Low Dry-Noise Ratio Interference Detection Method and Location Method Based on Multi-Beam Dual-Star System

Abstract

The present invention discloses a low dry-to-noise ratio interference detection method and a positioning method based on a multi-beam double-star system, including: determining the interference frequency according to the main satellite interference, and establishing an interference model of the main satellite interference and the secondary satellite interference based on this; performing matched filtering on the interference forwarded by the two multi-beam satellites to the ground receiving station to improve the dry-to-noise ratio between the interferences; performing wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determining a threshold, performing threshold processing on the wavelet coefficients to remove the noise coefficients and retain the interference coefficients. Using the retained interference coefficients for wavelet reconstruction to obtain the denoised interference. The present invention breaks through the double-star positioning limitation condition that the double stars need to jointly cover the interference source, and can realize the positioning of the interference source in a vast area, providing an effective tool for the positioning and troubleshooting of the interference source in the multi-beam satellite communication system.

Description

Technical Field

[0001] The present invention belongs to the field of satellite communication, and particularly relates to a low carrier-to-interference-plus-noise ratio (CINR) interference detection method and a positioning method based on a multi-beam dual-satellite system. Background Art

[0002] Geostationary Earth Orbit (GEO) communication satellites use multi-beam coverage to improve coverage gain and communication capacity. For example, Inmarsat-4 satellites use 19 wide spot beams and 228 narrow spot beams. Spaceborne multi-beam antenna technology enables a satellite to cover a larger area with higher gain and better directivity by simultaneously transmitting beams in multiple directions. However, multi-beam satellites are more vulnerable to interference threats due to their high beam gain.

[0003] Therefore, it is necessary to quickly and accurately locate the interference source. Chinese Patent No. CN 110045327 A discloses a single-satellite interference source positioning method based on multiple co-frequency reuse beams, but its positioning accuracy is affected by factors such as unknown or changing beam characteristics, resulting in poor positioning performance. Currently, the dual-satellite time-frequency difference joint positioning method is more commonly used for interference source positioning. It accurately locates interference signals by measuring the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) of the same interference source at two satellites. This method can achieve high-precision positioning with a relatively small number of required satellites.

[0004] However, when applying this method to the interference positioning of multi-beam satellites, there are important technical problems. For example, in TDOA / FDOA joint dual-satellite positioning, the interference source needs to be located within the dual-satellite area, but two multi-beam satellites usually cover different regions without a common coverage area.

[0005] Moreover, the co-frequency multi-beams of different satellites are highly isolated. When interference in the main satellite's spot beam arrives at the co-frequency beam of another auxiliary satellite for reception, it experiences significant attenuation due to beam isolation, resulting in a negative CINR at the auxiliary satellite. Currently, the mainstream estimation methods for dual-satellite time-frequency difference parameters, including the generalized correlation method, the cross ambiguity function method, and the fourth-order cumulant method, can provide relatively accurate parameter estimation only when the CINR is high (at least 5 dB). The generalized correlation method and the cross ambiguity function method have large errors at low CINR. Although the fourth-order cumulant method has slightly better anti-noise performance and can maintain good time-frequency difference estimation at -5 dB, it also has large errors when the CINR continues to decrease. When the CINR drops to -30 dB, the above methods cannot effectively estimate the time-frequency difference parameters, preventing multi-beam dual-satellites from applying the dual-satellite time-frequency difference joint positioning method for interference source positioning. Summary of the Invention

[0006] The present invention aims to at least partly solve one of the technical problems existing in the related art.

[0007] An object of the present invention is to provide a method for detecting low carrier-to-noise ratio interference based on multi-beam dual satellites. By virtue of the characteristic of high carrier-to-noise ratio of the main satellite, the interference frequency and bandwidth are determined according to the main satellite interference, and based on this, the interference submerged below the noise spectrum received by the secondary satellite is detected.

[0008] Another object of the present invention is to provide a method for locating low carrier-to-noise ratio interference based on a multi-beam dual-satellite system, which breaks through the dual-satellite positioning limit condition that the two satellites need to jointly cover the interference source, and can locate the interference source in a vast area, providing an effective tool for the location and troubleshooting of interference sources in multi-beam satellite communication systems.

[0009] To achieve the above object, on the one hand, the present invention provides a method for detecting low carrier-to-noise ratio interference based on a multi-beam dual-satellite system. The multi-beam dual-satellite system includes two multi-beam satellites, an interference source, and a ground receiving station; among the two multi-beam satellites, one is the main satellite and the other is the secondary satellite. The interference source is located within the range of one beam of the main satellite and within the sidelobe suppression range of the secondary satellite beam. The interference is relayed to the ground receiving station through the two multi-beam satellites.

[0010] The interference detection method includes:

[0011] S100. Determine the interference frequency according to the main satellite interference, and establish an interference model between the main satellite interference and the secondary satellite interference accordingly;

[0012] S200. Perform matched filtering on the interference relayed to the ground receiving station by the two multi-beam satellites to improve the carrier-to-noise ratio between the interferences;

[0013] S300. Perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine a threshold, perform threshold processing on the wavelet coefficients, remove the noise coefficients, and retain the interference coefficients.

[0014] S400. Use the retained interference coefficients for wavelet reconstruction to obtain the denoised interference;

[0015] A further preferred technical solution of the present invention is that in step S100, the interference frequency is determined according to the main satellite interference, and an interference model between the main satellite interference and the secondary satellite interference is established. The specific method is:

[0016] S110. The ground receiving station selects two multi-beam satellites to receive the interference emitted by the interference source and relayed by the two multi-beam satellites;

[0017] S120. Determine the main satellite, and determine the interference frequency according to the interference relayed by the main satellite;

[0018] S130. Perform a fast Fourier transform on the forwarding interference, perform a frequency transformation according to the determined interference frequency to obtain the original non-offset interference, and establish an interference model, which is expressed as:

[0019]

[0020] Among them, and are respectively the interference models of the interference from the interference source being forwarded to the ground receiving station by two multi-beam satellites. s(t) is the interference signal without noise emitted by the interference source, and t is the time variable, representing the signal variation with time; and are respectively the arrival times of the interference from the interference source being forwarded to the ground receiving station by two multi-beam satellites, is the natural constant, and j is the imaginary unit; and are respectively the arrival frequencies of the interference from the interference source being forwarded to the ground receiving station by two multi-beam satellites; and respectively represent the Gaussian white noise interference of the interference from the interference source being forwarded to the ground receiving station by two multi-beam satellites, and the interference and the noise are uncorrelated;

[0021] is the complex gain (including amplitude and phase information) of the i-th beam, and its calculation formula is:

[0022]

[0023] and are respectively the excitation amplitude and phase of the unit antenna, is the pattern function of the unit antenna, is the multi-beam satellite beam with different numbers, n is the n-th unit in the antenna array, is the phase change amount within the unit length, and respectively represent the elevation angle and azimuth angle of the antenna;

[0024] S140. Perform a normalization process on the interference model, and represent the interference forwarded by the two multi-beam satellites to the ground receiving station as:

[0025]

[0026] Among them, is the relative amplitude value of the interference received by the ground receiving station from the two multi-beam satellites, ; represents the time difference of the interference received by the ground receiving station from the two multi-beam satellites, ; It represents the frequency difference of the interference relayed by two multi-beam satellites received by the ground receiving station. .

[0027] Preferably, in step S200, the interference relayed by two multi-beam satellites to the ground receiving station is subjected to matched filtering. The specific method is as follows:

[0028] S210. Establish a matched filter, which is expressed as:

[0029]

[0030] Where, represents the known transmitted interference, is the time-domain impulse response, represents zero-mean stationary additive Gaussian white noise with a power spectral density of ; represents the power spectral density, is the power spectral density value of the additive Gaussian white noise within the frequency range;

[0031] S220. Substitute the interference models , into the matched filter, which is expressed as:

[0032]

[0033] Where, , are the output signals of the signals and after passing through the impulse response respectively, is the time offset, is the impulse response of the system.

[0034] Preferably, the specific method of step S300 is as follows:

[0035] S310. Decompose the interference to be processed into sub-band interferences of different scales and frequencies through wavelet transform, which is expressed as:

[0036]

[0037] Where, is the wavelet coefficient of each layer after the wavelet transform of the noise-containing interference , ; is the approximation coefficient, which is the wavelet transform coefficient of the effective part of the interference, is the detail coefficient, which is the wavelet transform coefficient containing the interference noise part; is the scale parameter, representing the number of wavelet decomposition layers, is the position offset of the wavelet function on the time axis, used to locate the local features of the signal;

[0038] S320. Determine the threshold. The threshold calculation formula is:

[0039]

[0040] where, is the length of the original signal, is the number of wavelet coefficients of the

[0041] S330. Perform threshold processing on the interference detail coefficients, set the detail coefficients smaller than a certain threshold to zero, and remove the influence of noise.

[0042] Preferably, in step S400, the approximated coefficients and detail coefficients after threshold processing are reconstructed through inverse wavelet transform to obtain the denoised interference.

[0043] On the other hand, the present invention provides a method for locating low carrier-to-interference-plus-noise ratio interference based on a multi-beam dual-satellite system, including:

[0044] S500. Substitute the reconstructed interference obtained by the above interference detection method into the cross ambiguity function for time-frequency difference parameter estimation;

[0045] S600. Use the time-frequency difference parameter estimation value, as well as the positions and velocities of the corresponding two selected satellites, to locate the interference source through the positioning calculation equations.

[0046] Further, the cross ambiguity function in step S500 is expressed as:

[0047]

[0048] where, and are the main satellite and secondary satellite retransmitted interferences received, represents the complex conjugate, is the Doppler frequency shift variable, is the total detection time.

[0049] Further, the method for time-frequency difference parameter estimation in step S500 is:

[0050] is take the absolute value , and the absolute value reaches the peak at the true time delay Doppler frequency shift. According to the peak position of, obtain the time delay and the Doppler frequency shift estimation values, expressed as:

[0051]

[0052] In the formula, represents the time delay and Doppler frequency shift estimation values corresponding to when the maximum value is obtained.

[0053] Furthermore, the positioning and settlement equation set in step S600 is expressed as:

[0054]

[0055] wherein, represents the position of the unknown interference source, c is the propagation speed of electromagnetic waves in vacuum, is the time difference of arrival of the interference of the interference source measured by two satellites to the two satellites, which includes the delay error caused by various errors; is the difference in arrival frequencies of the interference of the interference source measured by two satellites to the two satellites, is the frequency of the interference of the interference source, is the semi-major axis of the Earth, is the semi-minor axis of the Earth, is the satellite position, is the distance from the two satellites to the interference source, is the speed of the satellite.

[0056] On the other hand, the present invention provides a non-transitory computer-readable storage medium, on which computer instructions are stored, and the computer instructions enable a computer to execute the above-mentioned low dry signal-to-noise ratio interference positioning method based on a multi-beam double-satellite system.

[0057] On another aspect, the present invention provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus, and the processor calls the logical instructions in the memory to execute the above-mentioned low dry signal-to-noise ratio interference positioning method based on a multi-beam double-satellite system.

[0058] On yet another aspect, the present invention provides a computer program product, the computer program product includes a computer program, the computer program is stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer executes the above-mentioned low dry signal-to-noise ratio interference positioning method based on a multi-beam double-satellite system.

[0059] Beneficial effects: In view of the problem in the multi-beam satellite system that the main satellite interference source is outside the coverage area of the secondary satellite, and the interference entering the secondary satellite from the sidelobe outside the coverage area is lower than the noise power and difficult to detect, the present invention uses matched filtering to improve the interference-to-noise ratio between interferences. After obtaining wavelet coefficients at all levels and the approximate coefficient at the lowest level through wavelet coefficient decomposition method, through threshold determination, non-significant coefficients are discarded, and a modified set of detail coefficients is output for interference reconstruction to restore the interference.

[0060] The present invention maximally restores the interference with extremely low interference-to-noise ratio of the secondary satellite, calculates the time-frequency difference positioning parameters through the cross-ambiguity function with the main satellite interference, and effectively improves the estimation accuracy of the positioning parameters. Finally, through the dual-satellite time-frequency difference positioning algorithm, the precise positioning of the interference is obtained. The present invention breaks through the dual-satellite positioning limit condition that the two satellites need to jointly cover the interference source, and can realize the positioning of the interference source in a wide area, providing an effective tool for the positioning and troubleshooting of interference sources in multi-beam satellite communication systems. Brief Description of the Drawings

[0061] Figure 1 It is a model diagram of the multi-beam dual-satellite system in the present invention.

[0062] Figure 2 It is a flowchart of the method for detecting and positioning low interference-to-noise ratio based on the multi-beam dual-satellite system in the present invention;

[0063] Figure 3 It is a diagram of the cross-ambiguity function estimation result when the interference is not processed in the simulation experiment of Embodiment 1;

[0064] Figure 4 It is a diagram of the cross-ambiguity function estimation result after processing the interference in the simulation experiment of Embodiment 1;

[0065] Figure 5 It is a diagram comparing the time difference parameters of various algorithms at different interference-to-noise ratios in the simulation experiment of Embodiment 1;

[0066] Figure 6 It is a diagram comparing the frequency difference parameters of various algorithms at different interference-to-noise ratios in the simulation experiment of Embodiment 1;

[0067] Figure 7 It is a GDOP simulation diagram of Embodiment 1;

[0068] Figure 7 In (a), it is a simulation diagram of GDOP simulation with the time-frequency difference parameters obtained without being processed by the above method, Figure 7 In (b), it is a simulation diagram of GDOP simulation with the time-frequency difference parameters obtained after being processed by the matched filtering-hard threshold method in the method of Embodiment 1. Detailed Embodiment

[0069] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention, and they should not be construed as limiting the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts belong to the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for the purpose of description and cannot be construed as indicating or implying relative importance.

[0070] The following combines Figures 1 - 7 to describe the low dry-noise ratio interference detection method and positioning method provided by the present invention based on a multi-beam dual-satellite system.

[0071] Embodiment 1: This embodiment provides a low dry-noise ratio interference detection method and positioning method based on a multi-beam dual-satellite system.

[0072] The multi-beam dual-satellite system is as Figure 1 shown, including two multi-beam satellites, an interference source and a ground receiving station; among the two multi-beam satellites, one is the main satellite and the other is the secondary satellite. The interference source is located within the range of one beam of the main satellite and within the sidelobe suppression range of the secondary satellite beam. The interference is relayed by the two multi-beam satellites to the interference detection and positioning server. In this embodiment, taking the ground receiving station at the gateway station as an example, a feeder interference receiver and an interference positioning server are provided. The interference is relayed by the multi-beam satellite to the gateway station for subsequent interference processing and satellite positioning process.

[0073] The specific steps are as Figure 2 shown, including:

[0074] S100. Determine the interference frequency according to the main satellite interference, and establish an interference model for the main satellite interference and the secondary satellite interference accordingly.

[0075] S110. The interference of the interference source is transmitted into the multi-beam satellite, and the interference model is expressed as:

[0076]

[0077] S120. The ground receiving station selects two multi-beam satellites, determines the main satellite, and determines the interference frequency according to the interference relayed by the main satellite;

[0078] S130. Perform a fast Fourier transform on the relayed interference, perform a frequency transformation according to the determined interference frequency to obtain the original unshifted interference, and establish an interference model, which is expressed as:

[0079]

[0080] where, and are interference models of interference sources interfering with signals relayed by two multi-beam satellites to a ground receiving station. s(t) is the interference signal without noise emitted by the interference source, and t is the time variable, representing the change of the signal with respect to time; and are the arrival times of the interference from the interference source relayed by two multi-beam satellites to the ground receiving station, is the natural constant, and j is the imaginary unit. and are the arrival frequencies of the interference from the interference source relayed by two multi-beam satellites to the ground receiving station respectively; and represent the Gaussian white noise interference of the interference from the interference source relayed by two multi-beam satellites to the ground receiving station respectively, and the interference and noise are uncorrelated; is the complex gain (including amplitude and phase information) of the i-th beam, and its calculation formula is:

[0081]

[0082] and are the excitation amplitude and phase of the element antenna respectively, is the pattern function of the element antenna, is the multi-beam satellite beam with different numbers, and n is the n-th element in the antenna array, is the phase change amount within the element length, and represent the elevation angle and azimuth angle of the antenna respectively.

[0083] S140. Normalize the interference model, and represent the interference relayed by two multi-beam satellites to the ground receiving station as:

[0084]

[0085] where, is the relative amplitude value of the interference received by the ground receiving station from the two multi-beam satellites, ; represents the time difference of the interference received by the ground receiving station from the two multi-beam satellites, ; represents the frequency difference of the interference received by the ground receiving station from the two multi-beam satellites, .

[0086] S200. Perform matched filtering on the interference relayed by two multi-beam satellites to the ground receiving station to improve the interference-to-noise ratio between interferences.

[0087] Matched filter (MF) is an "optimal" linear filter based on the maximum output interference-to-noise ratio criterion. Its transfer function when the output interference-to-noise ratio reaches the maximum is: , the time domain impulse response is The filter input is the transmitted interference Interference with noise of superposition.

[0088] S210, establish a matched filter, expressed as:

[0089]

[0090] in, Indicates known emission interference, is the time domain impulse response, The power spectral density is Zero-mean stationary additive Gaussian white noise; represents the power spectral density, is the power spectral density value of additive Gaussian white noise in the frequency range.

[0091] S220, according to the above interference model, the interference , Substituting into the matched filter we get:

[0092]

[0093] S230, further changing the above formula to obtain:

[0094]

[0095] S240, the interference model , Substituting into the matched filter, it is expressed as:

[0096]

[0097] in, , Signal and The output signal after the impulse response is is the time offset, is the impulse response of the system.

[0098] S300, perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine the threshold, perform threshold processing on the wavelet coefficients, remove the noise coefficient, and retain the interference coefficient. Use wavelet denoising to eliminate noise interference as much as possible The impact of retaining effective interference 。

[0099] S310. Decompose the interference to be processed into sub-band interferences of different scales and frequencies through wavelet transform, expressed as:

[0100]

[0101] Where, is the interference with noise 、 Wavelet coefficients of each layer after wavelet transform; is the approximation coefficient, which is the wavelet transform coefficient of the effective part of the interference, is the detail coefficient, which is the wavelet transform coefficient containing the interference noise part; is the scale parameter, representing the number of wavelet decomposition layers, is the position offset of the wavelet function on the time axis, used to locate the local characteristics of the signal;

[0102] S320. Determine the threshold. The threshold calculation formula is:

[0103]

[0104] Where, is the length of the original signal, is the Number of wavelet coefficients of the layer;

[0105] S330. Perform threshold processing on the interference detail coefficients, set the detail coefficients less than a certain threshold to zero to remove the influence of noise. There are different processing methods for the detail coefficients greater than the threshold, which are divided into hard threshold and soft threshold processing, as follows:

[0106]

[0107]

[0108] Where sgn is the sign function.

[0109] S400. Reconstruct the approximation coefficient and detail coefficient after threshold processing through inverse wavelet transform to obtain the denoised interference.

[0110] S500. Substitute the denoised interference into the cross ambiguity function for time-frequency difference parameter estimation;

[0111] The cross ambiguity function, expressed as:

[0112]

[0113] Where, and Forward the interference received from the primary satellite and the secondary satellite Denoted as the complex conjugate Is the Doppler frequency shift variable Is the total detection time

[0114] Is Take the absolute value , and the absolute value reaches the peak at the true time delay Doppler frequency shift. According to The peak position of, the time delay And the Doppler frequency shift Estimated values, denoted as:

[0115]

[0116] In the formula Denotes The estimated values of the time delay and Doppler frequency shift corresponding to when the maximum value is obtained

[0117] In order to test the improvement of the time-frequency difference performance of the burst radiation source signal by the time-frequency difference estimation scheme of the algorithm proposed in the text, the estimated value of the time-frequency difference And the actual time-frequency difference information The error can be measured by the Root Mean Squared Error (RMSE), and its definition is as follows:

[0118]

[0119] Where Is the number of Monte Carlo times of the simulation experiment, here take .

[0120] S600. Use the estimated values of the time-frequency difference parameters, and select the positions and speeds of the corresponding two satellites. Locate the interference source through the positioning calculation equations

[0121] The positioning calculation equations are denoted as:

[0122]

[0123] Among them Represents the position of the unknown interference source, c is the speed of electromagnetic wave propagation in vacuum Is the arrival time difference of the interference of the interference source measured by the two satellites to the two satellites, which includes the delay error caused by various errors; Is the arrival frequency difference of the interference of the interference source measured by the two satellites to the two satellites Is the frequency of the interference of the interference source Is the semi-major axis of the earth Is the semi-minor axis of the earth Is the satellite position are the distances from two satellites to the interference source, is the speed of the satellite.

[0124] The method of this embodiment is verified by simulation as follows:

[0125] Through Matlab simulation, interference conforming to the above multi-beam dual-satellite system is generated. The sampling rate is 10 MHz, the time difference is set to 4.6 μs, the frequency difference is set to 10.2 Hz, the main satellite interference-to-noise ratio is 5 dB, and the secondary satellite signal-to-noise ratio is -30 dB.

[0126] Figure 3 is the cross ambiguity function estimation result graph without passing through the above method, Figure 4 is the cross ambiguity function estimation result graph after processing by the matching filtering-hard threshold method in the above method. Through the improvement of the method of the present invention, the estimation of the interference time-frequency difference parameters has been greatly improved, and the time-frequency difference parameters of the interference submerged in noise can be extracted after processing. Therefore, this method has good practical value.

[0127] Figure 5 and Figure 6 is the comparison graph of the root mean square errors of the time difference and frequency difference estimated by the cross ambiguity function method when the main satellite interference-to-noise ratio is set to 5 dB and the secondary satellite interference signal-to-noise ratio is set from -30 dB to 0 dB, after processing the interference with the hard threshold function, soft threshold function, and matching filtering-hard threshold and matching filtering-soft threshold. It can be seen that the root mean square errors of the estimated time difference and frequency difference after matching filtering processing are significantly lower than those without processing.

[0128] Figure 7 is the GDOP simulation graph after substituting the satellite positioning position and speed after estimating the time-frequency difference parameters for two of the algorithms, Figure 7 in (a) is the simulation graph of GDOP simulation with the time-frequency difference parameters obtained without passing through the above method, Figure 7 in (b) is the simulation graph of GDOP simulation with the time-frequency difference parameters obtained after processing by the matching filtering-hard threshold method in the above method. The satellite positions and speeds are respectively:

[0129] S1 = [101.43, 0.0, 35786000], S2 = [125.0, 0.0, 35786000], V1 = [0.0, -3070.0, 0.0], V2 = [0.0, -3070.0, 0.0].

[0130] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium, on which computer instructions are stored. The computer instructions cause the computer to execute a low interference-to-noise ratio interference positioning method based on a multi-beam dual-satellite system. The method includes the following steps:

[0131] S100. Determine the interference frequency based on the main satellite interference, and establish an interference model for the main satellite interference and the secondary satellite interference accordingly;

[0132] S200. Perform matched filtering on the interferences relayed by two multi-beam satellites to the ground receiving station to improve the signal-to-interference-plus-noise ratio (SINR) between the interferences;

[0133] S300. Perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine the threshold and perform threshold processing on the wavelet coefficients to remove the noise coefficients and retain the interference coefficients.

[0134] S400. Use the retained interference coefficients for wavelet reconstruction to obtain the denoised interference.

[0135] S500. Substitute the obtained reconstructed interference into the cross ambiguity function for time-frequency difference parameter estimation;

[0136] S600. Use the time-frequency difference parameter estimation value, as well as the positions and velocities of the selected corresponding two satellites, to locate the interference source through the positioning and settlement equations.

[0137] Embodiment 3: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communication bus. Among them, the processor, the communications interface, and the memory communicate with each other through the communication bus. The processor may call the logical instructions in the memory to execute the low SINR interference positioning method based on a multi-beam dual-satellite system, and this method includes the following steps:

[0138] S100. Determine the interference frequency based on the main satellite interference, and establish an interference model for the main satellite interference and the secondary satellite interference accordingly;

[0139] S200. Perform matched filtering on the interferences relayed by two multi-beam satellites to the ground receiving station to improve the SINR between the interferences;

[0140] S300. Perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine the threshold and perform threshold processing on the wavelet coefficients to remove the noise coefficients and retain the interference coefficients.

[0141] S400. Use the retained interference coefficients for wavelet reconstruction to obtain the denoised interference.

[0142] S500. Substitute the obtained reconstructed interference into the cross ambiguity function for time-frequency difference parameter estimation;

[0143] S600. Use the estimated value of the time-frequency difference parameter, and select the positions and velocities of the corresponding two satellites. Then, locate the interference source through the positioning calculation equations.

[0144] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0145] Embodiment 4: The present embodiment provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the low interference-to-noise ratio interference positioning method based on a multi-beam dual-satellite system. The method includes the following steps:

[0146] S100. Determine the interference frequency according to the main satellite interference, and establish an interference model for the main satellite interference and the secondary satellite interference accordingly.

[0147] S200. Perform matched filtering on the interference forwarded by the two multi-beam satellites to the ground receiving station to improve the interference-to-noise ratio between the interferences.

[0148] S300. Perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine a threshold, perform threshold processing on the wavelet coefficients to remove the noise coefficients and retain the interference coefficients.

[0149] S400. Use the retained interference coefficients for wavelet reconstruction to obtain the denoised interference.

[0150] S500. Substitute the obtained reconstructed interference into the cross ambiguity function for time-frequency difference parameter estimation.

[0151] S600. Use the estimated value of the time-frequency difference parameter, and select the positions and velocities of the corresponding two satellites. Then, locate the interference source through the positioning calculation equations.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low dry-noise ratio interference detection method based on a multi-beam dual-satellite system. The multi-beam dual-satellite system includes two multi-beam satellites, an interference source, and a ground receiving station. Among the two multi-beam satellites, one is the main satellite and the other is the secondary satellite. The interference source is located within the range of one beam of the main satellite and within the sidelobe suppression range of the secondary satellite beam. The interference is relayed to the ground receiving station through the two multi-beam satellites. It is characterized in that, The interference detection method includes: S100. Determine the interference frequency based on the main satellite interference, and establish an interference model between the main satellite interference and the secondary satellite interference accordingly; S200. Perform matched filtering on the interference forwarded by two multi-beam satellites to the ground receiving station to improve the signal-to-interference-plus-noise ratio between the interferences; S300. Perform wavelet decomposition on the interference after matched filtering to obtain wavelet coefficients of different scales and frequencies; then determine a threshold, perform threshold processing on the wavelet coefficients to remove the noise coefficients and retain the interference coefficients; specifically: S310. Decompose the interference to be processed into sub-band interferences of different scales and frequencies through wavelet transform, expressed as: d p,q = u p,q + e p,q Among them, d p,q is the wavelet coefficients of each layer after wavelet transform of the noise interference y1(t) and y2(t); u p,q is the approximation coefficient, which is the wavelet transform coefficient of the effective part of the interference, and e p,q is the detail coefficient, which is the wavelet transform coefficient containing the interference noise part; p is the scale parameter, representing the number of wavelet decomposition layers, and q is the position offset of the wavelet function on the time axis, which is used to locate the local features of the signal; S320. Determine the threshold, and the threshold calculation formula is: where N is the length of the original signal, N p is the number of wavelet coefficients at the p-th layer; S330. Perform threshold processing on the interference detail coefficients, set the detail coefficients smaller than a certain threshold to zero to remove the influence of noise; S400. Use the retained interference coefficients for wavelet reconstruction to obtain the denoised interference.

2. The low dry-noise ratio interference detection method based on a multi-beam dual-star system according to claim 1, characterized in that, In step S100, the interference frequency is determined based on the main satellite interference, and the interference model between the main satellite interference and the secondary satellite interference is established accordingly. The specific method is: S110. The ground receiving station selects two multi-beam satellites to receive the interference emitted by the interference source and forwarded by the two multi-beam satellites; S120. Determine the main satellite, and determine the interference frequency based on the interference forwarded by the main satellite; S130. Perform fast Fourier transform on the forwarded interference, perform frequency transformation according to the determined interference frequency to obtain the original unshifted interference, and establish the interference model, expressed as: where x1(t) and x2(t) are the interference models of the interference source interference forwarded by the two multi-beam satellites to the ground receiving station respectively, s(t) is the interference source emitting a noise-free interference signal, t is the time variable; τ1 and τ2 are the arrival times of the interference source interference forwarded by the two multi-beam satellites to the ground receiving station respectively, e is the natural constant, j is the imaginary unit; f1 and f2 are the arrival frequencies of the interference source interference forwarded by the two multi-beam satellites to the ground receiving station respectively; n1(t) and n2(t) respectively represent the Gaussian white noise interference of the interference source interference forwarded by the two multi-beam satellites to the ground receiving station, and the interference and the noise are uncorrelated; G i is the complex gain containing amplitude and phase information for the i-th beam, and its calculation formula is: A n and R n are the excitation amplitude and phase of the element antenna, respectively, f n (θ, ψ) is the pattern function of the element antenna, i is the multi-beam satellite beam with different numbers, n is the nth element in the antenna array, k is the phase change amount within the element length, and θ and ψ represent the antenna elevation angle and azimuth angle, respectively; S140. Perform normalization processing on the interference model, and express the interference forwarded by the two multi-beam satellites to the ground receiving station as: Where, α is the relative amplitude value of the interference received by the ground receiving station from the two multi-beam satellites, α = G2 / G1; D represents the time difference of the interference received by the ground receiving station from the two multi-beam satellites, D = τ2 - τ1; f G represents the frequency difference of the interference received by the ground receiving station from the two multi-beam satellites, f d = f1 - f2.

3. The low dry-noise ratio interference detection method based on a multi-beam dual-star system according to claim 2, characterized in that In step S200, perform matched filtering on the interference forwarded by the two multi-beam satellites to the ground receiving station. The specific method is: S210. Establish a matched filter, expressed as: Among them, s(t) represents the known transmitted interference, h(t) is the impulse response in the time domain, and n(t) represents a zero-mean stationary additive Gaussian white noise with a power spectral density of P n (f) = n0 / 2; P n (f) represents the power spectral density, and n0 is the power spectral density value of the additive Gaussian white noise within the frequency range; S220. Substitute the interference models x1(t) and x2(t) into the matched filter, expressed as: where y1(t) and y2(t) are the output signals of the signals x1(t) and x2(t) after impulse response respectively, τ is the time offset, and h(t - τ) is the impulse response of the system.

4. The low dry-noise ratio interference detection method based on a multi-beam dual-star system according to claim 1, wherein In step S400, the approximated coefficients and detail coefficients after threshold processing are reconstructed through inverse wavelet transform to obtain the denoised interference.

5. A low dry-noise ratio interference location method based on a multi-beam double-star system, characterized in that, It includes: S500. Substitute the reconstructed interference obtained by the interference detection method described in any one of claims 1-4 into the cross ambiguity function for time-frequency difference parameter estimation; S600. Using the estimated value of the time-frequency difference parameter, as well as the positions and velocities of the corresponding two selected satellites, the interference source is located through the positioning and settlement equations.

6. The low dry-noise ratio interference positioning method based on a multi-beam dual-star system according to claim 5, wherein In step S500, the cross ambiguity function is expressed as: where x(t) and y(t) are the received forward interferences of the main satellite and the secondary satellite, * represents the complex conjugate, f is the Doppler frequency shift variable, and T is the total detection time.

7. The method for locating low dry-noise ratio interference based on a multi-beam double-star system according to claim 6, characterized in that The method for estimating the time-frequency difference parameter in step S500 is: Take the absolute value |CAF(τ,f)| of CAF(τ,f). The absolute value reaches a peak at the true time delay and Doppler frequency shift. According to the peak position of |CAF(τ,f)|, obtain the time delay D and Doppler frequency shift f d The estimated value is expressed as: where (D, f d ) represents the estimated values of time delay and Doppler frequency shift corresponding to the maximum value of |CAF(τ, f)|.

8. The low dry-noise ratio interference location method based on a multi-beam double-star system according to claim 6, characterized in that, In step S600, the positioning and settlement equations are expressed as: Among them, (x, y, z) represents the position of the unknown interference source, c is the speed of electromagnetic wave propagation in vacuum, Δt is the arrival time difference of the interference from the interference source measured by two satellites, which includes the delay error caused by various errors; Δf is the arrival frequency difference of the interference from the interference source measured by two satellites, f0 is the frequency of the interference source interference, a is the semi-major axis of the earth, b is the semi-minor axis of the earth, (x si , y si , z si ), i = 1, 2 are the satellite positions, r1, r2 are the distances from the two satellites to the interference source, (v xi , v yi , v zi ), i = 1, 2 are the satellite velocities.

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