A 5G radiation source positioning method, electronic device and storage medium based on satellite-borne virtual aperture

Abstract

A 5G radiation source positioning method, electronic device and storage medium based on satellite-borne virtual aperture, belonging to the field of passive positioning technology of 5G radiation sources. In order to improve the accuracy of positioning of non-cooperative 5G radiation sources on the ground, the present invention collects baseband signals, extracts the phase information of subcarrier signals; calculates the phase difference of the two subcarrier signals before and after and takes the average, obtains the azimuth phase information along the slow time change; constructs a Kalman filter model to estimate the phase term information related to the radiation source position information, obtains the Kalman filter result; reconstructs the azimuth signal to obtain the reconstructed azimuth signal; uses fractional Fourier transform to perform spectrum estimation on the reconstructed azimuth signal to extract the radiation source position information; estimates the satellite effective speed by search focusing according to the distance model and Doppler parameters, combined with orbital information, obtains the satellite effective speed, uses the estimated satellite effective speed to solve the position coordinates of the radiation source, and completes the positioning of the radiation source.

Description

Technical Field

[0001] The present invention belongs to the technical field of passive positioning of 5G radiation sources, and specifically relates to a 5G radiation source positioning method based on satellite-borne virtual aperture, an electronic device and a storage medium. Background Art

[0002] With the vigorous development and construction of 5G and the prospect of early deployment of 6G, telepathy integration has become a current research hotspot. At the same time, with the vigorous construction of low-orbit satellite systems, satellite communications have once again become a hot topic. The integration of communication, navigation and remote sensing can be achieved by combining low-orbit satellite communication systems. It can efficiently utilize limited resources such as low-orbit satellite payloads, orbits and frequencies, so it has great research value. The purpose of this invention is to use low-orbit communication satellites to realize the reconnaissance and positioning of non-cooperative communication radiation sources on the ground, and to achieve fixed-point shielding, interference or attack on enemy base stations or users, which has important strategic significance for the construction of electronic reconnaissance systems and the development of future electronic warfare.

[0003] Traditional satellite-borne electronic reconnaissance systems are mainly divided into two types: single-satellite passive positioning systems and multi-satellite passive positioning systems. Common single-satellite passive positioning methods mainly include direction-finding positioning methods based on phase interferometers or antenna arrays, and positioning methods based on instantaneous Doppler frequency or Doppler frequency change rate. The direction-finding positioning method mainly uses phase interferometers or antenna arrays to measure the azimuth and elevation angles of the incoming wave direction, and locates the radiation source in combination with the earth model. However, its positioning accuracy requires relying on a complex antenna structure, and the single-satellite mobile platform has restrictions on the antenna mounting. Therefore, the positioning accuracy of this method is relatively low. The single-satellite positioning method based on instantaneous Doppler frequency or Doppler frequency change rate uses multiple measurements of Doppler information to complete positioning. The system and principle are relatively simple, but it requires long-term observation, and the positioning accuracy is affected by the limited instantaneous Doppler information measurement accuracy, so the positioning accuracy is relatively low. The commonly used methods of multi-satellite passive positioning are mainly based on the arrival time difference, arrival frequency difference or the combination of the two. The multi-satellite passive positioning method measures the arrival time or arrival frequency difference of the same radiation source received by multiple satellites, and solves the radiation source position by combining the relative position between satellites and the earth model. Although the positioning accuracy is relatively high, it is necessary to consider the coordination issues such as time synchronization between multiple satellites. The system is relatively complex and the cost is high. Therefore, in comparison, although the positioning accuracy of single-satellite passive positioning is poor, the system structure is relatively simple, the cost is low, and it has more research and application value.

[0004] In recent years, in order to improve the positioning accuracy of passive positioning and reduce the complexity of the positioning system, the concept of synthetic aperture has been introduced into passive positioning. The mobility of satellites in space is utilized to coherently accumulate long-term data, convert the platform's time gain into spatial gain, and form a long virtual aperture to improve the azimuth resolution and achieve high-precision positioning.

[0005] Most of the existing positioning algorithms for communication radiation sources or terminals are based on multi-satellite systems. In addition to the problem of strict time synchronization requirements, in some communication scenarios, the terminal cannot access multiple satellites at the same time, that is, there may be a situation where only a single satellite passes overhead at the same time, and it is impossible to use multiple satellites to complete positioning. In addition, the positioning of communication terminals is mostly based on cooperative users, and the terminal needs to cooperate to complete the positioning. With the development of technology, traditional positioning technology has already appeared deceptive means, and it is impossible to guarantee the acquisition of a reliable position. Therefore, there is a need for research on the positioning of single-satellite communication users and the positioning of non-cooperative users. Summary of the invention

[0006] The problem to be solved by the present invention is to improve the accuracy of positioning of ground non-cooperative 5G radiation sources and solve the contradiction between low system complexity and high positioning accuracy of traditional single-star passive positioning systems, and propose a 5G radiation source positioning method based on satellite-borne virtual aperture.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions:

[0008] A 5G radiation source positioning method based on a satellite-borne virtual aperture comprises the following steps:

[0009] S1. Collect baseband signals, pre-process the collected baseband signals, rearrange them into a receiving signal matrix in a fast and slow time manner, and perform Fourier transform on the receiving signal matrix along the fast time to extract the phase information of the subcarrier signal;

[0010] S2. According to the statistical characteristics of the transmitted signal, the phase difference between the two subcarrier signals before and after is calculated based on the phase information of the subcarrier signal extracted in step S1 and the average is taken to obtain the azimuth phase information along the slow time variation;

[0011] S3. Construct a Kalman filter model, based on the azimuth phase information obtained in step S2 along the slow time variation, the azimuth phase information processed is the sum of the phase term related to the radiation source position and the phase term related to the transmission modulation symbol phase, wherein the phase term related to the transmission modulation symbol phase as positioning interference conforms to the Gaussian distribution, and then use the constructed Kalman filter model to estimate the phase term information related to the radiation source position information to obtain the Kalman filter result;

[0012] S4. Reconstruct the azimuth signal according to the Kalman filter result obtained in step S3 to obtain a reconstructed azimuth signal;

[0013] S5. Using fractional Fourier transform to perform spectrum estimation on the azimuth signal reconstructed in step S4 to extract the radiation source position information;

[0014] S6. According to the distance model and Doppler parameters, combined with the orbit information, the satellite effective speed is estimated by using search focusing to obtain the satellite effective speed. The position coordinates of the radiation source are solved using the estimated satellite effective speed to locate the radiation source.

[0015] Furthermore, the specific implementation method of step S1 includes the following steps:

[0016] S1.1. Set the 5G radiation source transmission signal to an OFDM signal, and the expression is:

[0017]

[0018] Among them, i, l, k are the frame number, OFDM symbol number, and subcarrier number respectively, and c ilk is the OFDM modulation symbol, K is the total number of subcarriers, L is the total number of OFDM symbols in a time slot, and f k represents the subcarrier frequency, f k =f0+kΔf, Δf is the subcarrier spacing; T s is the duration of an OFDM symbol, and t is the signal time;

[0019] S1.2. Set the baseband signal to the received signal s r (t r ,t m ), the expression is:

[0020]

[0021] Among them, t r ,t m They are fast time and slow time respectively, c is the speed of light, R(t m ) is a distance model of a 5G radiation source positioning method based on a satellite-borne virtual aperture;

[0022] R(t m ) is:

[0023]

[0024] Among them, v r is the effective speed of the satellite, (R0, X0) is the two-dimensional coordinate of the radiation source target in the positioning plane, R0 is the distance in the range direction, and X0 is the distance in the azimuth direction;

[0025] S1.3. Discretize the received signals and rearrange them with one time slot as the slow time interval. The expression of the received signal matrix is:

[0026]

[0027] Among them, m and n are the number of slow time points and fast time points of the signal respectively. The length of each OFDM symbol is set to N0. For 5G signal N0=4096, the fast time domain dimension is N=LN0, and the slow time domain dimension is M. Therefore, the dimension of the two-dimensional data {data} is M×N;

[0028] S1.4. Perform Fourier transform on the received signal matrix along the fast time to extract the phase information of the subcarrier signal. The Fourier transformed received signal matrix H r (m,k), the expression is:

[0029]

[0030] Among them, φ mk is the phase of the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, C mk is the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, and the expression is:

[0031]

[0032] According to the Fourier transform result, the phase Φ of the kth subcarrier signal is obtained k (t m ) is:

[0033]

[0034] Furthermore, the specific implementation method of step S2 includes the following steps:

[0035] S2.1. Calculate the phase difference between the kth and k+1th subcarrier signals to obtain the phase difference ΔΦ(t m ) is:

[0036]

[0037] Among them, Δφ mk =φ m,k+1 -φ mk , is the phase difference between the sum of the k+1th and kth subcarrier frequency modulation symbols in a slow time;

[0038] Then the subcarrier phase difference is calculated according to the odd and even subcarrier frequency points and the average is calculated to obtain the azimuth phase information along the slow time variation. for:

[0039]

[0040] in, [·] indicates rounding. is the phase term related to the transmitted modulation symbol in the subcarrier phase difference mean; Approximately follows a Gaussian distribution and satisfies

[0041] Furthermore, the specific implementation method of step S3 includes the following steps:

[0042] S3.1. Transform the distance model obtained in step S1 to obtain the expression:

[0043]

[0044] in, Then rewrite the distance model into a uniform acceleration motion model, and the expression is:

[0045]

[0046] v m =v m-1 +a m-1 T

[0047] a m =a m-1

[0048] Among them, r m ,v m ,a m are the distance, velocity and acceleration of the uniform acceleration model corresponding to the distance model at time m, and the initial values ​​are set to r0, v0, a0, and T is the slow time interval;

[0049] S3.2. Based on the azimuth phase information along the slow time variation obtained in step S2, the distance model is estimated, and the expression is obtained as follows:

[0050]

[0051] in, is regarded as observation noise, is R(t m ) is obtained based on the phase of the processed signal;

[0052] S3.3. Construct the Kalman filter model, the expression is:

[0053] Z m =AZ m-1 +ω m-1

[0054]

[0055]

[0056] Among them, Z mis the system state matrix at time m, A is the state transfer matrix, ω is the process noise, which obeys the Gaussian distribution p(ω)~N(0,Q), is the observed value of the state matrix at time m, H is the state observation matrix, n m To measure the noise, according to the result of step 2, it is

[0057] S3.4. Based on the model in step S3.3, use the Kalman filter algorithm to calculate Z m To estimate, the Kalman filter algorithm process is:

[0058] S3.4.1. Given the initial value Z0 = [r0, v0, a0], where r0 is The mean of

[0059] S3.4.2. Loop and update the following equations for Z m Estimate and get the Kalman filter result Z m , set the number of iterations to M, and the system of equations is:

[0060]

[0061] Furthermore, the specific implementation method of step S4 includes the following steps:

[0062] S4.1. According to the Kalman filter result obtained in step S3, the distance and speed estimation results are fitted twice and once respectively, and the expression is obtained as follows:

[0063] r=p r1 t 2 +p r2 t+p r3 , p r =[p r1 ,p r2 ,p r3 ];

[0064] v=p v1 t+p v2 ,p v =[p v1 ,p v2 ]

[0065] Among them, p r is the distance fitting result, p v is the speed fitting result;

[0066] S4.2. Reconstruct according to the distance model and use the fitting result p r As the distance model fitting result has a large error, p r and p vPerform distance fitting together and get the expression of distance fitting as follows:

[0067]

[0068] S4.3. Then calculate the signal reconstruction result to obtain the reconstructed azimuth signal, which is expressed as:

[0069]

[0070] Furthermore, the specific implementation method of step S5 includes the following steps:

[0071] S5.1. According to the distance model of step S1, the Doppler frequency f of the received signal is obtained. d The expression is:

[0072]

[0073] in, is the Doppler center moment,

[0074] The Doppler frequency of the reconstructed signal calculated according to the distance model obtained in step S4 is:

[0075]

[0076] The Doppler modulation frequency is

[0077] S5.2. Solve the normalized modulation rate K=k based on the Doppler modulation rate in step S5.1 r S, the normalization factor of the fractional Fourier transform is Where T a is the total duration of the signal in azimuth, i.e., the synthetic aperture time, f a is the azimuth sampling frequency of the signal;

[0078] According to the normalized modulation frequency, the corresponding optimal rotation angle of fractional Fourier transform is obtained, and the expression is:

[0079] α0=-arccotK

[0080] The reconstructed azimuth signal is subjected to fractional Fourier transform at the optimal rotation angle α0 of fractional Fourier transform. α0 (u), the expression is:

[0081]

[0082] in, is the transformation factor of FRFT, is the kernel function of FRFT, which is expressed as follows:

[0083]

[0084] in δ(t) is the Dirac function;

[0085] S5.3. Then use the fractional Fourier transform to solve the Doppler center frequency. The time corresponding to the maximum value of the fractional Fourier transform at the optimal rotation angle is the Doppler center time. The expression is:

[0086]

[0087] in, is the corresponding value in the fractional domain of the maximum value of the fractional Fourier transform at the optimal rotation angle.

[0088] Furthermore, the specific implementation method of step S6 includes the following steps:

[0089] S6.1. Based on the fact that the effective satellite velocity is between the satellite orbit velocity and the satellite beam velocity, the effective satellite velocity is estimated by using this interval as the search interval. The specific search method is as follows:

[0090] The estimated satellite effective speed and Doppler modulation frequency are used to calculate the corresponding distance. According to the distance, a corresponding position coordinate is calculated on the ground. This position is used as the radiation source position. The signal is constructed using the distance between the satellite position and the processed received signal during the signal accumulation time. The speed when the signal matches is the satellite effective speed v r , at this time, the corresponding radiation source position coordinates are the target radiation source position;

[0091] S6.2. Using STK to simulate the satellite orbit, we can get the satellite position and orbital velocity, and find the satellite position S corresponding to the Doppler center time. T and orbital velocity V T , at this time the distance between the satellite and the radiation source is R0, the radial velocity is 0,

[0092] The Doppler modulation frequency is used to solve the distance between the satellite and the radiation source, and the calculation formula is:

[0093]

[0094] S6.3. Then solve the radiation source position coordinate U according to the following equations, and the calculation formula is:

[0095] V T ·(S T -U) T =0

[0096]

[0097] Among them, R e is the radius of the earth, and f is the flattening of the earth.

[0098] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a 5G radiation source positioning method based on a satellite-borne virtual aperture are implemented.

[0099] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the 5G radiation source positioning method based on a satellite-borne virtual aperture.

[0100] Beneficial effects of the present invention:

[0101] The present invention discloses a 5G radiation source positioning method based on a satellite-borne virtual aperture, which is a passive positioning technology for 5G radiation sources based on a satellite-borne virtual aperture. The present invention utilizes a virtual aperture formed by the movement of a satellite-borne platform in space, utilizes coherent processing technology to convert the spatial gain of the antenna into a time gain, accumulates the phase history, and utilizes the statistical characteristics of the modulation symbols of the 5G signal transmitter and the multi-carrier modulation characteristics of the 5G signal to compensate the signal phase, thereby completing the positioning of the 5G radiation source. The present invention can complete the positioning of non-cooperative 5G radiation sources, and phase compensation can be completed without the use of pilot structures and information; high-precision positioning can be achieved by using a single satellite single antenna and software-side signal processing, without the need for complex antennas and hardware structures, which greatly reduces system complexity and reduces costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 This is a flow chart of a 5G radiation source positioning method based on satellite-borne virtual aperture according to the present invention;

[0103] Figure 2 This is a schematic diagram of the result of step S2 of a 5G radiation source positioning method based on satellite-borne virtual aperture according to the present invention;

[0104] Figure 3 Schematic diagram of the result of step 3 of a 5G radiation source positioning method based on a satellite-borne virtual aperture according to the present invention, wherein (a) is the estimation result of r in the system state matrix Z, (b) is the estimation result of v in the system state matrix Z, and (c) is the estimation result of a in the system state matrix Z;

[0105] Figure 4 This is a schematic diagram of the distance fitting results of step 4 of a 5G radiation source positioning method based on satellite-borne virtual aperture described in the present invention.

[0106] Figure 5It is a time domain diagram of the reconstructed azimuth signal of step 4 of the 5G radiation source positioning method based on satellite-borne virtual aperture described in the present invention;

[0107] Figure 6 This is a schematic diagram of the results of step 6 of a 5G radiation source positioning method based on satellite-borne virtual aperture described in the present invention. DETAILED DESCRIPTION

[0108] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0109] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0110] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 6 The detailed instructions are as follows:

[0111] Embodiment 1:

[0112] A 5G radiation source positioning method based on a satellite-borne virtual aperture comprises the following steps:

[0113] S1. Collect baseband signals, pre-process the collected baseband signals, rearrange them into a receiving signal matrix in a fast and slow time manner, and perform Fourier transform on the receiving signal matrix along the fast time to extract the phase information of the subcarrier signal;

[0114] Furthermore, the specific implementation method of step S1 includes the following steps:

[0115] S1.1. Set the 5G radiation source transmission signal to an OFDM signal, and the expression is:

[0116]

[0117] Among them, i, l, k are the frame number, OFDM symbol number, and subcarrier number respectively, and c ilkis the OFDM modulation symbol, K is the total number of subcarriers, L is the total number of OFDM symbols in a time slot, and f k represents the subcarrier frequency, f k =f0+kΔf, Δf is the subcarrier spacing; T s is the duration of an OFDM symbol, and t is the signal time;

[0118] S1.2. Set the baseband signal to the received signal s r (t r ,t m ), the expression is:

[0119]

[0120] Among them, t r ,t m They are fast time and slow time respectively, c is the speed of light, R(t m ) is a distance model of a 5G radiation source positioning method based on a satellite-borne virtual aperture;

[0121] R(t m ) is:

[0122]

[0123] Among them, v r is the effective speed of the satellite, (R0, X0) is the two-dimensional coordinate of the radiation source target in the positioning plane, R0 is the distance in the range direction, and X0 is the distance in the azimuth direction;

[0124] S1.3. Discretize the received signals and rearrange them with one time slot as the slow time interval. The expression of the received signal matrix is:

[0125]

[0126] Among them, m and n are the number of slow time points and fast time points of the signal respectively. The length of each OFDM symbol is set to N0. For 5G signal N0=4096, the fast time domain dimension is N=LN0, and the slow time domain dimension is M. Therefore, the dimension of the two-dimensional data {data} is M×N;

[0127] S1.4. Perform Fourier transform on the received signal matrix along the fast time to extract the phase information of the subcarrier signal. The Fourier transformed received signal matrix H r (m,k), the expression is:

[0128]

[0129] Among them, φ mkis the phase of the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, C mk is the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, and the expression is:

[0130]

[0131] According to the Fourier transform result, the phase Φ of the kth subcarrier signal is obtained k (t m ) is:

[0132]

[0133] S2. According to the statistical characteristics of the transmitted signal, the phase difference between the two subcarrier signals before and after is calculated based on the phase information of the subcarrier signal extracted in step S1 and the average is taken to obtain the azimuth phase information along the slow time variation;

[0134] Furthermore, the specific implementation method of step S2 includes the following steps:

[0135] S2.1. Calculate the phase difference between the kth and k+1th subcarrier signals to obtain the phase difference ΔΦ(t m ) is:

[0136]

[0137] Among them, Δφ mk =φ m,k+1 -φ mk , is the phase difference between the sum of the k+1th and kth subcarrier frequency modulation symbols in a slow time;

[0138] Then the subcarrier phase difference is calculated according to the odd and even subcarrier frequency points and the average is calculated to obtain the azimuth phase information along the slow time variation. for:

[0139]

[0140] in, [·] indicates rounding. is the phase term related to the transmitted modulation symbol in the subcarrier phase difference mean; Approximately follows a Gaussian distribution and satisfies

[0141] Furthermore, according to the central limit theorem, the sum of multiple independent and identically distributed random variables is approximately close to the Gaussian distribution, so it can be considered that Approximately follows Gaussian distribution;

[0142] Furthermore, the proof is as follows:

[0143] 1) In step S1, after FFT, The real and imaginary parts of are approximately independent and identically distributed Gaussian distributions:

[0144] 2) Phase Since I(c) and R(c) are independent and identically distributed Gaussian variables, then It obeys the standard Cauchy distribution, so the phase obeys the uniform distribution, that is, φ mk ~U(-π,π), the mean is 0 and the variance is

[0145] 3) In step S2, after the phase difference between the two subcarriers is calculated, the mean is 0 and the variance is After taking the average of the phase difference, according to the central limit theorem, it approximately conforms to the Gaussian distribution, with a mean of 0 and a variance of

[0146] S3. Construct a Kalman filter model, based on the azimuth phase information obtained in step S2 along the slow time variation, the azimuth phase information processed is the sum of the phase term related to the radiation source position and the phase term related to the transmission modulation symbol phase, wherein the phase term related to the transmission modulation symbol phase as positioning interference conforms to the Gaussian distribution, and then use the constructed Kalman filter model to estimate the phase term information related to the radiation source position information to obtain the Kalman filter result;

[0147] Furthermore, the specific implementation method of step S3 includes the following steps:

[0148] S3.1. Transform the distance model obtained in step S1 to obtain the expression:

[0149]

[0150] in, Then rewrite the distance model into a uniform acceleration motion model, and the expression is:

[0151]

[0152] v m =v m-1 +a m-1 T

[0153] a m =a m-1

[0154] Among them, r m ,v m ,a mare the distance, velocity and acceleration of the uniform acceleration model corresponding to the distance model at time m, and the initial values ​​are set to r0, v0, a0, and T is the slow time interval;

[0155] S3.2. Based on the azimuth phase information along the slow time variation obtained in step S2, the distance model is estimated, and the expression is obtained as follows:

[0156]

[0157] in, is regarded as observation noise, is R(t m ) is obtained based on the phase of the processed signal;

[0158] S3.3. Construct the Kalman filter model, the expression is:

[0159] Z m =AZ m-1 +ω m-1

[0160]

[0161]

[0162] Among them, Z m is the system state matrix at time m, A is the state transfer matrix, ω is the process noise, which obeys the Gaussian distribution p(ω)~N(0,Q), is the observed value of the state matrix at time m, H is the state observation matrix, n m To measure the noise, according to the result of step 2, it is

[0163] S3.4. Based on the model in step S3.3, use the Kalman filter algorithm to calculate Z m To estimate, the Kalman filter algorithm process is:

[0164] S3.4.1. Given the initial value Z0 = [r0, v0, a0], where r0 is The mean of

[0165] S3.4.2. Loop and update the following equations for Z m Estimate and get the Kalman filter result Z m , set the number of iterations to M, and the system of equations is:

[0166]

[0167] S4. Reconstruct the azimuth signal according to the Kalman filter result obtained in step S3 to obtain a reconstructed azimuth signal;

[0168] Furthermore, the specific implementation method of step S4 includes the following steps:

[0169] S4.1. According to the Kalman filter result obtained in step S3, the distance and speed estimation results are fitted twice and once respectively, and the expression is obtained as follows:

[0170] r=p r1 t 2 +p r2 t+p r3 , p r =[p r1 ,p r2 ,p r3 ];

[0171] v=p v1 t+p v2 ,p v =[p v1 ,p v2 ]

[0172] Among them, p r is the distance fitting result, p v is the speed fitting result;

[0173] S4.2. Reconstruct according to the distance model and use the fitting result p r As the distance model fitting result has a large error, p r and p v Perform distance fitting together and get the expression of distance fitting as follows:

[0174]

[0175] S4.3. Then calculate the signal reconstruction result to obtain the reconstructed azimuth signal, which is expressed as:

[0176]

[0177] S5. Using fractional Fourier transform to perform spectrum estimation on the azimuth signal reconstructed in step S4 to extract the radiation source position information;

[0178] Furthermore, the specific implementation method of step S5 includes the following steps:

[0179] S5.1. According to the distance model of step S1, the Doppler frequency f of the received signal is obtained. d The expression is:

[0180]

[0181] in, is the Doppler center moment,

[0182] The Doppler frequency of the reconstructed signal calculated according to the distance model obtained in step S4 is:

[0183]

[0184] The Doppler modulation frequency is

[0185] S5.2. Solve the normalized modulation rate K=k based on the Doppler modulation rate in step S5.1 r S, the normalization factor of the fractional Fourier transform is Where T a is the total duration of the signal in azimuth, i.e., the synthetic aperture time, f a is the azimuth sampling frequency of the signal;

[0186] According to the normalized modulation frequency, the corresponding optimal rotation angle of fractional Fourier transform is obtained, and the expression is:

[0187] α0=-arccotK

[0188] The reconstructed azimuth signal is subjected to fractional Fourier transform at the optimal rotation angle α0 of fractional Fourier transform. The expression is:

[0189]

[0190] in, is the transformation factor of FRFT, is the kernel function of FRFT, which is expressed as follows:

[0191]

[0192] in δ(t) is the Dirac function;

[0193] S5.3. Then use the fractional Fourier transform to solve the Doppler center frequency. The time corresponding to the maximum value of the fractional Fourier transform at the optimal rotation angle is the Doppler center time. The expression is:

[0194]

[0195] in, is the corresponding value in the fractional domain of the maximum value of the fractional Fourier transform at the optimal rotation angle.

[0196] S6. According to the distance model and Doppler parameters, combined with the orbit information, the satellite effective speed is estimated by using search focusing to obtain the satellite effective speed. The position coordinates of the radiation source are solved using the estimated satellite effective speed to locate the radiation source.

[0197] Furthermore, the specific implementation method of step S6 includes the following steps:

[0198] S6.1. Based on the fact that the effective satellite velocity is between the satellite orbit velocity and the satellite beam velocity, the effective satellite velocity is estimated by using this interval as the search interval. The specific search method is as follows:

[0199] The estimated satellite effective speed and Doppler modulation frequency are used to calculate the corresponding distance. According to the distance, a corresponding position coordinate is calculated on the ground. This position is used as the radiation source position. The signal is constructed using the distance between the satellite position and the processed received signal during the signal accumulation time. The speed when the signal matches is the satellite effective speed v r , at this time, the corresponding radiation source position coordinates are the target radiation source position;

[0200] Furthermore, since the effective satellite velocity is between the satellite orbit velocity and the satellite beam velocity, this interval can be used as the search interval to search and estimate the effective satellite velocity;

[0201] S6.2. Using STK to simulate the satellite orbit, we can get the satellite position and orbital velocity, and find the satellite position S corresponding to the Doppler center time. T and orbital velocity V T , at this time, the distance between the satellite and the radiation source is R0, the radial velocity is 0, and the distance between the satellite and the radiation source is solved by using the Doppler modulation frequency, and the calculation formula is:

[0202]

[0203] S6.3. Then solve the radiation source position coordinate U according to the following equations, and the calculation formula is:

[0204] V T ·(S T -U) T =0

[0205]

[0206] diag(1,1,(1-f) -2 )·U·U T =R e 2

[0207] Among them, R eis the radius of the earth, and f is the flattening of the earth.

[0208] The method of this embodiment is illustrated as follows:

[0209] The following settings are made in step S1: 5G signal parameters are f0=29GHz, Δf=30kHz, B=20MHz, then the number of subcarriers K=667;

[0210] Further, the received signal is discretized and rearranged with a time slot length, which is 0.5 ms in this embodiment, for a slow time interval, and the signal length is 150 ms;

[0211] Furthermore, there are 14 OFDM symbols in a time slot signal, and the length of each OFDM symbol is N0=4096, then the fast time domain dimension is N=57344, and the slow time domain dimension is M=300;

[0212] In step S2, according to the central limit theorem, the sum of multiple independent and identically distributed random variables is approximately close to the Gaussian distribution, so it can be considered that Approximately obeys Gaussian distribution, satisfying Draw The distribution histogram of Figure 2 As shown, the variance of the simulation results is 0.0199, which is close to the theoretical results;

[0213] In step S3, based on the above settings, the phase signal processed in step S2 satisfies:

[0214]

[0215] Then we have: Set T = 0.5ms as the slow time interval, that is, the signal time of a time slot; use the Kalman filter algorithm to complete the estimation of Z. The Z estimation result is as follows: Figure 3 As shown, (a) is the estimation result of r in the system state matrix Z, (b) is the estimation result of v in the system state matrix Z, and (c) is the estimation result of a in the system state matrix Z.

[0216] In step S4, f0 is set to 29 GHz, and the signal reconstruction result is shown in the figure below. Figure 4 and Figure 5 As shown, Figure 4 The distance fitting result is obtained by fitting the distance using the S3 result. Figure 5 The time domain diagram of the reconstructed signal is to reconstruct the azimuth signal using the fitting result;

[0217] For step S5, set T a =150ms, f a =2kHz;

[0218] In step S6, the satellite is set to a low-orbit circular orbit with an orbit height of 498 meters. The satellite position and orbit speed can be obtained by simulating the satellite orbit using STK. Finally, the target radiation source positioning result is obtained as follows Figure 6 As shown in the figure, the target's true position coordinates and positioning result target are given, and the positioning error is about 1.46km.

[0219] Embodiment 2:

[0220] An electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of a 5G radiation source positioning method based on a satellite-borne virtual aperture described in Example 1 when executing the computer program.

[0221] The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. Furthermore, the processor is used to implement the steps of the above-mentioned 5G radiation source positioning method based on a satellite-borne virtual aperture when executing a computer program stored in the memory.

[0222] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0223] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0224] Example 3

[0225] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, a 5G radiation source positioning method based on a satellite-borne virtual aperture as described in Example 1 is implemented.

[0226] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. The computer-readable storage medium stores a computer program. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned 5G radiation source positioning method based on satellite-borne virtual aperture can be implemented.

[0227] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0228] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0229] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A 5G radiation source positioning method based on satellite-borne virtual aperture, characterized in that: The steps include: S1. Collect baseband signals, pre-process the collected baseband signals, rearrange them into a receiving signal matrix in a fast and slow time manner, and perform Fourier transform on the receiving signal matrix along the fast time to extract the phase information of the subcarrier signal; S2. According to the statistical characteristics of the transmitted signal, the phase difference between the two subcarrier signals before and after is calculated based on the phase information of the subcarrier signal extracted in step S1 and the average is taken to obtain the azimuth phase information along the slow time variation; S3. Construct a Kalman filter model, based on the azimuth phase information obtained in step S2 along the slow time variation, the azimuth phase information processed is the sum of the phase term related to the radiation source position and the phase term related to the transmission modulation symbol phase, wherein the phase term related to the transmission modulation symbol phase as positioning interference conforms to the Gaussian distribution, and then use the constructed Kalman filter model to estimate the phase term information related to the radiation source position information to obtain the Kalman filter result; S4. Reconstruct the azimuth signal according to the Kalman filter result obtained in step S3 to obtain a reconstructed azimuth signal; S5. Using fractional Fourier transform to perform spectrum estimation on the azimuth signal reconstructed in step S4 to extract the radiation source position information; S6. According to the distance model and Doppler parameters, combined with the orbit information, the satellite effective speed is estimated by using search focusing to obtain the satellite effective speed. The position coordinates of the radiation source are solved using the estimated satellite effective speed to locate the radiation source.

2. A 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 1, characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Set the 5G radiation source transmission signal to an OFDM signal, and the expression is: Among them, i, l, k are the frame number, OFDM symbol number, and subcarrier number respectively, and c ilk is the OFDM modulation symbol, K is the total number of subcarriers, L is the total number of OFDM symbols in a time slot, and f k represents the subcarrier frequency, f k =f0+kΔf, Δf is the subcarrier spacing; T s is the duration of an OFDM symbol, and t is the signal time; S1.

2. Set the baseband signal to the received signal s r (t r ,t m ), the expression is: Among them, t r ,t m They are fast time and slow time respectively, c is the speed of light, R(t m ) is a distance model of a 5G radiation source positioning method based on a satellite-borne virtual aperture; R(t m ) is: Among them, v r is the effective speed of the satellite, (R0, X0) is the two-dimensional coordinate of the radiation source target in the positioning plane, R0 is the distance in the range direction, and X0 is the distance in the azimuth direction; S1.

3. Discretize the received signals and rearrange them with one time slot as the slow time interval. The expression of the received signal matrix is: Among them, m and n are the number of slow time points and fast time points of the signal respectively. The length of each OFDM symbol is set to N0. For 5G signal N0=4096, the fast time domain dimension is N=LN0, and the slow time domain dimension is M. Therefore, the dimension of the two-dimensional data {data} is M×N; S1.

4. Perform Fourier transform on the received signal matrix along the fast time to extract the phase information of the subcarrier signal. The Fourier transformed received signal matrix H r (m,k), the expression is: Among them, φ mk is the phase of the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, C mk is the sum of the modulation symbols of L OFDM symbols at the kth subcarrier frequency in a slow time, and the expression is: According to the Fourier transform result, the phase Φ of the kth subcarrier signal is obtained k (t m ) is:

3. A 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 2, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Calculate the phase difference between the kth and k+1th subcarrier signals to obtain the phase difference ΔΦ(t m ) is: Among them, Δφ mk =φ m,k+1 -φ mk , is the phase difference between the sum of the k+1th and kth subcarrier frequency modulation symbols in a slow time; Then the subcarrier phase difference is calculated according to the odd and even subcarrier frequency points and the average is calculated to obtain the azimuth phase information along the slow time variation. for: in, [·] indicates rounding. is the phase term related to the transmitted modulation symbol in the subcarrier phase difference mean; Approximately follows a Gaussian distribution and satisfies 4. The 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 3 is characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Transform the distance model obtained in step S1 to obtain the expression: in, Then rewrite the distance model into a uniform acceleration motion model, and the expression is: v m =v m-1 +a m-1 T a m =a m-1 Among them, r m ,v m ,a m are the distance, velocity and acceleration of the uniform acceleration model corresponding to the distance model at time m, and the initial values ​​are set to r0, v0, a0, and T is the slow time interval; S3.

2. Based on the azimuth phase information along the slow time variation obtained in step S2, the distance model is estimated, and the expression is obtained as follows: in, is regarded as observation noise, is R(t m ) is obtained based on the phase of the processed signal; S3.

3. Construct the Kalman filter model, the expression is: Z m =AZ m-1 +ω m-1 Among them, Z m is the system state matrix at time m, A is the state transfer matrix, ω is the process noise, which obeys the Gaussian distribution p(ω)~N(0,Q), is the observed value of the state matrix at time m, H is the state observation matrix, n m To measure the noise, according to the result of step 2, it is p(n)~N(0,R), S3.

4. Based on the model in step S3.3, use the Kalman filter algorithm to calculate Z m To estimate, the Kalman filter algorithm process is: S3.4.

1. Given the initial value Z0 = [r0, v0, a0], where r0 is The mean of S3.4.

2. Loop and update the following equations for Z m Estimate and get the Kalman filter result Z m , set the number of iterations to M, and the equation set is: Prior (prediction): A priori error covariance: Kalman gain: Error covariance: Posterior (estimate):

5. A 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 4, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. According to the Kalman filter result obtained in step S3, the distance and speed estimation results are fitted twice and once respectively, and the expression is obtained as follows: r=p r1 t 2 +p r2 t+p r3 ,p r =[p r1 ,p r2 ,p r3 ]; v=p v1 t+p v2 ,p v =[p v1 ,p v2 ] Among them, p r is the distance fitting result, p v is the speed fitting result; S4.

2. Reconstruct according to the distance model and use the fitting result p r As the distance model fitting result has a large error, p r and p v Perform distance fitting together and get the expression of distance fitting as follows: S4.

3. Then calculate the signal reconstruction result to obtain the reconstructed azimuth signal, which is expressed as:

6. A 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 5, characterized in that: The specific implementation method of step S5 includes the following steps: S5.

1. According to the distance model of step S1, the Doppler frequency f of the received signal is obtained. d The expression is: in, is the Doppler center moment, The Doppler frequency of the reconstructed signal calculated according to the distance model obtained in step S4 is: The Doppler modulation frequency is S5.

2. Solve the normalized modulation rate K=k based on the Doppler modulation rate in step S5.1 r S, the normalization factor of the fractional Fourier transform is Where T a is the total duration of the signal in azimuth, i.e., the synthetic aperture time, f a is the azimuth sampling frequency of the signal; According to the normalized modulation frequency, the corresponding optimal rotation angle of fractional Fourier transform is obtained, and the expression is: α0=-arccotK The reconstructed azimuth signal is subjected to fractional Fourier transform at the optimal rotation angle α0 of fractional Fourier transform. The expression is: in, is the transformation factor of FRFT, is the kernel function of FRFT, which is expressed as follows: in δ(t) is the Dirac function; S5.

3. Then use the fractional Fourier transform to solve the Doppler center frequency. The time corresponding to the maximum value of the fractional Fourier transform at the optimal rotation angle is the Doppler center time. The expression is: in, is the corresponding value in the fractional domain of the maximum value of the fractional Fourier transform at the optimal rotation angle.

7. A 5G radiation source positioning method based on satellite-borne virtual aperture according to claim 6, characterized in that: The specific implementation method of step S6 includes the following steps: S6.

1. Based on the fact that the effective satellite velocity is between the satellite orbit velocity and the satellite beam velocity, the effective satellite velocity is estimated by using this interval as the search interval. The specific search method is as follows: The estimated satellite effective speed and Doppler modulation frequency are used to calculate the corresponding distance. According to the distance, a corresponding position coordinate is calculated on the ground. This position is used as the radiation source position. The signal is constructed using the distance between the satellite position and the processed received signal during the signal accumulation time. The speed when the signal matches is the satellite effective speed v r , at this time, the corresponding radiation source position coordinates are the target radiation source position; S6.

2. Using STK to simulate the satellite orbit, we can get the satellite position and orbital velocity, and find the satellite position S corresponding to the Doppler center time. T and orbital velocity V T , at this time, the distance between the satellite and the radiation source is R0, the radial velocity is 0, and the distance between the satellite and the radiation source is solved by using the Doppler modulation frequency, and the calculation formula is: S6.

3. Then solve the radiation source position coordinate U according to the following equations, and the calculation formula is: V T ·(S T -U) T =0 diag(1,1,(1-f) -2 )·U·U T =R e 2 Among them, R e is the radius of the earth, and f is the flattening of the earth.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of a 5G radiation source positioning method based on satellite-borne virtual aperture as described in any one of claims 1-7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the 5G radiation source positioning method based on satellite-borne virtual aperture described in any one of claims 1 to 7 is implemented.

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