Microwave array accurate positioning method based on radio frequency signals

Abstract

The invention discloses a microwave array accurate positioning method based on a radio frequency signal. The method comprises the following steps: S1, receiving navigation satellite signals from a BDS system, a GPS system and a GLONASS system; s2, the received GNSS signals are preprocessed; s4, performing enhancement processing on the weak signal; s5, performing fusion processing on the main lobe signal, the side lobe signal, the strong signal and the weak signal by using a multi-signal fusion algorithm; s6, a microwave array antenna is installed on the spacecraft; s7, fusing a microwave array positioning result with a positioning result of the multi-constellation combined navigation system by using a position calculation and correction algorithm; s8, transmitting a positioning result to a ground control center through a data transmission module; and S9, according to the signal strength and the positioning precision monitored in real time, weak signals are effectively enhanced by adopting adaptive beam forming and spatial filtering technologies, noise interference is suppressed, and the system can stably work in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of radio frequency positioning technology, and in particular to a microwave array precise positioning method based on radio frequency signals. Background Art

[0002] In the application of high-orbit (GEO) spacecraft, traditional GNSS navigation systems face challenges in signal coverage and signal strength. Due to factors such as earth occlusion, long transmission distance, and atmospheric loss, GNSS signals will experience severe attenuation and extremely uneven intensity distribution in high-orbit environments. The specific problems and application scenarios are as follows: 1. Signal blocking and attenuation issues: High-orbit spacecraft can only receive GNSS signals in the range of -169 to -157 dBW when the angle with the line connecting the center of the earth is 9.18°14°; when the angle is between 14° and 39.0413°, the received satellite signal strength is in the range of -182 to -168 dBW.

[0003] 2. Uneven distribution of signal strength: The relatively weak sidelobe signals account for about 78% (GEO spacecraft) to 80% (HEO spacecraft) of the receivable signals, which is far more than the main lobe signals with stronger signal strength. Summary of the invention

[0004] The present invention aims to provide a microwave array precise positioning method based on radio frequency signals to solve the above problems.

[0005] The present invention is achieved through the following technical solutions: A microwave array precise positioning method based on radio frequency signals comprises the following steps: S1: Install a BDS / GPS / GLONASS multi-constellation receiver on a GEO spacecraft to receive navigation satellite signals from the BDS, GPS and GLONASS systems; A high-sensitivity GNSS receiver with multi-constellation reception capability is installed on the GEO spacecraft. The receiver can receive navigation satellite signals from the BDS, GPS and GLONASS systems. The receiver antenna is installed on the outer surface of the spacecraft and can cover a reception range of 9.18°~39°. S2: Preprocess the received GNSS signal, including removing atmospheric loss, multipath effect and noise interference, and amplifying the signal; S3: using the signal detection algorithm to distinguish the main lobe signal from the side lobe signal, and classify the signal strength into strong signals and weak signals; S3.1: Calculate the signal-to-noise ratio (SNR) of each received signal; S3.2: According to a preset threshold, a signal with an SNR greater than the threshold is classified as a main lobe signal, and a signal with an SNR less than the threshold is classified as a side lobe signal; S3.3: Classify the main lobe signal and side lobe signal by strength, into strong signals (SNR>20 dB) and weak signals (SNR ≤ 20 dB) S4: Enhance weak signals by using a signal enhancement algorithm, adaptive beamforming method and spatial filtering technology to improve the signal-to-noise ratio of weak signals; S5: fusing the main lobe signal and the side lobe signal, the strong signal and the weak signal using a multi-signal fusion algorithm; S5.1: Inputting the measured values ​​of the main lobe signal and the side lobe signal, the strong signal and the weak signal into the Kalman filter; S5.2: The Kalman filter generates the optimal navigation solution through state prediction and measurement update; S6: Install a microwave array antenna on the spacecraft, use the signals received by the microwave array antenna for precise positioning, and use the microwave array signal processing algorithm to determine the signal source position. The microwave array signal processing algorithm includes the multiple signal classification (MUSIC) algorithm and the direction of arrival estimation (DOA) algorithm; S6.1: Estimate the direction of arrival of the signal using the MUSIC algorithm; S6.2: Calculate the phase difference of multiple antenna units using the DOA algorithm to determine the precise location of the signal source; S7: The microwave array positioning result is integrated with the positioning result of the multi-constellation joint navigation system using a position solution and correction algorithm; S7.1: Input the positioning result of the microwave array and the positioning result of the multi-constellation joint navigation system into the extended Kalman filter; S7.2: The extended Kalman filter generates the optimal positioning solution through state prediction and measurement update; S8: The positioning result is transmitted to the ground control center through the data transmission module. The ground control center processes and analyzes the received data and adjusts the antenna beam direction in real time through the adaptive direction adjustment algorithm; S9: Based on the real-time monitored signal strength and positioning accuracy, the segment selection and optimization algorithm is used to dynamically select the best signal frequency band.

[0006] Preferably, in S2, the received GNSS signal is preprocessed, including removing atmospheric loss, multipath effect and noise interference, and gain amplifying the signal. The specific steps are as follows: S2.1: Use the atmospheric model and measured data to correct the atmospheric loss using the correction algorithm: ; in, is the atmospheric loss correction value, P is the air pressure, T is the temperature, H is the altitude, , , is the correction factor; S2.2: Use the signal multipath detection algorithm to identify and correct the multipath effect. The signal multipath detection algorithm is: ; in, is the phase shift, is the speed of light, is the signal frequency, For time delay; S2.3: Use low-pass filter and adaptive filter to remove noise interference in the signal. The transfer function of the low-pass filter is: ; The update formula of the adaptive filter is: ; in, is the filter weight vector, is the input signal vector, is the step length, is the error signal; S2.4: Use an amplifier to amplify the signal. The formula is: ; in, is the output voltage, is the input voltage, is the magnification.

[0007] Preferably, in S2.3, a multi-stage gain amplifier is used to increase the signal strength through multi-stage amplification, and the specific steps are: S2.3.1: First-stage amplification, using a high-gain amplifier for preliminary amplification, the formula is; ; S2.3.2: Secondary amplification, using a low noise amplifier for secondary amplification, the formula is; ; S2.3.3: Three-stage amplification, using a bandpass filter amplifier for final amplification, the formula is; .

[0008] in, is the output voltage of the first stage amplifier, is the output voltage of the secondary amplifier, is the output voltage, is the input voltage, is the first-order magnification, is the secondary magnification, It is the third level of magnification.

[0009] 4. Preferably, in S3, the algorithm for distinguishing the main lobe signal from the side lobe signal is based on the characteristics of signal strength and signal direction. The main lobe signal is directed toward the navigation satellite and has a higher signal strength. The side lobe signal is a signal in a non-main lobe direction and has a weaker signal strength. The formula for distinguishing the main lobe signal from the side lobe signal is: Main lobe signal: ; Side lobe signal: Where S is the signal strength, is the signal strength threshold, is the launch direction of the navigation satellite, is the angle at which the signal arrives at the microwave array, and ∧ represents the logical “AND” operator, which is used to connect two conditions.

[0010] Preferably, in S4, the adaptive beamforming algorithm achieves signal enhancement and interference suppression by adjusting the weight of the antenna array, and the weight update formula is: ; in, is the weight vector, is the step size parameter, is the error signal, is the input signal vector, Represents the updated weight.

[0011] Preferably, in S6, the MUSIC algorithm realizes high-precision positioning of the signal through feature decomposition and spectrum peak search, and the spectrum peak search formula of the MUSIC algorithm is: ; in, is the steering vector of the signal, is the eigenvector of the noise subspace, is the number of antenna array elements, is the number of signals, and i is the number of antenna elements minus the index of the number of signals.

[0012] Preferably, in S7, the position solution and correction algorithm is: ; in, is the estimated position, is the design matrix, is the observation vector, Design matrix The transpose of .

[0013] Preferably, in S8, the adaptive direction adjustment algorithm adjusts the beam direction of the antenna in real time. The adaptive direction adjustment algorithm adjusts the beam direction by adjusting the phase of the antenna array element. The beam direction adjustment formula is: ; in, is the beam direction, is the speed of light, is the phase difference, is the antenna element spacing, is the signal frequency.

[0014] Preferably, the frequency band selection and optimization algorithm dynamically selects the best signal frequency band according to the signal strength and environmental conditions. The frequency band selection and optimization algorithm is based on the frequency band selection matrix and signal quality evaluation, and selects the best frequency band through matrix calculation and evaluation function. The frequency band selection matrix calculation formula is: ; in, Select the matrix for the frequency band, Indicates The frequency band is Signal strength under different environmental conditions, The evaluation function is: ; in, It is for Signal quality evaluation value of each frequency band; It is The weight of the environmental conditions, Indicates The frequency band is signal strength under different environmental conditions.

[0015] Preferably, in S8, quantum communication technology is introduced, and the specific steps include: S8.1: Design quantum communication systems, including quantum key distribution (QKD) modules and quantum communication channels; S8.2: Generate a secure key using a quantum key distribution module to encrypt the positioning result data; S8.3: Send the encrypted data to the ground control center via the quantum communication channel.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Through the gradual processing of three-stage amplification, the signal gain and noise control can be effectively balanced. The first-stage high-gain amplifier increases the signal, but may amplify the noise. The second-stage low-noise amplifier effectively controls the noise and ensures that the signal-to-noise ratio is improved. The third-stage bandpass filter amplifier further filters out irrelevant frequencies and improves the purity of the signal.

[0017] 2. By accurately distinguishing the main lobe signal from the side lobe signal, the system can avoid the interference of the side lobe signal on the positioning accuracy. The direction and strength characteristics of the main lobe signal make it more reliable and can ensure higher positioning accuracy. Especially in complex environments, the dual determination of signal strength and direction can effectively filter out errors and ensure the positioning accuracy of the system.

[0018] 3. The adaptive beamforming algorithm can not only effectively enhance the strength of the target signal by dynamically adjusting the weight vector, but also suppress interference from other directions, thereby improving the positioning accuracy, anti-interference ability and stability of the system in complex electromagnetic environments.

[0019] 4. The spectrum peak search of the MUSIC algorithm can provide higher directional resolution than traditional methods by suppressing the noise subspace. Even if the angles between the signal sources are close, the MUSIC algorithm can still accurately distinguish their directions.

[0020] 5. The position solution and correction algorithm effectively integrates the observation data of each sensor through the least squares method, providing an accurate, efficient and stable positioning solution, which is particularly suitable for the application requirements of the radio frequency signal microwave array precise positioning system.

[0021] 6. Quantum communication uses quantum entanglement and quantum key encryption to ensure that data cannot be tampered with during transmission. Any attempt to tamper with the data will be detected and exposed in real time by the quantum communication system, thereby ensuring the integrity of the transmitted data. This is crucial for high-precision positioning systems, which can prevent data from being maliciously modified and ensure the authenticity and reliability of positioning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0024] See also Figure 1 The embodiment of the present invention provides a microwave array precise positioning method based on radio frequency signals, comprising the following steps: S1: Install a BDS / GPS / GLONASS multi-constellation receiver on a GEO spacecraft to receive navigation satellite signals from the BDS, GPS and GLONASS systems; A high-sensitivity GNSS receiver with multi-constellation reception capability is installed on the GEO spacecraft. The receiver can receive navigation satellite signals from the BDS, GPS and GLONASS systems. The receiver antenna is installed on the outer surface of the spacecraft and can cover a reception range of 9.18°~39°. S2: Preprocess the received GNSS signal, including removing atmospheric loss, multipath effect and noise interference, and amplifying the signal; S3: using the signal detection algorithm to distinguish the main lobe signal from the side lobe signal, and classify the signal strength into strong signals and weak signals; S3.1: Calculate the signal-to-noise ratio (SNR) of each received signal; S3.2: According to a preset threshold, a signal with an SNR greater than the threshold is classified as a main lobe signal, and a signal with an SNR less than the threshold is classified as a side lobe signal; S3.3: Classify the main lobe signal and side lobe signal by strength, into strong signals (SNR>20 dB) and weak signals (SNR ≤ 20 dB) S4: Enhance weak signals by using a signal enhancement algorithm, adaptive beamforming method and spatial filtering technology to improve the signal-to-noise ratio of weak signals; S5: fusing the main lobe signal and the side lobe signal, the strong signal and the weak signal using a multi-signal fusion algorithm; S5.1: Inputting the measured values ​​of the main lobe signal and the side lobe signal, the strong signal and the weak signal into the Kalman filter; S5.2: The Kalman filter generates the optimal navigation solution through state prediction and measurement update; S6: Install a microwave array antenna on the spacecraft, use the signals received by the microwave array antenna for precise positioning, and use the microwave array signal processing algorithm to determine the signal source position. The microwave array signal processing algorithm includes the multiple signal classification (MUSIC) algorithm and the direction of arrival estimation (DOA) algorithm; S6.1: Estimate the direction of arrival of the signal using the MUSIC algorithm; S6.2: Calculate the phase difference of multiple antenna units using the DOA algorithm to determine the precise location of the signal source; S7: The microwave array positioning result is integrated with the positioning result of the multi-constellation joint navigation system using a position solution and correction algorithm; S7.1: Input the positioning result of the microwave array and the positioning result of the multi-constellation joint navigation system into the extended Kalman filter; S7.2: The extended Kalman filter generates the optimal positioning solution through state prediction and measurement update; S8: The positioning result is transmitted to the ground control center through the data transmission module. The ground control center processes and analyzes the received data and adjusts the antenna beam direction in real time through the adaptive direction adjustment algorithm; S9: Based on the real-time monitored signal strength and positioning accuracy, the segment selection and optimization algorithm is used to dynamically select the best signal frequency band.

[0025] In one possible implementation, S1 and S2 are the signal reception and preprocessing stages of the positioning method. First, a multi-constellation GNSS receiver is installed on the GEO spacecraft to receive navigation signals from the BDS, GPS, and GLONASS systems (S1). The received signals are preprocessed (S2) to remove atmospheric loss, multipath effects, and noise interference, and enhance signal quality, which provides better signal quality for subsequent signal detection and classification.

[0026] Furthermore, S3 and its sub-steps (S3.1 to S3.3) are the signal detection and classification stage. The main lobe signal and the side lobe signal are distinguished by the signal detection algorithm, and the signal strength is classified by the signal-to-noise ratio (SNR) value. The signal with an SNR value greater than the threshold is determined as the main lobe signal, and vice versa. The classification of signal strength not only improves the accuracy of signal quality assessment, but also provides a basis for subsequent enhancement processing.

[0027] Furthermore, S4 enhances weak signals and uses adaptive beamforming and spatial filtering technology to improve the signal-to-noise ratio of weak signals. By enhancing weak signals, the positioning accuracy of the signal is significantly improved, especially in weak signal environments, ensuring the robustness of the system.

[0028] Furthermore, in S5, main lobe and side lobe signals, strong signals and weak signals are fused through a multi-signal fusion algorithm (such as Kalman filtering). The Kalman filter provides the optimal navigation solution for signal prediction and update, thereby further improving positioning accuracy and stability.

[0029] Furthermore, S6 is the core step of microwave array positioning, which uses the signals received by the microwave array antenna for precise positioning. The direction of arrival of the signal is estimated by the MUSIC algorithm (S6.1), and then the phase difference of multiple antenna units is calculated using the DOA algorithm to determine the precise location of the signal source (S6.2). This method can effectively improve positioning accuracy, especially in complex signal environments with strong adaptability.

[0030] Furthermore, in S7, the positioning results of the microwave array are integrated with the positioning results of the multi-constellation joint navigation system. The positioning results are further optimized by performing state prediction and measurement update (S7.1 and S7.2) through the extended Kalman filter. The use of the extended Kalman filter can adjust and correct the positioning solution in real time, improving the dynamic performance of the positioning system.

[0031] Furthermore, S8 and S9 provide a mechanism for outputting and adjusting positioning results in real time. The positioning results are transmitted to the ground control center through the data transmission module. The control center can process and analyze the positioning results and dynamically adjust the antenna beam direction (S8) through the adaptive direction adjustment algorithm according to the real-time signal strength and positioning accuracy. Through the segment selection and optimization algorithm, the system can dynamically select the best signal frequency band (S9) to optimize the performance of the system, especially in the case of frequency band congestion or unstable signal, to ensure signal quality and positioning accuracy.

[0032] By combining the multi-constellation navigation system and microwave array signal processing technology, high-precision positioning can be achieved in complex signal environments, especially in high-orbit environments such as GEO spacecraft, which can provide higher accuracy than traditional navigation methods. At the same time, the use of adaptive beamforming and spatial filtering technology effectively enhances weak signals and suppresses noise interference, allowing the system to work stably in complex environments.

[0033] In one possible implementation, first, the atmospheric loss correction provides compensation for signal propagation loss, providing a more accurate signal for subsequent processing steps. Next, the multipath effect correction further eliminates the phase error caused by multipath propagation and enhances the stability of the signal. Then, through the noise interference removal technology, the purity of the signal is improved, eliminating the noise components that may affect the positioning accuracy. Finally, the signal gain amplification ensures that the signal strength is sufficient, so that the receiver can still obtain reliable signals in complex environments.

[0034] Specifically, the signal will be affected by the atmosphere during propagation, especially the changes in air pressure, temperature and altitude will cause signal attenuation. In order to compensate for these effects, the atmospheric model and measured data are used to calculate the atmospheric loss correction value ΔP through a correction algorithm. ; in, is the atmospheric loss correction value, P is the air pressure, T is the temperature, H is the altitude, , , is the correction coefficient; this process is crucial to improving the quality of the signal, because the attenuation of the atmosphere will lead to a decrease in positioning accuracy. After the correction, the signal loss is effectively compensated and the signal quality is improved, laying a good foundation for subsequent multipath effect correction and noise removal.

[0035] Furthermore, GNSS signals may encounter multipath effects, that is, the signals reach the receiver through different paths, causing changes in signal phase and delay, affecting positioning accuracy. In order to reduce this error, a signal multipath detection algorithm is used to calculate the signal phase offset and correct it. ; in, is the phase shift, is the speed of light, is the signal frequency, is the time delay; through this process, the signal phase error caused by multipath propagation can be effectively identified and compensated, positioning error can be reduced, and signal quality can be further improved. This step is performed after the aforementioned atmospheric loss correction, which is a further optimization of signal quality.

[0036] Furthermore, in the process of receiving signals, noise is a common problem that affects the signal quality. In order to remove the noise interference in the signal, a low-pass filter and an adaptive filter are used for signal processing. The transfer function of the low-pass filter is ; Through this filter, high-frequency noise components in the signal can be removed and low-frequency effective signals can be retained. The adaptive filter adapts to signal changes by dynamically adjusting the filter weights. Its update formula is: ; in, is the filter weight vector, is the input signal vector, is the step length, The adaptive filter continuously adjusts its parameters according to the input signal and the error signal to remove the noise component in real time and ensure the purity of the signal. This process cooperates with the atmospheric loss correction and multipath effect correction in the first two steps to ensure that the signal is affected by external noise as much as possible and further improve the reliability of the signal.

[0037] Finally, the signal after atmospheric correction, multipath correction and noise removal may become weaker due to attenuation during propagation. In this case, the gain amplifier is used to amplify the signal to ensure that the signal strength is sufficient for subsequent positioning calculations. The formula for gain amplification is: ; in, is the output voltage, is the input voltage, is the magnification.

[0038] By adjusting the gain multiple, the signal amplitude is increased, thereby enhancing the signal availability and ensuring that the receiver can obtain a signal of sufficient strength for accurate positioning calculations.

[0039] Through the orderly implementation of these steps, various error sources in the signal propagation process can be effectively reduced, the signal quality can be greatly improved, and the basis for high-precision GNSS positioning systems can be provided. These preprocessing steps enhance the robustness of the system in complex environments, especially in the case of weak signals or large noise, and can ensure the accuracy and reliability of positioning calculations.

[0040] In the embodiment of the present invention, in S2.3, a multi-stage gain amplifier is used to improve the signal strength through multi-stage amplification. In a possible implementation, first, after the signal is preliminarily processed, it enters the first-stage amplification stage and a high gain amplifier (HGA) is used to preliminarily amplify the signal. The formula is: ; A high-gain amplifier usually has a large gain capability and can amplify the received weak signal to a certain strength for subsequent processing, but it may amplify some noise, so further noise suppression is required.

[0041] Furthermore, after the first level of amplification, the signal enters the second level of amplification, where a low noise amplifier is used for secondary amplification. The low noise amplifier can further enhance the signal strength while maintaining the signal quality. The formula is: The main feature of a low noise amplifier is that it can suppress the noise introduced by a high gain amplifier while amplifying the signal, thereby ensuring that the signal-to-noise ratio (SNR) of the signal is improved. This process is particularly important for microwave signals, because the signal attenuation is large and the noise may affect the positioning accuracy.

[0042] Finally, the signal enters the third-stage amplification stage, where a bandpass filter amplifier is used. This amplifier not only has a gain function, but also can filter out unnecessary frequency components. The formula is: The role of the bandpass filter amplifier is to ensure that the signal contains only effective frequency components through the frequency selectivity of the bandpass filter, remove possible stray signals and interference, and improve the purity of the signal, thereby further optimizing the strength and quality of the signal in the final stage.

[0043] Through the gradual processing of three-stage amplification, the signal gain and noise control can be effectively balanced. The first-stage high-gain amplifier increases the signal, but may amplify the noise. The second-stage low-noise amplifier effectively controls the noise to ensure that the signal-to-noise ratio is improved. The third-stage bandpass filter amplifier further filters out irrelevant frequencies and improves the purity of the signal. The coordination between the amplifiers at each stage ensures the stability of the signal and avoids the over-amplification or signal distortion that may be caused by a single amplifier. Through multi-stage amplification, it can ensure that the signal is always within the appropriate intensity range and will not be over-attenuated or saturated.

[0044] in, is the output voltage of the first stage amplifier, is the output voltage of the secondary amplifier, is the output voltage, is the input voltage, is the first-order magnification, is the secondary magnification, It is the third level of magnification.

[0045] 5. In the embodiment of the present invention, in S3, the algorithm for distinguishing the main lobe signal from the side lobe signal is based on the characteristics of signal strength and signal direction. The main lobe signal is directed toward the navigation satellite and has a higher signal strength. The side lobe signal is a signal in a non-main lobe direction and has a weaker signal strength. The formula for distinguishing the main lobe signal from the side lobe signal is: Main lobe signal: ; Side lobe signal: Where S is the signal strength, is the signal strength threshold, is the launch direction of the navigation satellite, is the angle at which the signal arrives at the microwave array, and ∧ represents the logical “AND” operator, which is used to connect two conditions.

[0046] Furthermore, after the signal strength meets the threshold condition, the signal direction is determined. The direction of the signal can be determined by the spatial distribution of the microwave array. If the receiving direction of the signal is within the range of ±3° of the transmitting direction of the navigation satellite, the signal is determined to be a main lobe signal; if the signal direction is not within this range, it is determined to be a side lobe signal. The main lobe direction is usually the direction close to the navigation satellite, and the precise control of the signal direction ensures the accurate reception of the navigation satellite signal.

[0047] Further, according to the above steps, all signals that meet the strength and direction requirements are classified as main lobe signals, which have higher strength and more accurate direction information; while other signals that do not meet the conditions are classified as side lobe signals. Side lobe signals usually come from non-main lobe directions and have weaker strength, so they have less impact on the positioning system. Finally, the system will perform positioning calculations based on the main lobe signals, and the side lobe signals will be ignored or suppressed.

[0048] The signal differentiation algorithm can accurately identify the main lobe signal and filter out the side lobe signal interference by effectively combining the signal strength and direction information, thereby improving the accuracy, stability and anti-interference ability of the microwave array positioning system.

[0049] In a possible implementation, in step S4, the adaptive beamforming algorithm achieves signal enhancement and interference suppression in the microwave array system by adjusting the weight of the antenna array. The core of this process is to update the weight vector of the antenna array so that the received signal can be enhanced in a specific direction (the main signal direction) while suppressing interference signals from other directions. The weight update formula of the algorithm is as follows: ;in, is the weight vector, is the step size parameter, is the error signal, is the input signal vector, Represents the updated weight.

[0050] Specifically, at the beginning of the algorithm, an initial weight vector is first set, which can usually be a zero vector or a vector obtained based on some preliminary estimation. The selection of the initial weight may affect the convergence speed and effect of the adaptive process. Furthermore, for the received signal at each moment, the error signal is calculated according to the target direction (main signal direction), that is, the difference between the expected signal and the currently received signal. The error signal reflects the deviation between the received signal and the ideal signal. When the error signal is large, it indicates that the current weight has not effectively aligned the signal with the main signal direction.

[0051] Furthermore, the weight vector is updated according to the above formula based on the error signal and the input signal vector. The step size parameter here controls the rate of weight update. If the step size is too large, it may lead to over-adjustment and affect the stability of the system; if the step size is too small, the update process may be too slow, affecting the convergence speed of the algorithm. By continuously adjusting the weight vector, the signal enhancement direction will gradually align with the main signal direction, while the signals (interference) in other directions will be suppressed. Specifically, the input signal corresponding to the direction of the main signal contributes more to the update of the weight, thereby enhancing the signal strength in that direction. The signals from other directions, by continuously adjusting the weights, obtain a smaller gain or are directly suppressed, thereby achieving the effect of interference suppression.

[0052] The adaptive beamforming algorithm can not only effectively enhance the strength of the target signal by dynamically adjusting the weight vector, but also suppress interference from other directions, thereby improving the positioning accuracy, anti-interference ability and stability of the system in complex electromagnetic environments.

[0053] In the embodiment of the present invention, in a possible implementation, step S6 implements high-precision positioning of the signal through the MUSIC algorithm. The MUSIC algorithm is a high-resolution direction estimation algorithm using array signal processing, which can accurately estimate the direction of the signal source by performing feature decomposition and spectrum peak search on the received signal.

[0054] Specifically, in a microwave array system, the received signal is a superposition of signals from multiple directions and is received by the antenna array. The direction of each signal source corresponds to a specific steering vector, that is, the response vector of the antenna array in that direction. The steering vector usually depends on the geometric structure of the array and the incident angle of the signal.

[0055] The MUSIC algorithm first calculates the covariance matrix of the received signal, which reflects the distribution characteristics of the signal and noise. The covariance matrix can be estimated by the received signal samples. Then, eigendecomposition is performed to decompose the covariance matrix into a signal subspace and a noise subspace. The noise subspace consists of the eigenvectors corresponding to the smallest eigenvalue of the covariance matrix, and the signal subspace consists of the eigenvectors corresponding to the larger eigenvalues.

[0056] The spectrum peak search formula of the MUSIC algorithm is: ; in, is the steering vector of the signal, is the eigenvector of the noise subspace, is the number of antenna array elements, is the number of signals, and i is the number of antenna elements minus the index of the number of signals.

[0057] In the spectrum peak search, the MUSIC algorithm calculates the spectrum values ​​corresponding to different directions and finds the direction corresponding to the maximum spectrum value, thereby realizing high-precision positioning of the signal source. Since the eigenvector in the noise subspace is orthogonal to the steering vector of the signal, the MUSIC algorithm can effectively suppress noise and highlight the directional characteristics of the signal. In step S6, the MUSIC algorithm is closely integrated with the beamforming algorithm in the previous step. The beamforming step provides the system with preliminary enhancement of the signal, while the MUSIC algorithm further accurately estimates the incident angle of the signal through eigendecomposition and spectrum peak search. Specifically, beamforming enhances the target signal through adaptive adjustment of the antenna array, and the MUSIC algorithm uses the eigenspace of this enhanced signal to further determine the exact direction of the signal source. This combination ensures that the system can both effectively enhance the signal and accurately locate the signal source.

[0058] In the embodiment of the present invention, in S7, the position solution and correction algorithm is: ; in, is the estimated position, is the design matrix, is the observation vector, Design matrix The transpose of .

[0059] In one possible implementation, the design matrix is ​​usually composed of the positions and measurement information of multiple sensors. In the microwave array positioning system, the design matrix represents the geometric relationship between different sensors and the target object, such as the position of each sensor and the signal propagation path. Each row contains parameters related to the position of a sensor, usually including the coordinates of the sensor, the signal propagation speed, and other measurement-related factors. Each element of the matrix can be calculated by relevant physical models (such as time difference, arrival angle, etc.).

[0060] Furthermore, the observation vector contains the measurement information received from different sensors. This information may be calculated by time delay, signal strength or distance difference. For example, when using techniques such as time difference of arrival (TDOA), each element in the observation vector represents the time difference between the signal sent by one sensor and the arrival of another sensor. All these observations provide linear constraints on the target position.

[0061] Furthermore, the least squares method optimizes the estimation of the target position by minimizing the error between the observed value and the theoretical value. Through the least squares method, the actual position of the target can be accurately estimated when the positions and observation data of multiple sensors are known. This method can integrate the observation information of multiple sensors and reduce the influence of the error of a single sensor, thereby obtaining a more accurate positioning result.

[0062] In the embodiment of the present invention, in S8, the adaptive direction adjustment algorithm adjusts the beam direction of the antenna in real time, and the adaptive direction adjustment algorithm adjusts the beam direction by adjusting the phase of the antenna array element; In a possible implementation, the adaptive direction adjustment algorithm mainly controls the direction of the beam based on the phase difference. Specifically, by adjusting the phase difference between each array element in the antenna array, the beam direction of the antenna array can be changed. The beam direction adjustment formula is: ; in, is the beam direction, is the speed of light, is the phase difference, is the antenna element spacing, is the signal frequency.

[0063] During the positioning process, the antenna array receives radio frequency signals from the target object. In order to accurately capture the target position, the array needs to adjust the direction of the signal. By measuring the phase difference of the received signal in real time, combined with the pre-set array configuration (array element spacing, frequency and other parameters), the current phase difference is calculated, and then the array beam direction is adjusted using the above formula. The change in phase difference can be adjusted by adjusting the phase of each array element, so that the antenna array beam is directed toward the target.

[0064] Since the target object may be in motion or in dynamic changes in the environment, traditional static antenna arrays often cannot achieve continuous accurate positioning. The adaptive direction adjustment algorithm can track the changes of the target in real time, and by dynamically adjusting the beam direction, ensure that the antenna array can always accurately receive the signal from the target and effectively reduce the error caused by interference or signal attenuation.

[0065] In a possible implementation, the frequency band selection and optimization algorithm plays a critical role in the microwave array precise positioning method based on radio frequency signals. The algorithm dynamically selects the most suitable signal frequency band to ensure that the system can obtain the best signal strength under different environmental conditions, thereby improving positioning accuracy and stability.

[0066] Specifically, first, the algorithm will build a frequency band selection matrix based on different environmental conditions (such as multipath effect, noise interference, obstacle distribution, etc.) ; ; in, Select the matrix for the frequency band, Indicates The frequency band is Signal strength under different environmental conditions, The evaluation function is: ; in, It is for Signal quality evaluation value of each frequency band; It is The weight of the environmental conditions, Indicates The frequency band is signal strength under different environmental conditions.

[0067] By weighted summation, the impact of multiple environmental factors on the signal quality of each frequency band can be comprehensively considered to obtain an evaluation value for each frequency band.

[0068] Finally, by comparing the evaluation values ​​of different frequency bands, the frequency band with the highest evaluation value is selected as the optimal frequency band. The selection process is dynamic, and the optimal frequency band is continuously adjusted according to different environmental conditions (such as signal strength changes, noise levels, etc.), so as to achieve the best performance of the system under different conditions.

[0069] The frequency band selection and optimization algorithm can significantly improve the performance, accuracy and robustness of the positioning system by dynamically adjusting the frequency band, weighted signal strength and environmental conditions. Especially in complex environments, this algorithm can effectively deal with problems of different signal qualities and ensure the stable operation of the system under various conditions.

[0070] In an embodiment of the present invention, in S8, quantum communication technology is introduced, and the specific steps include: S8.1: designing a quantum communication system, including a quantum key distribution (QKD) module and a quantum communication channel; S8.2: using the quantum key distribution module to generate a secure key to encrypt the data of the positioning result; S8.3: sending the encrypted data to the ground control center through the quantum communication channel.

[0071] In one possible implementation, a quantum communication system needs to be designed and implemented first. The quantum communication system mainly includes two core modules: quantum key distribution (QKD) module and quantum communication channel. The role of the quantum key distribution module is to use the uncertainty principle in quantum mechanics to generate a secure key between the sender and the receiver. This key cannot be stolen or intercepted physically, because any measurement of the quantum state will destroy the quantum state itself, so that the communicating parties can immediately detect the eavesdropping. The quantum key distribution module can generate a completely secure key, so that the positioning data is strongly encrypted during transmission. Compared with traditional encryption technology, quantum communication technology is almost impossible to crack because quantum keys are unclonable during transmission, and any attempt to steal or intercept data will be immediately detected.

[0072] Furthermore, the quantum key distribution module generates a key for encryption according to the quantum communication protocol. This key is random and unique, and only the communicating parties have access to the key. After the key is generated, the key is used to encrypt the data in the positioning system. In this way, the transmitted positioning result data can be encrypted by quantum key to prevent it from being intercepted or decoded midway. The characteristics of quantum communication enable it to exhibit extremely strong anti-interference capabilities when facing external electromagnetic interference or network attacks. In traditional communications, signals may be tampered with, eavesdropped or interfered, but quantum communication can effectively avoid these problems through the transmission of quantum states. Even in harsh signal environments, quantum communication channels can ensure the reliable transmission of data and the accuracy and integrity of positioning results.

[0073] Furthermore, once the data is encrypted, it is sent to the ground control center through a quantum communication channel. The quantum communication channel uses the principles of quantum entanglement and quantum state transmission to establish a highly secure communication link between the sender and the receiver. Due to the special nature of quantum communication, any external interference or monitoring will be immediately exposed, thus ensuring the security of the encrypted data during transmission. After receiving the encrypted data, the ground control center decrypts the data using the previously shared quantum key and performs subsequent processing or analysis on it.

[0074] Quantum communication uses quantum entanglement and quantum key encryption to ensure that data cannot be tampered with during transmission. Any attempt to tamper with the data will be detected and exposed in real time by the quantum communication system, thereby ensuring the integrity of the transmitted data. This is crucial for high-precision positioning systems, which can prevent data from being maliciously modified and ensure the authenticity and reliability of positioning results.

[0075] The introduction of quantum communication technology enables the positioning system to not only provide high-precision positioning services, but also establish a higher level of trust in data security. Whether in the civilian or military fields, data security is an important part of system reliability. Through quantum encryption technology, it can ensure that the system can still provide positioning services safely and stably even in the event of attacks or interference.

[0076] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A microwave array precise positioning method based on radio frequency signals, characterized in that: The steps include: S1: Install a BDS / GPS / GLONASS multi-constellation receiver on a GEO spacecraft to receive navigation satellite signals from the BDS, GPS and GLONASS systems; A high-sensitivity GNSS receiver with multi-constellation reception capability is installed on the GEO spacecraft. The receiver can receive navigation satellite signals from the BDS, GPS and GLONASS systems, and the antenna of the receiver is installed on the outer surface of the spacecraft. S2: Preprocess the received GNSS signal, including removing atmospheric loss, multipath effect and noise interference, and amplifying the signal; S3: using the signal detection algorithm to distinguish the main lobe signal from the side lobe signal, and classify the signal strength into strong signals and weak signals; S3.1: Calculate the signal-to-noise ratio (SNR) of each received signal; S3.2: According to a preset threshold, a signal with an SNR greater than the threshold is classified as a main lobe signal, and a signal with an SNR less than the threshold is classified as a side lobe signal; S3.3: Classify the main lobe signal and side lobe signal by strength, into strong signal SNR > 20 dB and weak signal SNR ≤ 20 dB; S4: Enhance weak signals by using a signal enhancement algorithm, adaptive beamforming method and spatial filtering technology to improve the signal-to-noise ratio of weak signals; S5: fusing the main lobe signal and the side lobe signal, the strong signal and the weak signal using a multi-signal fusion algorithm; S5.1: Inputting the measured values ​​of the main lobe signal and the side lobe signal, the strong signal and the weak signal into the Kalman filter; S5.2: The Kalman filter generates the optimal navigation solution through state prediction and measurement update; S6: Install a microwave array antenna on the spacecraft, use the signals received by the microwave array antenna for precise positioning, and use the microwave array signal processing algorithm to determine the signal source position. The microwave array signal processing algorithm includes the multiple signal classification MUSIC algorithm and the direction of arrival estimation DOA algorithm; S6.1: Estimate the direction of arrival of the signal using the MUSIC algorithm; S6.2: Calculate the phase difference of multiple antenna units using the DOA algorithm to determine the precise location of the signal source; S7: The microwave array positioning result is integrated with the positioning result of the multi-constellation joint navigation system using a position solution and correction algorithm; S7.1: Input the positioning result of the microwave array and the positioning result of the multi-constellation joint navigation system into the extended Kalman filter; S7.2: The extended Kalman filter generates the optimal positioning solution through state prediction and measurement update; S8: The positioning result is transmitted to the ground control center through the data transmission module. The ground control center processes and analyzes the received data and adjusts the antenna beam direction in real time through the adaptive direction adjustment algorithm; S9: Based on the real-time monitored signal strength and positioning accuracy, the segment selection and optimization algorithm is used to dynamically select the best signal frequency band.

2. The microwave array precise positioning method based on radio frequency signals according to claim 1 is characterized in that: In S2, the received GNSS signal is preprocessed, including removing atmospheric loss, multipath effect and noise interference, and gain amplifying the signal. The specific steps are as follows: S2.1: Use the atmospheric model and measured data to correct the atmospheric loss using the correction algorithm: ； in, is the atmospheric loss correction value, P is the air pressure, T is the temperature, H is the altitude, , , is the correction factor; S2.2: Use the signal multipath detection algorithm to identify and correct the multipath effect. The signal multipath detection algorithm is: ； in, is the phase shift, is the speed of light, is the signal frequency, For time delay; S2.3: Use low-pass filter and adaptive filter to remove noise interference in the signal. The transfer function of the low-pass filter is: ； The update formula of the adaptive filter is: ； in, is the filter weight vector, is the input signal vector, is the step length, is the error signal; S2.4: Use an amplifier to amplify the signal. The formula is: ； in, is the output voltage, is the input voltage, is the magnification.

3. The microwave array precise positioning method based on radio frequency signals according to claim 2 is characterized in that: In S2.3, a multi-stage gain amplifier is used to increase the signal strength through multi-stage amplification. The specific steps are: S2.3.1: First-stage amplification, using a high-gain amplifier for preliminary amplification, the formula is; ； S2.3.2: Secondary amplification, using a low noise amplifier for secondary amplification, the formula is; ； S2.3.3: Three-stage amplification, using a bandpass filter amplifier for final amplification, the formula is; ； in, is the output voltage of the first stage amplifier, is the output voltage of the secondary amplifier, is the output voltage, is the input voltage, is the first-order magnification, is the secondary magnification, It is the third level of magnification.

4. The microwave array precise positioning method based on radio frequency signals according to claim 1 is characterized in that: In S3, the algorithm for distinguishing the main lobe signal from the side lobe signal is based on the characteristics of signal strength and signal direction. The main lobe signal is directed toward the navigation satellite and has a higher signal strength. The side lobe signal is a signal in a non-main lobe direction and has a weaker signal strength. The formula for distinguishing the main lobe signal from the side lobe signal is: Main lobe signal: ; Side lobe signal: Where S is the signal strength, is the signal strength threshold, is the launch direction of the navigation satellite, is the angle at which the signal arrives at the microwave array, and ∧ represents the logical "AND" operator, which is used to connect two conditions.

5. The microwave array precise positioning method based on radio frequency signals according to claim 1 is characterized in that: In S4, the adaptive beamforming algorithm achieves signal enhancement and interference suppression by adjusting the weight of the antenna array. The weight update formula is: ; in, is the weight vector, is the step size parameter, is the error signal, is the input signal vector, Represents the updated weight.

6. The microwave array precise positioning method based on radio frequency signals according to claim 1, characterized in that: In S6, the MUSIC algorithm achieves high-precision positioning of the signal through feature decomposition and spectrum peak search. The spectrum peak search formula of the MUSIC algorithm is: ; in, is the steering vector of the signal, is the eigenvector of the noise subspace, is the number of antenna array elements, is the number of signals, and i is the number of antenna elements minus the index of the number of signals.

7. The microwave array precise positioning method based on radio frequency signals according to claim 1, characterized in that: In S7, the position solution and correction algorithm is: ; in, is the estimated position, is the design matrix, is the observation vector, Design matrix The transpose of .

8. The microwave array precise positioning method based on radio frequency signals according to claim 1 is characterized in that: In S8, the adaptive direction adjustment algorithm adjusts the beam direction of the antenna in real time. The adaptive direction adjustment algorithm adjusts the beam direction by adjusting the phase of the antenna array element. The beam direction adjustment formula is: ; in, is the beam direction, is the speed of light, is the phase difference, is the antenna element spacing, is the signal frequency.

9. The microwave array precise positioning method based on radio frequency signals according to claim 1, characterized in that: The frequency band selection and optimization algorithm dynamically selects the best signal frequency band according to the signal strength and environmental conditions. The frequency band selection and optimization algorithm is based on the frequency band selection matrix and signal quality evaluation. It selects the best frequency band through matrix calculation and evaluation function. The frequency band selection matrix calculation formula is: ; in, Select the matrix for the frequency band, Indicates The frequency band is Signal strength under different environmental conditions, The evaluation function is: ; in, It is for Signal quality evaluation value of each frequency band; It is The weight of the environmental conditions, Indicates The frequency band is signal strength under different environmental conditions.

10. The microwave array precise positioning method based on radio frequency signals according to claim 1, characterized in that: In S8, quantum communication technology is introduced. The specific steps include: S8.1: Design a quantum communication system, including a quantum key distribution (QKD) module and a quantum communication channel; S8.2: Generate a secure key using a quantum key distribution module to encrypt the positioning result data; S8.3: Send the encrypted data to the ground control center via the quantum communication channel.

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