A Deep Space Channel Estimation Method, Device, Computer Equipment and Storage Medium

Abstract

The present invention provides a deep space channel estimation method, apparatus, computer device and storage medium, belonging to the field of deep space communication. The method includes: constructing a deep space communication system model; performing initial channel estimation on the pilot signal transmitted by the deep space communication system model to obtain a noisy channel response; determining the mean square error expression of the noisy channel response based on the Savitzky-Golay filter; realizing dynamic optimization of the noisy channel response through the Stein unbiased risk estimator (SURE) equivalent channel response mean square error; traversing the parameter combinations of the filter window size and order, and selecting the target parameter combination that minimizes the SURE; and outputting the final channel estimation result through the Savitzky-Golay filter of the target parameter combination. This optimization process greatly reduces the computational difficulty and the complexity of noise optimization while ensuring the accuracy of channel estimation, and enhances the reliability of the entire deep space communication system.

Description

Technical Field

[0001] The present invention belongs to the field of deep - space communication, and particularly relates to a deep - space channel estimation method, device, computer device, and storage medium. Background Art

[0002] In recent years, with the successive development of multiple deep - space exploration missions, ensuring the reliability of the communication link has become one of the key factors for mission success. Compared with terrestrial communication, deep - space channel estimation faces the following challenges: First, due to the extremely long transmission distance of deep - space communication, the signal undergoes huge propagation losses, resulting in an extremely low signal - to - noise ratio (SNR) of the received signal. Under strong noise interference, it becomes extremely difficult to accurately estimate the channel information. Second, during solar conjunction, that is, when the sun is located between the earth and the detector, the signal is affected by the scattering effect caused by coronal solar wind turbulence. This phenomenon is called solar scintillation, which will distort the received signal and further reduce the channel estimation performance. In addition, due to the high - speed relative motion between planets, the deep - space channel is significantly affected by the Doppler frequency shift and exhibits strong time - selective fading characteristics. Different from the commonly used Jakes power spectrum on the ground, the deep - space channel presents a Gaussian spectrum under the influence of solar scintillation. Therefore, designing a channel estimation method that can adapt to the characteristics of the time - varying deep - space channel under solar scintillation and has strong anti - noise ability has become a technical problem in deep - space communication systems.

[0003] Existing channel estimation methods include the Least Squares (LS) method and the Minimum Mean Square Error (MMSE) method. The LS algorithm has received attention because of its simple implementation and low computational complexity, but it ignores the influence of noise and is difficult to achieve good estimation performance especially in deep - space with low SNR. To solve the noise problem, the MMSE algorithm effectively improves the estimation accuracy by introducing the channel and noise statistical characteristics. However, the MMSE algorithm depends on accurate channel prior information, resulting in high computational difficulty and complexity. Summary of the Invention

[0004] To solve the problem of excessive complexity in channel estimation in the prior art, the present invention provides a deep - space channel estimation method, device, computer device, and storage medium.

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

[0006] First, a deep - space channel estimation method is provided, and the method includes:

[0007] Construct a deep - space communication system model;

[0008] Perform initial channel estimation on the pilot signal transmitted by the deep - space communication system model to obtain a noisy channel response;

[0009] Construct a Savitzky-Golay filter and determine the mean square error expression of the noisy channel response based on the Savitzky-Golay filter;

[0010] Equivalently represent the mean square error of the noisy channel response through Stein's unbiased risk estimator (SURE) to obtain the SURE equivalent formula;

[0011] Traverse the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter, and select the target parameter combination that minimizes the SURE equivalent formula;

[0012] Denoise the noisy channel response by using the Savitzky-Golay filter with the target parameter combination, and output the final channel estimation result.

[0013] Optionally, the construction of the deep space communication system model includes:

[0014] Establish a deep space communication system model during solar conjunction, where the ground base station communicates point-to-point with a deep space probe;

[0015] Based on the solar scintillation effect, establish a Rice model for the deep space time-varying channel in the deep space communication system model. The Rice model includes a direct path component and a scattered path component modeled by Gaussian spectrum, and the specific formula is:

[0016] ;

[0017] where C is the deep space time-varying channel, C LOS and C NLOS represent the direct path component and the scattered path component respectively, K is the Rice factor, and the relationship between the Rice factor and the scintillation index m of the solar scintillation effect is .

[0018] Optionally, the pilot signal is expressed as:

[0019] ;

[0020] where, is the received pilot signal, represents a complex vector with dimension N ×1, N is the length of the signal, represents the transmitted pilot signal and , where (·) H represents conjugate transpose, I represents the identity matrix, represents the channel response of the deep space time-varying channel, and n represents complex Gaussian noise, , represents a complex Gaussian distribution with a mean of 0 and a variance of ;

[0021] The initial channel estimation of the received pilot signal includes:

[0022] Performing initial channel estimation on the received pilot signal using the least squares method LS. The specific formula is:

[0023] ;

[0024] where the vector represents the noisy channel response estimated by LS, is the equivalent noise, that is, .

[0025] Optionally, the mean square error of the equivalent channel response by the Stein unbiased risk estimator SURE includes:

[0026] Minimizing the mean square error of the channel response. The formula is:

[0027] ;

[0028] where represents the Savitzky-Golay filter with parameters ( M , Q ), N is the signal length, represents the result after denoising;

[0029] Equivalent the minimization of the mean square error of the channel response by SURE. The formula is:

[0030] ;

[0031] where, , is the variance of the noise, s i represents the i th element of the vector s, f (s i ) represents the result after denoising the i th element of the vector s, and ∞ represents infinity.

[0032] Optionally, the fitting formula of the Savitzky-Golay filter is:

[0033] ;

[0034] where, are the polynomial coefficients, k is the polynomial order, is the fitting result, x is the window size.

[0035] Secondly, a deep space channel estimation device is provided, and the device includes:

[0036] A construction module for constructing a deep space communication system model;

[0037] An estimation module for receiving the pilot signal transmitted by the deep space communication system model, performing initial channel estimation, and obtaining a noisy channel response;

[0038] A denoising module for constructing a Savitzky-Golay filter and determining the mean square error expression of the noisy channel response based on the Savitzky-Golay filter; equivalently obtaining the mean square error of the noisy channel response through the Stein unbiased risk estimator (SURE) to obtain the SURE equivalent formula; traversing the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter, and selecting the target parameter combination that minimizes the SURE equivalent formula; denoising by using the Savitzky-Golay filter with the target parameter combination, and outputting the final channel estimation result.

[0039] In addition, a computer-readable storage medium is provided, and the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned deep space channel estimation method is implemented.

[0040] Finally, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above-mentioned deep space channel estimation method is implemented.

[0041] A deep space channel estimation method provided by the present invention has the following beneficial effects:

[0042] The present invention uses a Savitzky-Golay filter to denoise the noisy channel response. This filter can smooth the signal based on local polynomial least squares fitting, thereby reducing noise while preserving the basic characteristics of the signal. It is combined with a method of equivalent channel response mean square error through Stein's unbiased estimation theory to achieve dynamic optimization of the noisy channel response. This method does not require the true value of the signal, but optimizes the filter parameters through an unbiased estimation of the MSE, thereby avoiding dependence on prior channel information, greatly reducing the computational difficulty of channel estimation and reducing the complexity of channel estimation. In addition, this dynamic optimization process can ensure that the filter can achieve the best noise reduction effect under different noise and channel conditions, with strong generalization. Finally, selecting the target parameter combination that minimizes the SURE can ensure that the Savitzky-Golay filter achieves the best filtering effect in a specific noise and channel environment. This optimization process greatly reduces the computational difficulty and the complexity of noise optimization while ensuring the accuracy of channel estimation, enhancing the reliability of the entire deep space communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the embodiments of the present invention and their design schemes, the accompanying drawings required for this embodiment will be briefly introduced below. The drawings in the following description are only partial 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.

[0044] Figure 1 It is a flowchart of a deep space channel estimation method provided by the present invention according to an exemplary embodiment.

[0045] Figure 2 It is a schematic diagram of a deep space communication system model provided by the present invention according to an exemplary embodiment.

[0046] Figure 3 It is a schematic diagram of the steps of a deep space channel estimation method based on adaptive Savitzky-Golay filtering provided by the present invention according to an exemplary embodiment.

[0047] Figure 4 It is a block diagram of a deep space channel estimation device provided by the present invention according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To enable those skilled in the art to better understand the technical solutions of the present invention and implement them, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0049] To solve the problems of existing channel estimation methods, the present invention transforms the channel estimation problem into a signal denoising problem and uses a Savitzky-Golay filter for denoising. At the same time, it combines Stein's Unbiased Risk Estimation (SURE) theory to optimize the parameter selection of the filter to achieve the best noise reduction effect.

[0050] The Savitzky-Golay filter is a widely used signal smoothing method that can effectively reduce noise. Instead of simply averaging the points in the window, the Savitzky-Golay filter is a filtering method based on local polynomial least squares fitting to smooth the signal. Therefore, compared with other smoothing filters, it is particularly advantageous in preserving the basic features of the input signal.

[0051] The following will, in conjunction with the accompanying drawings, elaborate on the technical solutions provided by each embodiment of the present invention.

[0052] First, the present invention provides a deep space channel estimation method, specifically as Figure 1 shown, including the following steps:

[0053] S101. Construct a deep space communication system model.

[0054] In this step, first establish a deep space communication system model during solar conjunction, as Figure 2 shown, where the ground base station communicates point-to-point with a deep space probe;

[0055] Model the deep space time-varying channel. Based on the solar scintillation effect, establish a Rice model for the deep space time-varying channel in this deep space communication system model. This Rice model includes a direct path component and a scattered path component modeled by Gaussian spectrum. The deep space channel h affected by solar scintillation can be represented by a Rice channel, and the specific formula is:

[0056] ;

[0057] where C is the deep space time-varying channel, C LOS and C NLOS represent the direct path component and the scattered path component respectively, K is the Rice factor, and the relationship between the Rice factor and the scintillation index m of the solar scintillation effect is .

[0058] Model the time-varying characteristics of C NLOS using Gaussian spectrum. The Gaussian power spectral density is:

[0059] ;

[0060] where f is the frequency,f d is the maximum Doppler shift, and its calculation formula is:

[0061] ;

[0062] where c is the speed of light, v is the relative velocity, f c is the carrier frequency. Performing the inverse Fourier transform on the power spectral density, the autocorrelation function of the scattering component is obtained as:

[0063] ;

[0064] where τ is the sampling interval.

[0065] Model the time-varying characteristics of the direct path using a cosine signal. The autocorrelation of the cosine signal is: LOS

[0066] .

[0067] Based on the above modeling formulas of C NLOS and C LOS , the autocorrelation function of the deep space time-varying channel C( t ) can be written as:

[0068] .

[0069] S102. Perform initial channel estimation on the pilot signal transmitted by the deep space communication system model to obtain the noisy channel response.

[0070] Transmit the pilot signal according to the established communication system model and channel model. The received pilot signal can be expressed as:

[0071] ;

[0072] where is the received pilot signal, represents a complex vector of dimension N×1, where N is the length of the signal, represents the transmitted pilot signal and , where (·)H represents the conjugate transpose, I represents the identity matrix, represents the channel response of the deep space time-varying channel, n represents complex Gaussian noise, , represents a complex Gaussian distribution with a mean of 0 and a variance of .

[0073] Performing the initial channel estimation on the received pilot signal includes:​

[0074] The received pilot signal is initially channel - estimated using the least - squares (LS) method. The specific formula is as follows:

[0075] ;

[0076] Among them, the vector represents the noisy channel response estimated by LS, is the equivalent noise, that is .

[0077] S103. Construct a Savitzky - Golay filter and determine the mean - square error expression of the noisy channel response based on the Savitzky - Golay filter.

[0078] Based on the above initial channel - estimation formula, the channel - estimation problem can be transformed into a signal denoising problem. In this step, a Savitzky - Golay filter with a window size of 2 M +1 and a polynomial order of Q is used to filter the result of the LS estimation. Among them, the fitting formula of the Savitzky - Golay filter is:

[0079] ;

[0080] Among them, are the polynomial coefficients, k is the polynomial order, is the fitting result, x is the window size.

[0081] By stacking into a vector, the above formula can be rewritten in matrix form:

[0082] ;

[0083] Among them, A is the matrix , and a is the vector .

[0084] In this step, the least - squares method is used to minimize the sum of the squared residuals of the data in the window. The loss function J is:

[0085] ;

[0086] Taking the derivative of the polynomial coefficients and setting it to zero , the coefficient vector can be obtained:

[0087] ;

[0088] At this time, the optimization problem is to minimize the mean square error MSE. First, determine the mean square error of the noisy channel response, and then perform minimization. The specific formula is:

[0089] ;

[0090] where represents the Savitzky-Golay filter with parameters ([[]] M , Q ), and represents the result after denoising.

[0091] S104. Equivalent the mean square error of the noisy channel response through the Stein unbiased risk estimator (SURE) to obtain the SURE equivalent formula; traverse the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter, and select the target parameter combination that minimizes the SURE equivalent formula.

[0092] In an actual communication system, since the true value h of the signal in the mean square error of the above-mentioned noisy channel response cannot be obtained, the optimization of Problem P1 cannot be completed.

[0093] To solve this optimization problem, the present invention introduces the Stein unbiased risk estimator (SURE). According to the Stein unbiased risk estimator theory, it can be known that: . SURE is an unbiased estimator of MSE. Therefore, the P1 problem is equivalent through SURE, and the formula is:

[0094] ;

[0095] where , is the variance of the noise, s i represents the i -th element of the vector s, f (s i ) represents the denoised result of the

[0096] -th element of the vector s, and ∞ represents infinity. M and Q After determining the mean square error formula of the noisy channel response, a grid search method is used for optimization. Set the maximum values of M max and Q max , traverse all ([[]] M , Q ) combinations, and select the target parameter combination corresponding to the minimum SURE value as the optimal Savitzky-Golay filter parameter ([[]] M opt , Qopt ) The pseudo-code is shown in Table 1 below:

[0097] Table 1: M and Q The traversal pseudo-code of

[0098]

[0099] The specific interpretation of this pseudo-code is as follows:

[0100] Initialization: Set M = 0, Q = 0, and let SUREmin be .

[0101] Traverse all combinations of M and Q and calculate .

[0102] When SURE is less than SURE min , update the values of M and Q .

[0103] Select the combination of M and Q corresponding to the minimum SURE to obtain M opt , Q opt .

[0104] S105. Denoise the noisy channel response by using the Savitzky-Golay filter with the target parameter combination, and output the final channel estimation result.

[0105] Use the target parameter combination as the optimal Savitzky-Golay filter parameters for denoising, and finally obtain the channel estimation result:

[0106] ;

[0107] Among them, is the channel estimation result.

[0108] In one embodiment, based on the above steps, the present invention constructs a deep space channel estimation method based on adaptive Savitzky-Golay filtering, as shown in Figure 3 and includes:

[0109] S1. Construct a deep space time-varying channel.

[0110] Construct a deep space time-varying channel based on the deep space communication system model.

[0111] S2. Transmit a pilot signal.

[0112] S3. LS estimation.

[0113] Perform initial channel estimation through LS estimation to obtain a noisy channel response.

[0114] S4. Savitzky-Golay filtering.

[0115] S5. Minimize SURE.

[0116] S6. Grid search optimization.

[0117] Traverse the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter through grid search, and select the target parameter combination that minimizes SURE.

[0118] S7. Channel estimation result.

[0119] Denoise the noisy channel response by using the Savitzky-Golay filter with the target parameters.

[0120] The specific implementation method of each step is as described above Figure 1 specific content.

[0121] Using the above method, the Savitzky-Golay filter is used to denoise the noisy channel response. This filter can smooth the signal based on local polynomial least squares fitting, thereby reducing noise while retaining the basic characteristics of the signal. Combined with the method of equivalent channel response mean square error through Stein's unbiased estimation theory, it realizes the dynamic optimization of the noisy channel response. This method does not require the true value of the signal, but optimizes the parameters of the filter through the unbiased estimation of MSE, thereby avoiding the dependence on channel prior information, greatly reducing the computational difficulty of channel estimation and reducing the complexity of channel estimation. In addition, this dynamic optimization process can ensure that the filter can achieve the best noise reduction effect under different noise and channel conditions, with strong generalization. Finally, selecting the target parameter combination that minimizes SURE can ensure that the Savitzky-Golay filter achieves the best filtering effect in a specific noise and channel environment. This optimization process greatly reduces the computational difficulty and the complexity of noise optimization while ensuring the accuracy of channel estimation, enhancing the reliability of the entire deep space communication system.

[0122] Secondly, the present invention also provides a deep space channel estimation device, as Figure 4 shown, including:

[0123] A construction module 401 for constructing a deep space communication system model;

[0124] An estimation module 402, configured to receive a pilot signal transmitted by the deep space communication system model, perform initial channel estimation, and obtain a noisy channel response;

[0125] A denoising module 403, configured to construct a Savitzky-Golay filter, and determine a mean square error expression of the noisy channel response based on the Savitzky-Golay filter; equivalently obtain a mean square error of the noisy channel response through the Stein's unbiased risk estimate (SURE) theory to obtain a SURE equivalent formula; traverse parameter combinations of a window size parameter and a polynomial order of the Savitzky-Golay filter, and select a target parameter combination that minimizes the SURE equivalent formula; denoise the noisy channel response by using the Savitzky-Golay filter adopting the target parameter combination, and output a final channel estimation result.

[0126] By adopting the above device, denoising processing is performed on the noisy channel response through a Savitzky-Golay filter, which can smooth a signal based on local polynomial least squares fitting, thereby reducing noise while retaining basic features of the signal, and combining with a method of equivalently obtaining a mean square error of a channel response through the Stein's unbiased risk estimate theory, realizing dynamic optimization of the noisy channel response. This method does not require the true value of a signal, but optimizes parameters of the filter through an unbiased estimate of the mean square error (MSE), thereby avoiding dependence on prior channel information, greatly reducing the computational difficulty of channel estimation and reducing the complexity of channel estimation. In addition, this dynamic optimization process can ensure that the filter can achieve an optimal noise reduction effect under different noise and channel conditions, with strong generalization ability. Finally, selecting a target parameter combination that minimizes the SURE can ensure that the Savitzky-Golay filter achieves an optimal filtering effect in a specific noise and channel environment. This optimization process greatly reduces the computational difficulty and the complexity of noise optimization while ensuring the accuracy of channel estimation, and enhances the reliability of the entire deep space communication system.

[0127] The present invention further provides a computer-readable storage medium storing a computer program, and the computer program can be used to execute the steps of the deep space channel estimation method provided above Figure 1 The present invention further provides a computer device. At a hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may of course further include hardware required for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs it to implement the steps of the deep space channel estimation method provided above

[0128] Figure 1 The present invention further provides a computer device. At a hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may of course further include hardware required for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs it to implement the steps of the deep space channel estimation method provided above

[0129] ​Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0130] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0131] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0133] It should be noted that the above specific implementation manner can enable those skilled in the art to understand the present invention and creation more comprehensively, but does not limit the present invention and creation in any way. Therefore, although this specification has described the present invention and creation in detail, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention and creation; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention and creation are covered by the protection scope of the patent of the present invention and creation. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A deep space channel estimation method, characterized in that, The method includes: Constructing a deep space communication system model; Performing initial channel estimation on the pilot signal transmitted by the deep space communication system model to obtain a noisy channel response; Constructing a Savitzky-Golay filter and determining the mean square error expression of the noisy channel response based on the Savitzky-Golay filter; Equivalent the mean square error of the noisy channel response through Stein's unbiased risk estimator (SURE) to obtain a SURE equivalent formula; Traversing the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter, and selecting the target parameter combination that minimizes the SURE equivalent formula; Denosing the noisy channel response by using the Savitzky-Golay filter with the target parameter combination, and outputting the final channel estimation result; The deep space communication system model includes a deep space time-varying channel. Modeling the deep space time-varying channel includes: Based on the solar scintillation effect, establishing a Rice model for the deep space time-varying channel in the deep space communication system model. The Rice model includes a direct path component and a scattered path component modeled by a Gaussian spectrum. The specific formula is: ； Among them, C is a deep-space time-varying channel, C LOS and C NLOS represent the direct-path component and the scattered-path component respectively, K is the Rice factor, and the relationship between the Rice factor and the scintillation index of the solar scintillation effect m is ; Performing initial channel estimation on the pilot signal includes: Using the least squares (LS) method to perform initial channel estimation on the pilot signal; The equivalent of the mean square error of the noisy channel response through Stein's unbiased risk estimator (SURE) includes: Minimizing the mean square error of the channel response, the formula is: ； where denotes the Savitzky-Golay filter with parameters ( M , Q ), M and Q denote the parameters of the window size parameter and the polynomial order respectively, N is the signal length, the vector denotes the noisy channel response of the LS estimate, denotes the result after denoising, is the channel response; Equivalent the minimization of the mean square error of the channel response through SURE, the formula is: ； Among them, , is the variance of the noise, and s i represents the i -th element of the vector s. f (s i ) represents the denoising result of the i -th element of the vector s, and ∞ represents infinity.

2. The method for deep space channel estimation according to claim 1, wherein The pilot signal is expressed as: ； Among them, is the received pilot signal, represents a complex vector of dimension N ×1, N is the length of the signal, represents the transmitted pilot signal and , where (·) H represents conjugate transpose, I represents the identity matrix, represents the channel response of the deep space time-varying channel, n represents complex Gaussian noise, , represents a complex Gaussian distribution with a mean of 0 and a variance of ; Performing initial channel estimation on the received pilot signal, the specific formula is: ； where the vector represents the noisy channel response estimated by LS, is the equivalent noise, i.e., .

3. A deep space channel estimation method according to claim 1, characterized in that, The fitting formula of the Savitzky-Golay filter is: ； Among them, is the polynomial coefficient, k is the polynomial order, is the fitting result, x is the window size.

4. A deep space channel estimation device, characterized in that The device includes: A construction module for constructing a deep space communication system model; An estimation module for receiving the pilot signal transmitted by the deep space communication system model, performing initial channel estimation, and obtaining a noisy channel response; A denoising module for constructing a Savitzky-Golay filter and determining the mean square error expression of the noisy channel response based on the Savitzky-Golay filter; equivalent the mean square error of the noisy channel response through Stein's unbiased risk estimator (SURE) to obtain a SURE equivalent formula; traversing the parameter combinations of the window size parameter and the polynomial order of the Savitzky-Golay filter, and selecting the target parameter combination that minimizes the SURE equivalent formula; denosing the noisy channel response by using the Savitzky-Golay filter with the target parameter combination, and outputting the final channel estimation result; The deep space communication system model includes a deep space time-varying channel. The construction module is further configured to model the deep space time-varying channel, including: Based on the solar scintillation effect, establishing a Rice model for the deep space time-varying channel in the deep space communication system model. The Rice model includes a direct path component and a scattered path component modeled by a Gaussian spectrum. The specific formula is: ； Among them, C is the deep space time-varying channel, C LOS and C NLOS represent the direct path component and the scattered path component respectively, K is the Rice factor, and the relationship between the Rice factor and the scintillation index m of the solar scintillation effect is ; The estimation module is further configured to use the least squares (LS) method to perform initial channel estimation on the pilot signal; The denoising module is further configured to minimize the mean square error of the channel response, the formula is: ； where denotes the Savitzky-Golay filter when the parameters are ( M , Q ), M and Q denote the parameters of the window size parameter and the polynomial order respectively, N is the signal length, the vector denotes the noisy channel response of the LS estimation, denotes the result after denoising, is the channel response; Equivalent to minimizing the mean square error of the channel response through SURE, the formula is: ； Among them, , is the variance of the noise, s i represents the i -th element of the vector s, f (s i ) represents the denoising result of the i -th element of the vector s, and ∞ represents infinity.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above claims 1 to 3 is implemented.

6. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any one of the above claims 1 to 3 is implemented.

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