA parameter and the polarization parameter, and A is the signal steering vector matrix.

[0066] Among them, the element spatial phase matrix of the kth signal is expressed as:

[0067]

[0068] This matrix is a diagonal matrix, where the nth diagonal element u n,k = exp(-j2π[p T (θ k , φ k )r n / λ) represents the spatial phase factor of the kth signal associated with the nth antenna element. In addition, p(θ k , φ k ) = -[sinφ kcosθ k , sinφ k sinθ k T is the signal propagation vector, r n is the position vector of the antenna, and λ is the wavelength.

[0069] On the other hand, the polarization response is divided into three parts, namely, the polarization sensitivity matrix B brought by different pointing forms of the short dipole antenna, the conversion matrix from the polarization coordinate system to the plane coordinate system and the polarization form of the signal source Stacking the polarization sensitivity characteristics of all short dipole antennas together, B is expressed as:

[0070] B = [B 1 , B 2 ,..., B M T

[0071] where, B l = [cosα l sinα l T , and α l is the antenna pointing of the l-th antenna. To unify the polarization coordinates and the array coordinates, the conversion matrix from the polarization coordinate system to the plane coordinate system for the array composed of short dipoles is:

[0072]

[0073] The first column represents the horizontal polarization component in the plane, while the second column represents the vertical polarization component in the plane. Finally, is the polarization form of the k-th signal source, defined as:

[0074]

[0075] Therefore, the steering vector of the k-th signal can be expressed as:

[0076]

[0077] Step S3: Use the structure designed in the above Step S1 to receive high-precision quantization and low-precision quantization data, and process the low-precision quantization data according to the additive Gaussian noise model to obtain the received data under mixed-resolution quantization; where, the selection of the quantization factor α is shown in Table 1.

[0078] Table 1

[0079] b (bit) 1 2 3 4 5 β 0.3634 0.1175 0.03454 0.009497 0.002499

[0080] Let M L and M H ​​​respectively represent the numbers of low-resolution and high-resolution ADCs. The mixed quantized received signal is expressed as:

[0081]

[0082] where x L (t) and x L (t) respectively represent the signals to be quantized at low resolution and high resolution.

[0083] Since the data of the low-precision quantization channel conforms to the additive Gaussian noise model, in this embodiment, the signal quantized at low resolution is processed by using the additive quantization noise model to obtain a new low-resolution quantized received signal:

[0084]

[0085] where represents the quantization function, and w Q (t) represents the quantization noise uncorrelated with x L (t). The coefficient α = 1 - β is the linear quantization gain, where β represents the loss factor of the low-resolution ADC, defined as:

[0086]

[0087] When the quantization precision is low (b ≤ 5), the estimated values of the loss factor are shown in Table 1. For larger quantization bits (b > 5), the loss factor can be approximated as:

[0088]

[0089] In addition, the covariance matrix of the quantization noise w Q (t) is related to the antenna receiving amplitude and is expressed as:

[0090]

[0091] Therefore, the total signal received after mixed-resolution quantization is expressed as:

[0092]

[0093] where where, A L and A H respectively represent the low- and high-resolution signal steering vector matrices, n L (t) and n H (t) respectively represent the low- and high-resolution noise signals, and w Q (t) represents the quantization noise uncorrelated with the low-resolution quantized data x L (t).

[0094] Step S4: Perform covariance decomposition on the received data under mixed-resolution quantization;

[0095] Specifically:

[0096] Perform covariance decomposition on the received data: Calculate the covariance of the k snapshots of the received data Perform eigenvalue decomposition on the covariance Among them, the eigenvectors corresponding to the n large eigenvalues are what we call The eigenvectors corresponding to the (m - K) small eigenvalues are called

[0097] Step S5: Perform spectral peak search on the mixed-resolution quantization data after covariance decomposition to estimate DOA information and polarization information.

[0098] Specifically:

[0099] Perform spectral peak search on the data after mixed-resolution quantization to estimate DOA information and polarization information. Divide the two-dimensional area to be searched into grid points. According to the array information, calculate the part of the steering vector corresponding to each grid point excluding polarization. For the channels corresponding to low-resolution quantization, multiply by the corresponding quantization factor α. The resulting part is what we call D θ,φ , calculate the corresponding value for each point. Among them, the angle corresponding to the point with the largest value is the estimated DOA. Then, calculate the corresponding polarization parameters according to the method of finding the optimal solution.

[0100] Among them, according to Step S2, we express D in different grid points θ,φ as:

[0101] D θ,φ = ΓΥ θ,φ BΞ θ,φ

[0102] Let Calculate the matrix for each grid point, and then compare the determinant values. The angle corresponding to the grid point with the smallest value is the estimated DOA, as shown in Figure 2 (a):

[0103]

[0104] After estimating the DOA information, next, perform the estimation of polarization information. Use the Lagrange gradient method to obtain the solution of (H(θ k , φ k ) - λI)h γ,η = 0, which is the estimated polarization information. In addition, the search method can also be used to estimate the polarization information, as shown in Figure 3As shown in (a).

[0105] Embodiment 3: Refer to Figures 2 to 5 To describe this embodiment, this embodiment compares the conformal array direction finding method based on hybrid resolution quantization proposed in the above embodiment with the existing 1-bit quantization method to fully illustrate the effect of the conformal array direction finding method based on hybrid resolution quantization;

[0106] Specifically: As Figure 2 and Figure 3 shown, compared with full low-resolution quantization, both the spatial spectrum and polarization spectrum peaks of the hybrid quantization structure proposed by the present invention are significantly higher, and the estimation accuracy is improved.

[0107] Furthermore, through 2000 Monte Carlo experiments for statistics, the root mean square error of the angle measurement accuracy is obtained as the accuracy measurement standard for comparison. By comparing the estimation accuracies under different bit quantization structures, the performance improvement and power consumption reduction brought by the hybrid resolution quantization structure are analyzed.

[0108] As Figure 4 shown, as the signal-to-noise ratio increases, the accuracy of parameter estimation improves. Hybrid resolution quantization is superior to full low-resolution quantization. At low signal-to-noise ratios, the main error is caused by thermal noise, while at high signal-to-noise ratios, the quantization error dominates. The RMSE increases as the number of quantization bits decreases, but the reduction in accuracy is small. Therefore, the hybrid quantization method can slightly sacrifice accuracy in exchange for reduced resource consumption.

[0109] As Figure 5 shown, the selection of quantization antennas also affects the estimation results, because low-resolution quantization is used at antennas with high received amplitudes, which will cause an increase in quantization noise. Based on this characteristic, we can adjust the quantization structure to improve the estimation accuracy in the direction of interest.

[0110] Embodiment 4: The conformal array direction finding method based on hybrid resolution quantization proposed in the above embodiment can be fully implemented by computer software. Therefore, this embodiment proposes a conformal array direction finding system based on hybrid resolution quantization, and the direction finding system includes:

[0111] A storage device for designing the conformal array structure;

[0112] A storage device for respectively constructing models of the signals received by the receiving antennas and the signals transmitted by the transmitting antennas, and obtaining the received data according to the transmitted signal model and the received signal model;

[0113] A storage device for receiving high-precision quantization and low-precision quantization data using the structure designed in step S1, and processing the low-precision quantization data according to the additive Gaussian noise model to obtain the received data under mixed-resolution quantization;

[0114] A storage device for performing covariance decomposition on the received data under mixed-resolution quantization;

[0115] A storage device for performing spectral peak search on the mixed-resolution quantization data after covariance decomposition to estimate DOA information and polarization information.

[0116] Embodiment 5. This embodiment provides a computer-readable storage medium. 9. A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes a conformal array direction finding method based on mixed-resolution quantization described in any one of the above embodiments.

[0117] Embodiment 6. This embodiment provides a computer device, which includes a memory and a processor. A computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a conformal array direction finding method based on mixed-resolution quantization described in any one of the above embodiments.

[0118] For the computer device provided in this embodiment, the hardware device in 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 conformal array direction finding method and steps based on mixed-resolution quantization in the above method embodiments.

[0119] The above are only the embodiments of the present invention and do not limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. 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 conformal array direction finding method based on hybrid resolution quantization, characterized in that: The method is: S1: Design the conformal array structure; S2: constructing models of the receiving antenna receiving signal and the transmitting antenna transmitting signal respectively, and obtaining receiving data according to the transmitting signal model and the receiving signal model; S3: using the structure designed in step S1 to receive high-precision quantization and low-precision quantization data, and processing the low-precision quantization data according to an additive Gaussian noise model to obtain received data under mixed resolution quantization; S4: performing covariance decomposition on the received data under mixed resolution quantization; S5: Perform spectrum peak search on the mixed resolution quantized data after covariance decomposition to estimate DOA information and polarization information.

2. The conformal array direction finding method based on hybrid resolution quantization according to claim 1, characterized in that: S1 is specifically: Design the conformal array arrangement of m antennas, design the position of the low-resolution receiving channel, design the number of low-resolution bits, and calculate the corresponding quantization factor α based on the number of low-resolution bits.

3. The conformal array direction finding method based on hybrid resolution quantization according to claim 1, characterized in that: The received signal of the receiving antenna includes the target signal S(t) and the noise signal n(t); The transmission signal of the transmitting antenna includes frequency, incoming wave direction and signal source polarization parameters.

4. The conformal array direction finding method based on hybrid resolution quantization according to claim 3, characterized in that: The received data is represented as: Among them, s k (t) represents the kth narrowband signal, represents the steering vector of the kth signal composed of spatial response and polarization response, and A is the signal steering vector matrix.

5. The conformal array direction finding method based on hybrid resolution quantization according to claim 1, characterized in that: The received data under mixed resolution quantization is expressed as: Among them, A L and A H Respectively represent the low and high resolution signal steering vector matrices, n L (t) and n H (t) represent low and high resolution noise signals, respectively, w Q (t) represents the low-resolution quantized data x L (t) Uncorrelated quantization noise.

6. The conformal array direction finding method based on hybrid resolution quantization according to claim 1, characterized in that: S4 is specifically: Calculate the covariance of the received k snapshots The covariance Perform eigenvalue decomposition, where the eigenvectors corresponding to the n largest eigenvalues ​​are The eigenvectors corresponding to the (mK) small eigenvalues ​​are 7. The conformal array direction finding method based on hybrid resolution quantization according to claim 6, characterized in that: S5 is as follows: The two-dimensional area to be searched is divided into grid points. According to the array information, the corresponding steering vector at each grid point is calculated, excluding the polarized part. The corresponding low-resolution quantized channel is multiplied by the corresponding quantization factor α to obtain D θ,φ , calculate the corresponding The angle corresponding to the largest point is the estimated DOA, and then the corresponding polarization parameters are calculated.

8. A conformal array direction finding system based on hybrid resolution quantization, 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 conformal array direction finding method based on mixed resolution quantization according to any one of claims 1 to 7 is executed.

10. A computer device, characterized in that: The device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a conformal array direction finding method based on mixed resolution quantization as claimed in any one of claims 1 to 7.

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