A conformal array direction finding method based on mixed resolution quantization and system thereof

Abstract

This invention proposes a conformal array direction finding method and system based on hybrid resolution quantization, relating to the field of direction-of-arrival (DOA) estimation in passive radar. It aims to reduce the high resource consumption of conformal arrays in DOA estimation, thereby solving the deployment challenges of direction finding under space-constrained conditions such as UAV positioning or vehicle-mounted radar. The method involves designing the conformal array structure; constructing models of the received signal from the receiving antenna and the transmitted signal from the transmitting antenna to obtain received data; using the designed structure to receive high-precision and low-precision quantized data, and processing the low-precision quantized data according to an additive Gaussian noise model to obtain received data under hybrid resolution quantization; performing covariance decomposition on the received data under hybrid resolution quantization; and performing spectral peak search on the covariance-decomposed hybrid resolution quantized data to estimate DOA and polarization information. This invention is applicable to direction finding methods for conformal arrays.

Description

Technical Field

[0001] This invention belongs to the field of Direction of Arrival (DOA) estimation technology in passive radar, specifically relating to a direction finding method based on hybrid resolution quantization and applicable to conformal arrays. Background Technology

[0002] Conformal arrays refer to antenna arrays attached to the surface of a carrier. They offer advantages such as space saving, full utilization of aperture, meeting aerodynamic requirements, ease of wide-angle scanning, and reduced radar cross-section, making them promising for applications in aviation, communications, and radar. Research on DOA estimation and polarization parameter estimation methods is a crucial step in driving the engineering applications of conformal array technology.

[0003] The DOA estimation problem is of great significance in the field of array signal processing and has wide applications in automotive radar, sonar, and wireless communication. When using high-bandwidth signals, high-precision analog-to-digital converters (ADCs) are required, necessitating significant power consumption and hardware costs. However, with the increase in quantization bits and sampling rates, the power consumption and hardware cost of analog-to-digital converters (ADCs) increase exponentially. Utilizing a single-bit ADC is a promising technique to alleviate these ADC problems. One-bit sampling based on a time-varying threshold scheme can eliminate the ambiguity between signal amplitude and noise variance. However, pure one-bit ADC systems suffer from problems such as large rate loss, especially in high signal-to-noise ratio (SNR) conditions and dynamic range issues, where strong targets can mask weak targets. Summary of the Invention

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

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention proposes a conformal array direction finding method based on hybrid resolution quantization, the method comprising the following steps:

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

[0008] Step S2: Construct models for the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and obtain the received data based on the transmitted signal model and the received signal model;

[0009] Step S3: Using the structure designed in step S1, high-precision quantization and low-precision quantization data are received, and the low-precision quantization data is processed 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 quantized data after covariance decomposition to estimate DOA and polarization information.

[0012] Furthermore, step S1 above specifically includes:

[0013] Design the conformal array arrangement of m antennas, design the location of the low-resolution receiving channel, and design the number of low-resolution bits. Calculate the corresponding quantization factor α based on the number of low-resolution bits.

[0014] Furthermore, the received signals of the above-mentioned receiving antenna include the target signal S(t) and the noise signal n(t);

[0015] The transmitted signal from the transmitting antenna includes the frequency, direction of arrival, and polarization parameters of the signal source.

[0016] Furthermore, the received data described above is represented as follows:

[0017]

[0018] Among them, s k (t) represents the k-th narrowband signal. Let A represent the steering vector of the k-th signal, which is composed of the spatial response and polarization response, and let A be the signal steering vector matrix.

[0019] Furthermore, the received data under the above mixed resolution quantization is represented as follows:

[0020]

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

[0022] Furthermore, step S4 above specifically includes:

[0023] Calculate the covariance of the received k snapshots. covariance Perform eigenvalue decomposition, where the eigenvectors corresponding to the n largest eigenvalues ​​are: The eigenvectors corresponding to the (mK) small eigenvalues ​​are

[0024] Furthermore, step S5 above specifically includes:

[0025] The two-dimensional region to be searched is divided into grid points. Based on the array information, the polarization-free portion of the steering vector at each grid point is calculated. The corresponding low-resolution quantized channel is then multiplied by the corresponding quantization factor α to obtain D. θ,φ Calculate the corresponding value for each point The angle corresponding to the point with the largest value is the estimated DOA, and then the corresponding polarization parameters are calculated.

[0026] The conformal array direction finding method based on hybrid resolution quantization proposed in this invention can be fully implemented using computer software. Therefore, this invention proposes a conformal array direction finding system based on hybrid resolution quantization, the direction finding system comprising:

[0027] Storage devices used for designing conformal array structures;

[0028] A storage device for constructing models of the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and for obtaining the received data based on the transmitted signal model and the received 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] Storage device for covariance decomposition of received data under mixed resolution quantization;

[0031] Storage device used to perform spectral peak search and estimate DOA and polarization information for mixed-resolution quantized data after covariance decomposition.

[0032] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs a conformal array direction finding method based on hybrid resolution quantization as described above.

[0033] The present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a conformal array direction finding method based on hybrid resolution quantization as described above.

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

[0035] This invention proposes a direction-finding algorithm suitable for a hybrid resolution receiver structure of conformal arrays. By designing the conformal array structure, it receives high- and low-precision quantized data. Simultaneously, the low-precision quantized data is processed according to an additive Gaussian noise model to obtain received data under hybrid resolution quantization. Using the received data under hybrid resolution quantization for DOA estimation solves the problem of high resource consumption in conformal arrays during DOA estimation, thus effectively solving the antenna array problem in limited space. It also provides a wider field of view and reduces resource consumption, providing a new solution for direction finding under miniaturized and low-power conditions.

[0036] Furthermore, since existing rank-deficient MUSIC algorithms are mainly applied to the processing of high-precision ADC quantized data, this invention processes the signal after mixed-resolution quantization, enabling the rank-deficient MUSIC algorithm to handle received data under mixed-resolution quantization, thus allowing it to still be used in mixed-resolution quantization reception.

[0037] This invention is applicable to the direction finding method of conformal arrays. Attached Figure Description

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the eight-element uniform circular array proposed in this invention, wherein Figure (a) is an oblique view and Figure (b) is a front view;

[0040] Figure 2 These are the spatial spectra under different quantization structures described in this invention, wherein Figure (a) is the spatial spectrum of the mixed resolution quantization structure and Figure (b) is the spatial spectrum of the 1-bit quantization structure.

[0041] Figure 3 These are the polarization spectra under different quantization structures described in this invention, wherein Figure (a) is the polarization spectrum under the mixed resolution quantization structure, and Figure (b) is the polarization spectrum under 1-bit quantization.

[0042] Figure 4 The present invention presents the statistical results of DOA and polarization information estimation accuracy under different quantization structures, wherein Figure (a) shows the DOA estimation accuracy and Figure (b) shows the polarization parameter estimation accuracy.

[0043] Figure 5 The present invention presents the statistical results of DOA and polarization information estimation accuracy under different quantization channel selections, wherein Figure (a) shows the DOA estimation accuracy and Figure (b) shows the polarization parameter estimation accuracy. Detailed Implementation

[0044] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[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, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0046] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0047] Implementation Method 1: Conformal arrays can effectively conform to the shape of the carrier platform. This not only solves the deployment problem when performing direction finding under space-constrained conditions, but also provides a wider field of view. However, in UAV positioning or vehicle-mounted radar applications, the available space for antenna arrays is often limited, and computational resources are also restricted. Therefore, miniaturization and low power consumption are urgent challenges that need to be addressed. Conformal arrays can effectively solve the miniaturization problem, and the conformal array direction finding method based on hybrid resolution quantization proposed in this implementation method can further reduce resource consumption and has significant practical engineering value.

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

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

[0050] Step S2: Construct models for the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and obtain the received data based on the transmitted signal model and the received signal model;

[0051] Step S3: Using the structure designed in step S1, high-precision quantization and low-precision quantization data are received, and the low-precision quantization data is processed according to the additive Gaussian noise model to obtain the received data under mixed resolution quantization.

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

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

[0054] Because the analog signal needs to be converted into a digital signal by an ADC during actual data reception, and then processed, using a high-precision (high-resolution) ADC (quantization of tens of bits) results in a large number of data bits and high resource consumption, while using a low-precision ADC (such as single-bit quantization) leads to insufficient data accuracy and errors in subsequent processing. Therefore, this implementation proposes a conformal array direction finding method based on mixed resolution quantization. By designing the conformal array structure, it receives high- and low-precision quantized data, and processes the low-precision quantized data according to an additive Gaussian noise model to obtain the received data under mixed resolution quantization. Using the received data under mixed resolution quantization for DOA estimation solves the problem of high resource consumption in conformal array DOA estimation, thus effectively solving the antenna array problem in limited space, providing a wider field of view, reducing resource consumption, and providing a new solution for direction finding under miniaturized and low-power conditions.

[0055] Furthermore, since existing rank-deficient MUSIC algorithms are mainly applied to the processing of high-precision ADC quantized data, this invention processes the signal after mixed-resolution quantization, enabling the rank-deficient MUSIC algorithm to handle received data under mixed-resolution quantization, thus allowing it to still be used in mixed-resolution quantization reception.

[0056] Implementation Method 2, see below Figures 1 to 5 This embodiment describes in detail 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 a conformal array arrangement of m antennas, design the location of the low-resolution receiving channel, and design the number of low-resolution bits, and calculate the corresponding quantization factor α based on the number of low-resolution bits. Since the conformal array needs to fit the carrier surface, short dipole antennas are generally used. The signal model described in this embodiment is applicable to any array. Because cylindrical conformal antenna arrays have a better field of view, therefore... Figure 1 Taking an eight-element circular array as an example, the radius of the eight-element circular array is 0.1m. Antennas 1, 3, 5, and 7 are selected as low-resolution receiving channel positions, and the data received by them is quantized at low resolution. The remaining antennas are selected as high-resolution receiving channel positions, and the data received by them is quantized at high resolution.

[0060] Step S2: Construct models for the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and obtain the received data based on the transmitted signal model and the received signal model;

[0061] Specifically:

[0062] Models are constructed for the signals received by the receiving antenna and transmitted by the transmitting antenna. K transmitter signal models are designed, including frequency, direction of arrival, and signal source polarization parameters. The signals received by the receiver include the target signal S(t) and the noise signal n(t).

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

[0064]

[0065] Among them, s k (t) represents the k-th narrowband signal. Let A represent the steering vector of the k-th signal, which consists of the spatial response and polarization response. It is a function of the DOA parameter and the polarization parameter, and A is the signal steering vector matrix.

[0066] The element-space phase matrix of the k-th signal is represented 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 k-th signal associated with the n-th antenna element. Furthermore, p(θ) k ,φ k )=-[sinφ kcosθ k ,sinφ k sinθ k ] T It is the signal propagation vector, r n λ is the antenna's position vector, and λ is the wavelength.

[0069] On the other hand, the polarization response consists of three parts: the polarization sensitivity matrix B resulting from different pointing forms of the short dipole antenna, the transformation matrix from the polarization coordinate system to the planar coordinate system, and so on. and signal source polarization form The polarization sensitivity characteristics of all short dipole antennas are stacked together, and B is represented as:

[0070] B = [B1, B2, ..., B M ] T

[0071] Among them, B l =[cosα l sinα l ] T , and α l This refers to the antenna pointing of the l-th antenna. To unify polarization coordinates and array coordinates, the transformation matrix from the polarization coordinate system to the planar coordinate system for an array composed of short dipoles is:

[0072]

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

[0074]

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

[0076]

[0077] Step S3: Using the structure designed in step S1 above, high-precision quantization and low-precision quantization data are received, and the low-precision quantization data is processed according to the additive Gaussian noise model to obtain the received data under mixed resolution quantization; wherein, the selection of 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 HThese represent the number of low-resolution and high-resolution ADCs, respectively. The mixed quantized received signal is represented as:

[0081]

[0082] Where, x L (t) and x L (t) represents the signals to be quantized at low and high resolutions, respectively.

[0083] Since the data in the low-precision quantization channel conforms to the additive Gaussian noise model, this embodiment processes the low-resolution quantized signal using the additive quantization noise model to obtain a new low-resolution quantized received signal.

[0084]

[0085] in, Let w represent the quantization function. Q (t) represents the relationship with x L (t) Uncorrelated quantization noise. 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 a larger quantization bit depth (b>5), the loss factor can be approximated as:

[0088]

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

[0090]

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

[0092]

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

[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 data from the k received snapshots. covariance We perform eigenvalue decomposition, where the eigenvectors corresponding to the n largest eigenvalues ​​are called eigenvectors. The eigenvectors corresponding to the (mK) small eigenvalues ​​are called...

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

[0098] Specifically:

[0099] Spectral peak search is performed on the mixed-resolution quantized data to estimate DOA and polarization information. The two-dimensional region to be searched is divided into grid points. Based on the array information, the polarization-free portion of the steering vector corresponding to each grid point is calculated. The portion corresponding to the low-resolution quantized channel is then multiplied by the corresponding quantization factor α. The resulting portion is called DOA. θ,φ Calculate the corresponding value for each point The value is calculated, and the angle corresponding to the point with the largest value is the estimated DOA. Then, the corresponding polarization parameters are calculated according to the method of finding the optimal solution.

[0100] In step S2, we will determine the D values ​​at different grid points. θ,φ Represented as:

[0101] D θ,φ =ΓΥ θ,φ BΞ θ,φ

[0102] make Calculate each grid point The matrix is ​​then compared, and the angle corresponding to the smallest grid point is the estimated DOA. Figure 2 As shown in (a):

[0103]

[0104] After estimating the DOA information, the next step is to estimate the polarization information, using the Lagrange gradient method to obtain (H(θ)). k ,φ k )-λI)h γ,η The solution where = 0 is the estimated polarization information. Alternatively, a search method can also be used to estimate polarization information, such as... Figure 3As shown in (a).

[0105] Implementation Method 3, see below Figures 2 to 5 This embodiment describes a conformal array direction finding method based on hybrid resolution quantization proposed in the above embodiment, which is compared 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: such as Figure 2 and Figure 3 As shown, compared with full low-resolution quantization, the spatial spectrum and polarization spectrum peaks of the hybrid quantization structure proposed in this invention are significantly higher, and the estimation accuracy is improved.

[0107] Furthermore, statistical analysis was conducted through 2000 Monte Carlo experiments to obtain the root mean square error of angle measurement accuracy as a benchmark for comparison. The estimation accuracy under different bit quantization structures was compared, and the performance improvement and power consumption reduction brought by the hybrid resolution quantization structure were analyzed.

[0108] like Figure 4 As shown, the accuracy of parameter estimation improves with increasing signal-to-noise ratio (SNR). Hybrid resolution quantization outperforms full low-resolution quantization. At low SNR, the main error is caused by thermal noise, while at high SNR, quantization error dominates. RMSE increases with decreasing quantization bits, but the decrease in accuracy is minimal. Therefore, hybrid quantization can slightly sacrifice accuracy in exchange for reduced resource consumption.

[0109] like Figure 5 As shown, the choice of quantization antenna also affects the estimation results. This is because low-resolution quantization is used at antennas with high received amplitude, which increases quantization noise. Based on this characteristic, we can adjust the quantization structure to improve the estimation accuracy in the direction of interest.

[0110] Implementation Method 4: The conformal array direction finding method based on hybrid resolution quantization proposed in the above implementation methods can be fully implemented using computer software. Therefore, this implementation method proposes a conformal array direction finding system based on hybrid resolution quantization, the direction finding system comprising:

[0111] Storage devices used for designing conformal array structures;

[0112] A storage device for constructing models of the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and for obtaining the received data based on 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] Storage device for covariance decomposition of received data under mixed resolution quantization;

[0115] Storage device used to perform spectral peak search and estimate DOA and polarization information for mixed-resolution quantized data after covariance decomposition.

[0116] Implementation Method 5: This implementation method provides a computer-readable storage medium, 9. The computer-readable storage medium stores a computer program, which, when executed by a processor, performs a conformal array direction finding method based on hybrid resolution quantization as described in any of the above implementation methods.

[0117] Implementation Method Six: This implementation method provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a conformal array direction finding method based on hybrid resolution quantization as described in any one of the above implementation methods.

[0118] This embodiment provides a computer device, the hardware of which is a general-purpose model and is not shown in the figure. The system includes a processor and a memory, which can be connected by 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 executes various functional applications and data processing by running the non-transitory software programs, instructions and modules stored in the memory, so as to realize the conformal array direction finding method and steps based on hybrid resolution quantization in the above method embodiment.

[0119] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should 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 as follows: S1: Design of conformal array structure; S2: Construct models for the received signal from the receiving antenna and the transmitted signal from the transmitting antenna, and obtain the received data based on the transmitted signal model and the received signal model; S3: The structure designed in step S1 is used to receive high-precision quantization and low-precision quantization data, and the low-precision quantization data is processed according to the additive Gaussian noise model to obtain the received data under mixed resolution quantization. S4: Perform covariance decomposition on the received data under mixed resolution quantization; S5: Perform spectral peak search on the mixed resolution quantized data after covariance decomposition to estimate DOA and polarization information.

2. The conformal array direction finding method based on hybrid resolution quantization according to claim 1, characterized in that, S1 specifically refers to: Design the conformal array arrangement of m antennas, design the location of the low-resolution receiving channel, and design the number of low-resolution bits. 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 signals from the receiving antenna include the target signal S(t) and the noise signal n(t); The transmitted signal from the transmitting antenna includes the frequency, direction of arrival, and polarization parameters of the signal source.

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 follows: Among them, s k (t) represents the k-th narrowband signal. Let A represent the steering vector of the k-th signal, which is composed of the spatial response and polarization response, and let A be 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 represented as follows: Among them, A L and A H Let n represent the low-resolution and high-resolution signal steering vector matrices, respectively. L (t) and n H (t) represent low-resolution 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 specifically refers to: Calculate the covariance of the received k snapshots. 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 specifically refers to: The two-dimensional region to be searched is divided into grid points. Based on the array information, the polarization-free portion of the steering vector at each grid point is calculated. The corresponding low-resolution quantized channel is then multiplied by the corresponding quantization factor α to obtain D. θ,φ Calculate the corresponding value for each point The angle corresponding to the point with the largest value 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 includes a storage device for performing the method and steps of claim 1.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the conformal array direction finding method based on hybrid resolution quantization as described in any one of claims 1-7.

10. A computer device, characterized in that, The device includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a conformal array direction finding method based on hybrid resolution quantization as described in any one of claims 1-7.

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