Air defense and disaster prevention early warning alarm real-time control system based on Beidou third-generation communication

Abstract

The invention relates to the field of communication signal processing, in particular to an air defense and disaster prevention early warning alarm real-time control system based on Beidou third-generation communication, which adopts a Daubechies wavelet family to perform five-layer discrete wavelet decomposition and decomposes the total delay of satellite signals into different scale coefficients. By accurately controlling the boundary scale, the system can effectively separate the ionosphere error from the troposphere error. And by inserting a zero value between filter coefficients, information loss caused by down-sampling operation is avoided. And aiming at different atmospheric error characteristics, the system adaptively selects an optimal wavelet basis function. A stable correction effect is kept under extreme weather conditions, and an efficient and practical air defense and disaster prevention real-time early warning control method is constructed.

Description

Technical Field

[0001] The present invention relates to the fields of communication signal processing, atmospheric error correction and real-time early warning control, and in particular to a real-time control system for air defense and disaster prevention early warning alarms based on Beidou-3 communication. Background Art

[0002] In modern information-based combat environments and disaster warning systems, precise positioning, navigation, and time synchronization services are essential for achieving precise prevention and control. However, the atmospheric environment (including the ionosphere and troposphere) causes propagation delays, attenuation, and scattering of satellite navigation signals, severely impacting positioning accuracy and communication quality. When high-precision satellite navigation systems travel through the atmosphere, the signal propagation path and speed are affected by atmospheric media. In particular, free electrons in the ionosphere and variations in water vapor content in the troposphere can cause significant propagation delays, leading to increased positioning errors. In military air defense and natural disaster warning systems, such errors can lead to target identification and tracking failures, or miss critical warning opportunities, resulting in serious consequences.

[0003] Existing atmospheric error modeling methods are mostly based on static or semi-static models, which make it difficult to accurately describe the spatiotemporal variations of atmospheric parameters. Model accuracy decreases significantly, especially under conditions of drastic weather changes. For example, while the International Reference Ionosphere Model (IRI) can describe the global distribution of the ionosphere, its temporal resolution is only at the hourly level, making it difficult to capture the rapid changes in ionospheric disturbances. Although the Numerical Tropospheric Prediction Model (NWP) incorporates detailed physical processes, its high computational complexity makes it difficult to meet the requirements of real-time corrections.

[0004] Research has shown that Multiple-Input Multiple-Output (MIMO) technology can significantly improve channel capacity and transmission reliability through spatial diversity and multiplexing. Wavelet transforms, due to their excellent time-frequency localization properties, offer unique advantages in non-stationary signal analysis and denoising. The BeiDou-3 system provides high-precision positioning and short message communication services with global coverage, possessing inherent dual-use characteristics for both military and civilian use. MIMO technology, through multi-antenna transmission and reception, creates multiple parallel channels in the spatial domain, theoretically increasing channel capacity by a factor of N (where N is the number of antennas). Wavelet transforms, through scaling and translation operations, provide multi-resolution analysis, effectively capturing both local signal characteristics and global trends. BeiDou-3, my country's independently developed global satellite navigation system, provides multiple frequency signals (B1I / B3I / B1C / B2a / B2b), providing ample observation data for ionospheric error correction and precise positioning.

[0005] Therefore, the organic combination of MIMO technology, wavelet transform theory and BeiDou-3 communication system solves the technical problems of insufficient accuracy, poor adaptability and weak real-time performance of traditional single signal processing methods in complex atmospheric environments. By integrating MIMO antenna arrays, wavelet multi-resolution analysis and BeiDou-3 multi-frequency observations, accurate modeling and correction of atmospheric errors are achieved, providing all-weather, all-day high-precision navigation positioning and communication guarantees for air defense and disaster prevention applications. Summary of the Invention

[0006] The present invention aims to solve the following technical problems:

[0007] How to effectively integrate the technical advantages of MIMO technology, wavelet transform and BeiDou-3 communication system to achieve high-precision satellite navigation signal error correction in complex and changeable atmospheric environments, especially to maintain a stable correction effect under extreme weather conditions (such as severe thunderstorms, typhoons, geomagnetic storms, etc.) in order to build an efficient and practical real-time early warning and control method for air defense and disaster prevention.

[0008] This invention proposes a real-time control system for air defense and disaster prevention warning alarms based on BeiDou III communications. The solution includes the following technical contents:

[0009] The present invention adopts a distributed MIMO antenna array structure to simultaneously receive BeiDou satellite signals through multiple antennas to build a spatial diversity receiving system:

[0010] ;

[0011] in, Represents the received signal matrix, with dimension , is the number of receiving antennas, is the number of time samples; Represents the channel matrix, with dimension , is the number of transmitting antennas; Represents the sending signal matrix, with dimension ; Represents the additive white Gaussian noise matrix, with dimension .

[0012] For atmospheric propagation environment, channel matrix It can be further broken down into:

[0013] ;

[0014] in, Represents the spatial domain channel characteristics, describing the attenuation and phase shift of the signal during spatial propagation; Represents the channel characteristics in the time domain and describes the dynamic changes of the channel over time; Represents the frequency domain channel characteristics and describes the propagation differences of signals of different frequencies; Represents the signal polarization characteristics and describes the changes in the signal polarization state during propagation.

[0015] Spatial domain channel characteristics It can be further expressed as:

[0016] ;

[0017] in, Represents the transmission antenna To the receiving antenna The spatial propagation characteristics of represents the signal wavelength, Represents the transmission antenna To the receiving antenna distance.

[0018] Time domain channel characteristics It can be expressed based on Jake's model as:

[0019] ;

[0020] in, represent and The time-dependent properties of moments, represents the zero-order Bessel function, Represents the maximum Doppler shift.

[0021] Frequency domain channel characteristics It can be expressed as:

[0022] ;

[0023] in, Representative frequency and The frequency correlation characteristics between Represents the maximum delay spread.

[0024] Polarization channel characteristics It can be expressed as:

[0025] ;

[0026] in, 、 、 and represent the coupling coefficients between the horizontal-horizontal, horizontal-vertical, vertical-horizontal, and vertical-vertical polarization states, respectively.

[0027] The present invention adopts a coding scheme based on orthogonal space-time block code (OSTBC), and its coding matrix is satisfy:

[0028] ;

[0029] in, represent The conjugate transposed matrix of represents a constant, represent The identity matrix of order.

[0030] For a 2-transmit 2-receive MIMO system, the classic Alamouti coding is used:

[0031] ;

[0032] in, and represents the symbols sent in two consecutive time slots, represent The complex conjugate of .

[0033] For a 4-transmit 4-receive MIMO system, extended OSTBC coding is used:

[0034] ;

[0035] in, 、 、 and Represents the symbols transmitted in four consecutive time slots.

[0036] To adapt to the characteristics of Beidou satellite signals, this invention designs an adaptive modulation and coding scheme that dynamically adjusts the coding rate and modulation mode based on channel state information (CSI). The modulation mode can be adaptively selected from BPSK, QPSK, 8PSK to 16QAM, and the coding rate can be adaptively adjusted from 1 / 2 to 5 / 6. The adaptive modulation and coding selection method is as follows:

[0037] ;

[0038] in, Indicates the modulation method used and coding rate The transmission rate, Represents the signal-to-noise ratio The bit error rate below.

[0039] The present invention adopts discrete wavelet transform to perform multi-scale analysis on the received signal to separate the atmospheric error into ionospheric error and tropospheric error.

[0040] ;

[0041] in, Representative signal The wavelet coefficients of Represents the scale parameter, which indicates the degree of stretching or compression of the wavelet. represents the translation parameter, indicating the time position of the wavelet, Represents the complex conjugate of the wavelet basis function.

[0042] The fast algorithm for discrete wavelet transform can be expressed as a set of high-pass and low-pass filters:

[0043] ;

[0044] ;

[0045] in, Representative Tier The approximation coefficients, Representative Tier The detail coefficient, and Represent the wavelet low-pass and high-pass filter coefficients, respectively, satisfying the orthogonality condition:

[0046] ;

[0047] ;

[0048] ;

[0049] in, represents the Kronecker delta function, when The value is 1 when , otherwise it is 0.

[0050] The present invention uses the Daubechies wavelet family (db4-db20), the Symlet wavelet family (sym8-sym20), and the Coiflet wavelet family (coif3-coif5) as the main analysis tools, and adaptively selects the optimal wavelet basis according to different atmospheric error characteristics. The evaluation indicators for wavelet basis selection include:

[0051] ;

[0052] ;

[0053] ;

[0054] in, represents the energy concentration of wavelet coefficients, represents the sparsity of wavelet coefficients, represents the information entropy of the wavelet coefficients, represents the normalized energy distribution.

[0055] For BeiDou satellite signals, the ionospheric delay is estimated using the following formula:

[0056] ;

[0057] in, represents the ionospheric delay (unit: seconds), stands for total electron content (unit: TECU, ), Represents the signal frequency (unit: Hz), Represents the elevation angle to the satellite Related mapping functions.

[0058] Mapping Function The exact expression is:

[0059] ;

[0060] in, represents the radius of the Earth (about 6371km), Represents the height of maximum electron density in the ionosphere (about 350km).

[0061] For the tropospheric delay, the Saastamoinen model is used:

[0062] ;

[0063] in, represents the tropospheric delay (unit: meter), represents the zenith angle, represents atmospheric pressure (unit: hPa), represents temperature (unit: K), Represents water vapor pressure (unit: hPa).

[0064] Taking into account the altitude dependence of tropospheric delay, an altitude correction factor is introduced:

[0065] ;

[0066] in, represents the tropospheric delay at sea level, represents the height attenuation coefficient, Represents the station altitude (unit: km).

[0067] Atmospheric error separation algorithm based on wavelet transform delays satellite signals Breaks down to:

[0068] ;

[0069] in, Representative Layer wavelet detail coefficients, corresponding to the high-frequency components of atmospheric disturbances, Representative Layer wavelet approximation coefficient, corresponding to the low-frequency components of atmospheric disturbances, Represents the number of decomposition layers (usually 5-7 layers).

[0070] According to the physical meaning of wavelet coefficients of different scales, an ionosphere and troposphere error separation model is established:

[0071] ;

[0072] ;

[0073] in, represents the dividing scale between ionospheric and tropospheric errors (usually 2-3), Represents the proportion of ionospheric error in the low-frequency component (usually 0.6-0.8, dynamically adjusted according to season and geographical location).

[0074] To improve the accuracy of wavelet decomposition, the present invention uses the translation-invariant wavelet transform (TIWT) technique to eliminate the displacement variability in traditional wavelet transforms, making the decomposition results unaffected by the time position of the signal. Zero values are inserted between the wavelet filter coefficients to avoid downsampling operations:

[0075] ;

[0076] ;

[0077] in, and Representative Dilation filter coefficients of the layer:

[0078] ;

[0079] ;

[0080] To further improve the accuracy of atmospheric error separation, this paper proposes a fine-grained decomposition method based on wavelet packet transform (WPT), which subdivides the signal at both high and low frequency parts to form a complete binary structure. The WPT coefficient calculation formula is:

[0081] ;

[0082] ;

[0083] in, Representative Tier The wavelet packet coefficients of nodes, Represents the node position parameters.

[0084] Based on the WPT coefficient, a more refined atmospheric error separation model is constructed:

[0085] ;

[0086] ;

[0087] in, and The sets of wavelet packet nodes corresponding to the ionospheric and tropospheric errors are trained from historical data using machine learning methods.

[0088] The present invention makes full use of the B1I (1561.098MHz), B3I (1268.52MHz), B1C (1575.42MHz), B2a (1176.45MHz) and B2b (1207.14MHz) multi-frequency signals provided by the BeiDou III satellite system to construct the multi-frequency observation equation:

[0089] ;

[0090] ;

[0091] in, Representative receiver Receiving satellite of Frequency pseudorange observation value, represents the corresponding carrier phase observation value, represents the geometric distance, represents the speed of light, and represent the receiver and satellite clock errors, respectively, represent The wavelength of the frequency point, represents the whole-cycle ambiguity, and represent the pseudorange and carrier phase observation noise, respectively.

[0092] Geometric distance It can be further expressed as:

[0093] ;

[0094] in, Representative Satellite The coordinates of Representative receiver 's coordinates.

[0095] The ionosphere-free combination of the multi-frequency observation equation can be expressed as:

[0096] ;

[0097] ;

[0098] in, and represent the ionospheric-free combined observation values of pseudorange and carrier phase, and Represents two different frequencies.

[0099] The ionosphere-free combination eliminates the first-order ionospheric delay effect, but the high-order ionospheric effect still exists, and its influence can be expressed as:

[0100] ;

[0101] in, represents the second-order ionospheric delay effect (unit: meter), represents the strength of the Earth's magnetic field (unit: Tesla), Represents the angle between the signal propagation direction and the direction of the Earth's magnetic field.

[0102] To achieve high-precision positioning, precise point positioning (PPP) technology is used, using precise ephemeris and clock products to replace broadcast ephemeris. The PPP observation equation is:

[0103] ;

[0104] ;

[0105] in, represents the precise satellite clock error, represents the wet delay mapping function, represents the zenith wet delay, represents the carrier phase ambiguity term.

[0106] Considering that the BeiDou system includes satellites of different orbit types (GEO, IGSO, MEO), this paper proposes a differentiated weighting strategy:

[0107] ;

[0108] in, Representative Satellite The weight of represents the satellite elevation angle, Representative Satellite The carrier-to-noise ratio, Represents the reference carrier-to-noise ratio (usually 45dB-Hz).

[0109] To fully utilize the short message communication function of the BeiDou-3 system, this paper designs an efficient differential correction number broadcasting scheme, encoding the atmospheric error correction information into a compact binary message. The differential correction number message structure includes:

[0110] 1. Message header (4 bytes): Contains message type, time stamp, and region identifier;

[0111] 2. Ionospheric grid corrections (variable length): spherical harmonics are used to represent the global TEC distribution;

[0112] 3. Tropospheric grid correction number (variable length): uses regional tropospheric delay model parameters;

[0113] 4. Satellite orbit and clock corrections (variable length): provide precise ephemeris correction parameters;

[0114] 5. Checksum (2 bytes): CRC16 algorithm is used to ensure data integrity.

[0115] The ionospheric TEC distribution represented by the spherical harmonics model is:

[0116] ;

[0117] in, Represents latitude and longitude The total electron content at represents the normalized associated Legendre function, and represents the spherical harmonic coefficients, Represents the maximum order (usually 8-12).

[0118] The present invention constructs a three-layer early warning control system architecture:

[0119] (1) Data acquisition layer: It consists of a MIMO antenna array, a BeiDou receiver, and auxiliary sensors, and is responsible for receiving and preprocessing multi-frequency satellite signals;

[0120] (2) Data processing layer: It consists of a wavelet transform module, an atmospheric error separation module, a multi-source data fusion module, and a signal enhancement module, and is responsible for realizing signal time-frequency analysis and error correction;

[0121] (3) Application service layer: It consists of situation awareness module, risk assessment module, decision control module and information release module, and is responsible for realizing air defense and disaster prevention warning and control functions.

[0122] The function of the data acquisition layer can be expressed as a data acquisition function:

[0123] ;

[0124] in, represent The collection of data at each moment, represents the satellite signal observation value, Represents the communication link status, Represents platform parameters (position, attitude, etc.).

[0125] The functions of the data processing layer can be expressed as data processing functions:

[0126] ;

[0127] in, represent Information products at all times, Represents historical data, Represents processing model parameters.

[0128] The functions of the application service layer can be expressed as decision control functions:

[0129] ;

[0130] in, represent Moment-to-moment action decisions Represents information products, Represents the risk assessment results, Represents the objective function.

[0131] The decision-making and control process of the system can be expressed as a multi-objective optimization problem:

[0132] ;

[0133] Satisfy the constraints:

[0134] ;

[0135] ;

[0136] ;

[0137] in, Represents the objective function vector, including optimization goals, represents the decision variable vector, and represent inequality and equality constraint functions respectively, Represents the decision space.

[0138] Specific optimization goals include:

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] in, 、 、 and Represent the number of false positive, true negative, false negative and true positive samples respectively, Represents the system response time, Represents resource consumption indicators.

[0144] The present invention introduces an adaptive weight matrix into decision control :

[0145] ;

[0146] in, Representative Observation stations for The influence weight of each observation station satisfies .

[0147] Weight updates use reinforcement learning methods and are dynamically adjusted based on historical data:

[0148] ;

[0149] in, Representative The weight value of the iteration, represents the learning rate (usually 0.01-0.1), Representative The reward signal for the iteration is in the range of [-1, 1].

[0150] Risk assessment uses a Bayesian network model to describe the conditional dependencies between different risk factors:

[0151] ;

[0152] in, Representative risk events, Representative Evidence variables, represent The parent node collection.

[0153] In order to achieve dynamic risk assessment, the Dynamic Bayesian Network (DBN) is introduced:

[0154] ;

[0155] in, and Respectively represent and The risk status at any moment, represent Observational evidence of the moment.

[0156] The system adopts a layered control architecture, including strategic layer, tactical layer and execution layer:

[0157] 1. Strategic layer: responsible for long-term planning and resource allocation, with decision cycles ranging from hours to days

[0158] 2. Tactical layer: responsible for mid-term task scheduling and coordination, with a decision cycle of minutes

[0159] 3. Execution layer: responsible for short-term real-time control and response, with a decision cycle of seconds

[0160] In order to achieve flexible response of the system, five levels of response modes are defined:

[0161] 1. Normal monitoring mode: daily monitoring and data collection

[0162] 2. Alert monitoring mode: Increase the frequency of data collection and analysis

[0163] 3. Early warning preparation mode: start preliminary early warning preparation and pre-position resources

[0164] 4. Warning activation mode: issue a formal warning and initiate emergency response

[0165] 5. Emergency response mode: fully activate emergency response measures and carry out rescue and disaster reduction operations

[0166] To improve the system's ability to identify abnormal atmospheric patterns, we designed a hybrid model based on a convolutional neural network (CNN) and a long short-term memory network (LSTM) to identify abnormal patterns in the spatiotemporal distribution of atmospheric parameters. The model structure includes:

[0167] 1. Input layer: receives multi-dimensional atmospheric parameter time series data, with dimensions of ,in represents the number of time steps, and represents the spatial dimension, Represents the number of channels (parameter type);

[0168] 2. Spatiotemporal convolution module: extract spatiotemporal features through 3D convolution, and the convolution kernel parameters are , the step size is ;

[0169] 3. Attention mechanism: Introducing the self-attention mechanism to capture long-range dependencies between different spatiotemporal locations;

[0170] 4. LSTM module: Modeling temporal dependencies, with a hidden state dimension of 256;

[0171] 5. Fully connected output layer: Generates anomaly scores and maps them to interval;

[0172] The model training adopts the contrastive learning method, and the loss function is:

[0173] ;

[0174] in, Represents the input sample The feature representation of represents the positive sample (same category), represents negative samples (different categories), represents the cosine similarity function, represents the temperature parameter (usually set to 0.07), Represents the negative sample set.

[0175] In order to improve the performance of the model on sparse abnormal samples, Focal Loss is used as a supplement:

[0176] ;

[0177] in, represents the predicted probability, represents the category weight, Represents the focus parameter (usually set to 2).

[0178] The final loss function is the weighted sum of contrast loss and focal loss:

[0179] ;

[0180] in, and is the weight coefficient, which is determined by cross-validation.

[0181] The model training data includes historical atmospheric parameter records and corresponding anomaly labels. Data augmentation techniques include random cropping, sliding time windows, and Gaussian noise perturbation. To address class imbalance, weighted sampling and synthetic minority oversampling technique (SMOTE) are used.

[0182] Compared with the prior art, the present invention has the following significant advantages:

[0183] (1) MIMO technology and space-time coding improve the reliability and anti-interference capability of signal reception, improve the signal-to-noise ratio by 3-5dB in complex electromagnetic environments, and reduce the bit error rate by an order of magnitude. Tests show that in a -15dB interference-to-signal ratio (JSR) environment, the system can still maintain a signal detection rate of more than 90%, while the detection rate of a traditional single-antenna system in a -5dB JSR environment is only about 50%;

[0184] (2) The use of multi-scale wavelet transform enables accurate modeling and separation of atmospheric errors, improving the accuracy of ionospheric delay correction to over 90% and that of tropospheric delay correction to over 85%. Even under extreme atmospheric disturbance conditions (such as strong geomagnetic storms), the correction accuracy can still be maintained above 75%, which is much higher than the 40%-60% correction rate of traditional models.

[0185] (3) By utilizing the BeiDou-3 multi-frequency coordinated observation, high-precision positioning is achieved all day and all night, with positioning accuracy reaching sub-meter level under complex atmospheric conditions. The system can achieve fast resolution of whole-cycle ambiguity (FAST) within 60 seconds, and the initialization time is shortened by 40% compared with traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0186] Figure 1 This is a diagram of the system architecture provided by the present invention. DETAILED DESCRIPTION

[0187] The overall system architecture of the present invention is as follows: Figure 1 As shown in the figure, the system consists of four main parts: data acquisition subsystem, signal processing subsystem, atmospheric error correction subsystem, and early warning and control subsystem. These subsystems exchange data and collaborate via standardized interfaces. The system adopts a modular design concept, allowing each functional module to be independently upgraded and replaced, improving system maintainability and scalability.

[0188] Data acquisition subsystem:

[0189] This subsystem realizes multi-dimensional MIMO signal acquisition and space-time coding, and adopts a distributed MIMO antenna array structure. The typical configuration of the antenna array is or Each antenna unit can simultaneously receive signals from the BeiDou-3 satellites at five frequencies: B1I (1561.098MHz), B3I (1268.52MHz), B1C (1575.42MHz), B2a (1176.45MHz), and B2b (1207.14MHz).

[0190] The representation of the MIMO system is:

[0191] ;

[0192] in, Represents the received signal matrix, with dimension , is the number of receiving antennas, is the number of time samples; Represents the channel matrix, with dimension , is the number of transmitting antennas; Represents the sending signal matrix, with dimension ; Represents the additive white Gaussian noise matrix, with dimension .

[0193] For atmospheric propagation environment, channel matrix It can be further decomposed into:

[0194] ;

[0195] in, Represents the spatial domain channel characteristics, describing the attenuation and phase shift of the signal during spatial propagation; Represents the channel characteristics in the time domain and describes the dynamic changes of the channel over time; Represents the frequency domain channel characteristics and describes the propagation differences of signals of different frequencies; Represents the signal polarization characteristics and describes the changes in the signal polarization state during propagation.

[0196] Spatial domain channel characteristics It can be further expressed as:

[0197] ;

[0198] in, Represents the transmission antenna To the receiving antenna The spatial propagation characteristics of represents the signal wavelength, Represents the transmission antenna To the receiving antenna distance.

[0199] Time domain channel characteristics It can be expressed based on Jake's model as:

[0200] ;

[0201] in, represent and The time-dependent properties of moments, represents the zero-order Bessel function, Represents the maximum Doppler shift.

[0202] Frequency domain channel characteristics It can be expressed as:

[0203] ;

[0204] in, Representative frequency and The frequency correlation characteristics between Represents the maximum delay spread.

[0205] Polarization channel characteristics It can be expressed as:

[0206] ;

[0207] in, 、 、 and represent the coupling coefficients between the horizontal-horizontal, horizontal-vertical, vertical-horizontal, and vertical-vertical polarization states, respectively.

[0208] The spatial layout of the antenna array adopts non-uniform distribution, and the antenna spacing matrix Defined as:

[0209] ;

[0210] in, Representative Rank The distance between the antenna element and the center of the array (unit: meters).

[0211] Considering the wavelength differences at different frequencies, antenna spacing design follows the following principles:

[0212] ;

[0213] ;

[0214] in, and represent the minimum and maximum antenna spacing, respectively, and Represent the minimum and maximum operating wavelengths, corresponding to B2a and B3I frequencies respectively.

[0215] The present invention adopts a coding scheme based on orthogonal space-time block code (OSTBC), and its coding matrix is satisfy:

[0216] ;

[0217] in, represent The conjugate transposed matrix of represents a constant, represent The identity matrix of order.

[0218] For a 2-transmit 2-receive MIMO system, the classic Alamouti coding is used:

[0219] ;

[0220] in, and represents the symbols sent in two consecutive time slots, represent The complex conjugate of .

[0221] For a 4-transmit 4-receive MIMO system, extended OSTBC coding is used:

[0222] ;

[0223] in, 、 、 and Represents the symbols transmitted in four consecutive time slots.

[0224] To improve the directional performance of the array antenna, the present invention designs an adaptive beamforming algorithm to dynamically adjust the amplitude and phase characteristics of each antenna element. The beamforming weight vector is calculated using the minimum variance distortionless response (MVDR) criterion:

[0225] ;

[0226] in, represents the MVDR weight vector, represents the covariance matrix of the received signal, Represents the target direction The steering vector, represent The conjugate transpose of .

[0227] To adapt to the characteristics of Beidou satellite signals, this paper designs an adaptive modulation and coding scheme that dynamically adjusts the coding rate and modulation mode based on channel state information (CSI). The modulation mode can be adaptively selected from BPSK, QPSK, 8PSK, and 16QAM, and the coding rate can be adaptively adjusted from 1 / 2 to 5 / 6. The selection criteria for adaptive modulation and coding are:

[0228] ;

[0229] in, Indicates the modulation method used and coding rate The transmission rate, Represents the signal-to-noise ratio The bit error rate below.

[0230] The antenna array configuration parameters in different scenarios are shown in Table 1:

[0231] ;

[0232] in, The wavelength corresponding to the operating frequency (based on the B1I frequency point). The array scale and antenna spacing in different scenarios vary to balance the requirements of system performance and physical size.

[0233] During signal acquisition, the signal received by each antenna element undergoes low-noise amplification and analog-to-digital conversion. The sampling rate is 50MHz and the quantization accuracy is 16 bits to ensure sufficient signal dynamic range. The front-end circuit integrates a temperature-compensated crystal oscillator (TCXO) and automatic gain control (AGC) circuit to ensure stable operation within the -40°C to +85°C temperature range.

[0234] The acquisition subsystem also integrates auxiliary sensors, including a high-precision barometer (accuracy ±0.1hPa), a temperature and humidity sensor (temperature accuracy ±0.1°C, humidity accuracy ±2%), and a three-axis gyroscope (accuracy better than 0.01° / s), to obtain local meteorological parameters and platform attitude information, providing input data for atmospheric error modeling.

[0235] In addition, the system is equipped with a high-stability atomic clock or rubidium clock as a time reference, with a frequency stability better than 5×10^-11 / day and a phase noise better than -130dBc / Hz@10Hz, effectively reducing the contribution of clock error to measurement errors.

[0236] For applications in extreme environments, the system is also designed with environmental adaptive functions, including:

[0237] 1. Temperature adaptation: Automatically adjust the local oscillator frequency when the temperature changes to maintain frequency stability

[0238] 2. Electromagnetic self-adaptation: automatically start band-stop filtering when strong interference is detected to protect the receiving front end

[0239] 3. Power adaptation: Automatically adjust the transmission power according to the communication distance and quality to save energy

[0240] 4. Antenna Adaptation: Automatically select the best antenna combination based on satellite visibility to optimize reception performance

[0241] The comprehensive technical indicators of the system are shown in Table 2:

[0242] ;

[0243] Signal processing subsystem:

[0244] This subsystem implements multi-scale wavelet transform atmospheric error modeling and separation, employing a software-defined radio (SDR) architecture to collaboratively process received signals through a field-programmable gate array (FPGA) and a general-purpose processing unit (GPU). The system employs a heterogeneous computing architecture, allocating tasks with different characteristics to the most appropriate processing platform, improving overall computing efficiency.

[0245] The main hardware configuration of the signal processing platform includes: FPGA, GPU, dedicated DSP, and high-speed storage device.

[0246] The system signal processing flow is as follows:

[0247] (1) Multi-channel signal synchronization: The maximum likelihood estimation method is used to achieve time synchronization of multi-antenna received signals, with a synchronization accuracy better than 5ns. The synchronization process is as follows:

[0248] ;

[0249] in, represents the estimated time deviation, and Representing the and The signal received by the antenna.

[0250] In the specific implementation, the sliding correlation method is used to calculate the optimal delay:

[0251] ;

[0252] in, represent and The cross-correlation function, represents the number of sampling points, Represents the maximum search delay.

[0253] In order to improve the computational efficiency, the FFT method is used to calculate the cross-correlation in the actual implementation:

[0254] ;

[0255] in, and represent Fourier transform and inverse Fourier transform respectively, stands for complex conjugate.

[0256] (2) Channel estimation: Channel estimation is performed based on the pilot sequence, using the minimum mean square error (MMSE) criterion. The channel estimation formula is:

[0257] ;

[0258] in, represents the estimated channel matrix, represents the cross-correlation matrix between the received signal and the transmitted signal, Represents the autocorrelation matrix of the transmitted signal.

[0259] In order to reduce computational complexity and improve robustness, the regularized MMSE method is used in actual implementation:

[0260] ;

[0261] in, represents the regularization parameter, which is usually set to an estimate of the noise power, Represents the identity matrix.

[0262] (3) Space-time decoding: The maximum likelihood (ML) criterion is used to decode space-time codes. The decoding process can be expressed as:

[0263] ;

[0264] in, represents the estimated transmitted signal matrix, stands for the Frobenius norm.

[0265] For Alamouti coding, there exists a closed-form solution:

[0266] ;

[0267] ;

[0268] in, and represents the estimated signal symbol, and represents the channel coefficient, and Represents receiving signal.

[0269] (4) Wavelet decomposition: The decoded signal is subjected to multi-scale wavelet decomposition to implement the discrete wavelet transform (DWT) described in the invention. The mathematical expression of wavelet decomposition is:

[0270] ;

[0271] in, Representative signal The wavelet coefficients of Represents the scale parameter, which indicates the degree of stretching or compression of the wavelet. represents the translation parameter, indicating the time position of the wavelet, Represents the complex conjugate of the wavelet basis function.

[0272] The fast algorithm for discrete wavelet transform is based on Mallat multiresolution analysis, which is a set of high-pass and low-pass filters:

[0273] ;

[0274] ;

[0275] in, Representative Tier The approximation coefficients, Representative Tier The detail coefficient, and Represent the wavelet low-pass and high-pass filter coefficients, respectively, satisfying the orthogonality condition:

[0276] ;

[0277] ;

[0278] ;

[0279] in, represents the Kronecker delta function, when The value is 1 when , otherwise it is 0.

[0280] During implementation, a fast improvement solution is used to improve computing efficiency:

[0281] ;

[0282] ;

[0283] ;

[0284] in, represents the input signal, and represent even-indexed and odd-indexed samples respectively, and Represent the prediction and update operators respectively, and represent the approximation and detail coefficients respectively.

[0285] The present invention uses the Daubechies wavelet family (db4-db20), the Symlet wavelet family (sym8-sym20), and the Coiflet wavelet family (coif3-coif5) as the main analysis tools, and adaptively selects the optimal wavelet basis according to different atmospheric error characteristics. The evaluation indicators for wavelet basis selection include:

[0286] ;

[0287] ;

[0288] ;

[0289] in, represents the energy concentration of wavelet coefficients, represents the sparsity of wavelet coefficients, represents the information entropy of the wavelet coefficients, represents the normalized energy distribution.

[0290] In order to further improve the decomposition efficiency, an adaptive wavelet packet decomposition strategy is designed according to different signal characteristics:

[0291] ;

[0292] ;

[0293] in, Representative Node energy, Representative Node No. wavelet coefficients.

[0294] The present invention proposes an atmospheric error separation algorithm based on wavelet transform to delay the satellite signal. Breaks down to:

[0295] ;

[0296] in, Representative Layer wavelet detail coefficients, corresponding to the high-frequency components of atmospheric disturbances, Representative Layer wavelet approximation coefficient, corresponding to the low-frequency components of atmospheric disturbances, Represents the number of decomposition layers (5-7 layers).

[0297] According to the physical meaning of wavelet coefficients of different scales, an ionosphere and troposphere error separation model is established:

[0298] ;

[0299] ;

[0300] in, represents the dividing scale between ionospheric and tropospheric errors (2-3), Represents the proportion of ionospheric error in the low-frequency component (0.6-0.8, dynamically adjusted according to season and geographical location).

[0301] To improve the accuracy of wavelet decomposition, the present invention adopts the translation-invariant wavelet transform (TIWT) technology to eliminate the displacement variability in traditional wavelet transform, so that the decomposition result is not affected by the time position of the signal:

[0302] ;

[0303] ;

[0304] in, and Representative Dilation filter coefficients of the layer:

[0305] ;

[0306] ;

[0307] To further improve the accuracy of atmospheric error separation, this paper proposes a fine-grained decomposition method based on wavelet packet transform (WPT), which subdivides the signal at both high and low frequency parts to form a complete binary tree structure. The WPT coefficient is:

[0308] ;

[0309] ;

[0310] in, Representative Tier The wavelet packet coefficients of nodes, Represents the node position parameters.

[0311] Based on the WPT coefficient, a more refined atmospheric error separation model is constructed:

[0312] ;

[0313] ;

[0314] in, and The sets of wavelet packet nodes corresponding to the ionospheric and tropospheric errors are trained from historical data using machine learning methods.

[0315] The performance comparison of different wavelet basis functions is shown in Table 3.

[0316] Table 3:

[0317] ;

[0318] The appropriate wavelet basis function can be selected based on the actual application requirements and computing resources. In general, the db4 wavelet basis function can achieve a good balance between computing efficiency and correction accuracy.

[0319] The software architecture of the signal processing subsystem adopts a layered design, including:

[0320] 1. Hardware abstraction layer: shields the underlying hardware differences and provides a unified interface;

[0321] 2. Data management layer: responsible for data acquisition, storage, distribution and synchronization;

[0322] 3. Algorithm core layer: implements the core signal processing algorithm;

[0323] 4. Application interface layer: provides standardized interfaces to upper-layer applications.

[0324] The system uses a data flow architecture and pipeline processing mode, with each processing unit working in parallel, which improves system throughput. The overall performance indicators of the signal processing subsystem are shown in Table 4.

[0325] Table 4:

[0326] ;

[0327] Atmospheric error correction subsystem:

[0328] This subsystem is the core part of the present invention, responsible for realizing BeiDou III multi-frequency coordinated observation and error correction. The processing flow is as follows:

[0329] (1) Ionospheric delay estimation: The ionospheric free combination is constructed using the BeiDou-3 multi-frequency signal to estimate the ionospheric total electron content (TEC) distribution. The relationship between ionospheric delay and TEC is:

[0330] ;

[0331] in, The representative frequency is The delay of the signal passing through the ionosphere (unit: meter), represents the total electron content of the ionosphere (unit: TECU, ), Represents the elevation angle to the satellite Related mapping functions:

[0332] ;

[0333] in, represents the radius of the Earth (about 6371km), Represents the height of maximum electron density in the ionosphere (about 350km).

[0334] The oblique TEC value can be estimated using dual-frequency observations:

[0335] ;

[0336] in, represents the total oblique electron content, and Represents two frequencies, and represents the corresponding pseudorange observation value.

[0337] Considering the high precision of carrier phase observations, the actual TEC estimation uses phase smoothing code technology:

[0338] ;

[0339] in, Representative The smoothed pseudorange of epochs, represents the original pseudorange, Represents the carrier phase observation value.

[0340] The present invention introduces a TEC estimation method based on wavelet transform, which represents TEC as a two-dimensional wavelet expansion in the vertical and horizontal directions:

[0341] ;

[0342] in, represents the wavelet detail coefficient, represents the two-dimensional wavelet basis function, represents the wavelet approximation coefficient, represents the two-dimensional scaling function, represents the number of decomposition layers, and Representing the Layer and The number of wavelet coefficients in the direction.

[0343] To deal with the sparsity of observation data, regularization constraints are introduced:

[0344] ;

[0345] in, represents the regularization parameter, which is determined by cross-validation.

[0346] The ionospheric TEC distribution represented by the spherical harmonics model is:

[0347] ;

[0348] in, Represents latitude and longitude The total electron content at represents the normalized associated Legendre function, and represents the spherical harmonic coefficients, Represents the maximum order (usually 8-12).

[0349] (2) Tropospheric delay estimation: Tropospheric delay consists of two parts: dry delay and wet delay. The total delay can be expressed as:

[0350] ;

[0351] For the dry delay part, the improved Saastamoinen model is adopted:

[0352] ;

[0353] in, represents the dry delay (unit: meter), represents the surface atmospheric pressure (unit: hPa), represents the latitude of the observation point, Represents the height of the observation point (unit: km), Represents the satellite elevation angle (unit: radians).

[0354] For the wet delay part, a prediction model based on a neural network is used:

[0355] ;

[0356] in, represents the wet delay (unit: meter), Represents a neural network model, with input parameters including temperature (Unit: ℃), relative humidity (Percentage), Air Pressure (Unit: hPa), satellite elevation angle (units: radians) and the day of the year (1-366).

[0357] (3) BeiDou III multi-frequency coordinated observation: This invention makes full use of the B1I (1561.098MHz), B3I (1268.52MHz), B1C (1575.42MHz), B2a (1176.45MHz) and B2b (1207.14MHz) multi-frequency signals provided by the BeiDou III satellite system to construct the multi-frequency observation equation:

[0358] ;

[0359] ;

[0360] in, Representative receiver Receiving satellite of Frequency pseudorange observation value, represents the corresponding carrier phase observation value, represents the geometric distance, represents the speed of light, and represent the receiver and satellite clock errors, respectively, represent The wavelength of the frequency point, represents the whole-cycle ambiguity, and represent the pseudorange and carrier phase observation noise, respectively.

[0361] Geometric distance It can be further expressed as:

[0362] ;

[0363] in, Representative Satellite The coordinates of Representative receiver 's coordinates.

[0364] The ionosphere-free combination of the multi-frequency observation equation can be expressed as:

[0365] ;

[0366] ;

[0367] in, and represent the ionospheric-free combined observation values of pseudorange and carrier phase, and Represents two different frequencies.

[0368] The ionosphere-free combination eliminates the first-order ionospheric delay effect, but the high-order ionospheric effect still exists, and its influence can be expressed as:

[0369] ;

[0370] in, represents the second-order ionospheric delay effect (unit: meter), represents the strength of the Earth's magnetic field (unit: Tesla), Represents the angle between the signal propagation direction and the direction of the Earth's magnetic field.

[0371] To achieve high-precision positioning, the present invention adopts Precise Point Positioning (PPP) technology, using precise ephemeris and clock products to replace broadcast ephemeris. The PPP observation equation is:

[0372] ;

[0373] ;

[0374] in, represents the precise satellite clock error, represents the wet delay mapping function, represents the zenith wet delay, represents the carrier phase ambiguity term.

[0375] Considering that the BeiDou system includes satellites of different orbit types (GEO, IGSO, MEO), this paper proposes a differentiated weighting strategy:

[0376] ;

[0377] in, Representative Satellite The weight of represents the satellite elevation angle, Representative Satellite The carrier-to-noise ratio, Represents the reference carrier-to-noise ratio (usually 45dB-Hz).

[0378] (4) Error correction fusion: The ionospheric and tropospheric delay estimation results are fused and corrected with the multipath signal received by MIMO. The fusion process uses the weighted least squares method:

[0379] ;

[0380] in, represents the corrected delay estimate, represents the design matrix, represents the weight matrix, Represents the initial delay estimate.

[0381] Weight Matrix Dynamically adjusts based on satellite elevation angle, signal strength, and receiver noise level:

[0382] ;

[0383] in, , Representative The elevation angle of the satellite, represents the carrier-to-noise ratio, represents the standard deviation of the observation noise.

[0384] In order to improve the correction accuracy, the present invention further introduces a residual correction model to capture system errors and model defects:

[0385] ;

[0386] in, represents the residual delay, Represents the space-time bicubic spline interpolation function.

[0387] The final calibration model is:

[0388] ;

[0389] Under extreme atmospheric conditions, the system automatically switches to robust mode and uses the anomaly-resistant M-estimation instead of the conventional least squares method:

[0390] ;

[0391] in, represents the residual, Represents the robust loss function, using Huber function or Tukey function:

[0392] ;

[0393] ;

[0394] in, Represents the threshold parameter, which is usually set to 1.345 times the residual standard deviation (Huber function) or 4.685 times (Tukey function).

[0395] The performance evaluation results of the atmospheric error correction system are shown in Table 5.

[0396] Table 5:

[0397] ;

[0398] Early warning and control subsystem:

[0399] This subsystem implements the real-time early warning control system architecture and builds a three-layer early warning control system architecture:

[0400] (1) Data acquisition layer: It consists of a MIMO antenna array, a BeiDou receiver, and auxiliary sensors, and is responsible for receiving and preprocessing multi-frequency satellite signals;

[0401] (2) Data processing layer: It consists of a wavelet transform module, an atmospheric error separation module, a multi-source data fusion module, and a signal enhancement module, and is responsible for realizing signal time-frequency analysis and error correction;

[0402] (3) Application service layer: It consists of situation awareness module, risk assessment module, decision control module and information release module, and is responsible for realizing air defense and disaster prevention warning and control functions.

[0403] The function of the data acquisition layer can be expressed as a data acquisition function:

[0404] ;

[0405] in, represent The collection of data at each moment, represents the satellite signal observation value, Represents the communication link status, Represents platform parameters (position, attitude, etc.).

[0406] The functions of the data processing layer can be expressed as data processing functions:

[0407] ;

[0408] in, represent Information products at all times, Represents historical data, Represents processing model parameters.

[0409] The functions of the application service layer can be expressed as decision control functions:

[0410] ;

[0411] in, represent Moment-to-moment action decisions, Represents information products, Represents the risk assessment results, Represents the objective function.

[0412] The decision-making and control process of the system can be expressed as a multi-objective optimization problem:

[0413] ;

[0414] Satisfy the constraints:

[0415] ;

[0416] ;

[0417] ;

[0418] in, Represents the objective function vector, including optimization goals, represents the decision variable vector, and represent inequality and equality constraint functions respectively, Represents the decision space.

[0419] Specific optimization goals include:

[0420] ;

[0421] ;

[0422] ;

[0423] ;

[0424] in, 、 、 and Represent the number of false positive, true negative, false negative and true positive samples respectively, Represents the system response time, Represents resource consumption indicators.

[0425] The present invention introduces an adaptive weight matrix into decision control :

[0426] ;

[0427] in, Representative Observation stations for The influence weight of each observation station satisfies .

[0428] Weight updates use reinforcement learning methods and are dynamically adjusted based on historical data:

[0429] ;

[0430] in, Representative The weight value of the iteration, represents the learning rate (usually 0.01-0.1), Representative The reward signal for the iteration is in the range of [-1, 1].

[0431] Risk assessment uses a Bayesian network model to describe the conditional dependencies between different risk factors:

[0432] ;

[0433] in, Representative risk events, Representative Evidence variables, represent The parent node collection.

[0434] In order to achieve dynamic risk assessment, the Dynamic Bayesian Network (DBN) is introduced:

[0435] ;

[0436] in, and Respectively represent and The risk status at any moment, represent Observational evidence of the moment.

[0437] The main functions of the early warning and control subsystem include:

[0438] (1) Space threat assessment: Based on high-precision positioning information and combined with target motion characteristics, potential threats are assessed in real time. Threat assessment indicators include:

[0439] ;

[0440] in, Represents the collision risk index, with a value range of [0, 1]. Represents the minimum approach distance, Represents the distance uncertainty.

[0441] ;

[0442] in, represents the speed risk index, represents the target speed, represents the speed threshold, represents the maximum reference speed, Represents the Heaviside step function.

[0443] ;

[0444] in, represents the acceleration risk index, represents the target acceleration, represents the acceleration threshold, Represents the maximum reference acceleration.

[0445] The comprehensive risk index is calculated by weighted fusion:

[0446] ;

[0447] in, 、 、 and Represent the weight of each risk factor, satisfying , Represents the risk of trajectory anomaly, calculated by comparing with the historical trajectory library.

[0448] The risk level classification standards are shown in Table 6.

[0449] Table 6:

[0450] ;

[0451] (2) Meteorological disaster assessment: Based on the atmospheric anomaly features extracted by wavelet transform and combined with historical data, a meteorological disaster assessment model is constructed. The atmospheric anomaly feature vector is defined as:

[0452] ;

[0453] in, represents the ionospheric TEC anomaly index, represents the TEC gradient, represents the tropospheric water vapor anomaly index, represents the water vapor gradient, represents wind speed, Represents the probability of rainfall.

[0454] The calculation formula of the ionospheric TEC anomaly index is:

[0455] ;

[0456] in, represents the observed TEC value, represents the reference model TEC value, represents the TEC standard deviation.

[0457] The TEC gradient calculation formula is:

[0458] ;

[0459] The calculation formula of the tropospheric water vapor anomaly index is:

[0460] ;

[0461] in, represents the observed precipitable water, represents the reference model precipitable water, represents the standard deviation of precipitable water.

[0462] The meteorological disaster risk level is determined by a fuzzy inference system:

[0463] ;

[0464] in, Represents the risk level (0-1 range), Representative The weight of the rule, Representative The membership function of the rule.

[0465] The corresponding relationship between meteorological disaster types and characteristic parameters is shown in Table 7.

[0466] Table 7:

[0467] ;

[0468] (3) Warning information release: Based on the threat assessment results, warning information is released through the BeiDou short message communication function. Warning information encoding uses an efficient variable-length coding scheme, which improves coding efficiency by more than 30%. The warning message structure is shown in Table 8.

[0469] Table 8:

[0470] ;

[0471] The present invention optimizes Beidou short message communication, including:

[0472] Low-density parity-check code is used to improve coding efficiency, with a code rate of 0.8 and a minimum Hamming distance of 12;

[0473] Adaptive modulation technology is used to dynamically adjust the modulation mode according to the channel quality. The mapping relationship between channel quality and modulation mode satisfies the formula:

[0474] ;

[0475] in, represents the optimal modulation mode, represents the bit rate, represents the bit error rate;

[0476] Introducing a time-slot random multiple access mechanism to reduce channel conflicts;

[0477] Implement a hierarchical message transmission strategy and assign different transmission priorities and retransmission times according to the warning level.

[0478] The sending process of warning messages adopts priority queue management, and high-priority messages are sent first:

[0479] ;

[0480] in, Represents the message content, Represents priority (1-10, the larger the value, the higher the priority), Indicates the time when the message was generated.

[0481] The queue sorting rules are:

[0482] ;

[0483] Messages are sent using a reliable transport protocol, including confirmation and retransmission mechanisms:

[0484] ;

[0485] in, Represents the maximum number of retransmissions. Represents the highest priority value (usually 10).

[0486] (4) Control command execution: For different types of threats, the system automatically generates corresponding control commands, including air defense system activation, personnel evacuation, resource allocation, etc. The generation of control commands is based on the preset response strategy library and real-time status assessment results. The control command execution adopts a hierarchical structure:

[0487] a. Strategy layer: Determine the overall response strategy and resource allocation

[0488] ;

[0489] in, Represents the response strategy, Represents the risk assessment results, Represents the current system status, Represents historical decision records.

[0490] b. Tactical level: determine specific implementation plans and task allocation

[0491] ;

[0492] in, Represents a tactical plan, Represents the response strategy, Represents available resources, Represents the state of the environment.

[0493] c. Execution layer: performs specific control actions and real-time adjustments

[0494] ;

[0495] in, Represents a control action, Represents a tactical plan, Represents the feedback status, Stands for real-time intervention.

[0496] The system supports comprehensive early warning functions for multiple disaster types, including meteorological disasters, geological disasters, marine disasters, space disasters, and man-made disasters. It uses a Bayesian network model to integrate multi-source disaster information and build a disaster chain reaction prediction model:

[0497] ;

[0498] in, Representative Types of disasters, represent The parent node set of Represents environmental conditions; the system supports comprehensive multi-hazard risk assessment and integrates risk indices for different disaster types:

[0499] ;

[0500] in, represents the comprehensive risk index, Representative The risk index of disasters, Represents the weight factor, satisfying .

[0501] To improve the system's ability to identify abnormal atmospheric patterns, we also designed a hybrid model based on convolutional neural networks (CNN) and long short-term memory networks (LSTM) to identify abnormal patterns in the spatiotemporal distribution of atmospheric parameters.

[0502] The model training adopts the contrastive learning method, and the loss function is:

[0503] ;

[0504] in, Represents the input sample The feature representation of represents the positive sample (same category), represents negative samples (different categories), represents the cosine similarity function, represents the temperature parameter (usually set to 0.07), Represents the negative sample set.

[0505] In order to improve the performance of the model on sparse abnormal samples, Focal Loss is used as a supplement:

[0506] ;

[0507] in, represents the predicted probability, represents the category weight, Represents the focus parameter (usually set to 2).

[0508] The final loss function is the weighted sum of contrast loss and focal loss:

[0509] ;

[0510] in, and is the weight coefficient, which is determined by cross-validation.

[0511] The model training data includes historical atmospheric parameter records and corresponding anomaly labels, and data enhancement techniques include random cropping, time window sliding, and Gaussian noise perturbation.

[0512] The system software architecture adopts a microservice architecture, which mainly includes the following service modules:

[0513] (1) Signal Acquisition Service: Responsible for the acquisition and preprocessing of MIMO antenna array signals; implements adaptive gain control, frequency synchronization, and preliminary filtering, supporting a maximum sampling rate of 50MHz and 16-bit quantization accuracy. Using a streaming processing architecture, the minimum processing latency is less than 1ms.

[0514] (2) Wavelet analysis service: realizes multi-scale wavelet decomposition and reconstruction of signals; supports 18 wavelet basis functions, with a maximum decomposition layer of 12 layers, and uses multi-threaded parallel processing to improve efficiency, with processing delay less than 5ms.

[0515] (3) Atmospheric error modeling service: Build ionospheric and tropospheric delay models; integrate multiple atmospheric models (including IRI, NeQuick, GPT2, etc.), support real-time parameter inversion and historical data training, and the model update frequency can reach up to 1Hz.

[0516] (4) Positioning service: Calculate high-precision positioning results based on corrected observation values; support three modes: single-point positioning, differential positioning and precise single-point positioning (PPP), with a position update rate of up to 100 Hz, and support fast ambiguity fixation (FAST) technology.

[0517] (5) Early warning assessment service: Analyze potential threats and generate risk assessment reports; integrate multiple early warning models and prediction algorithms, support multi-source data fusion and historical data comparison, and update the assessment at a frequency of 1-10Hz.

[0518] (6) Control and scheduling service: Generate control instructions and supervise the execution process; adopt a hierarchical control architecture, support manual, semi-automatic and fully automatic control modes, and monitor and feedback the instruction execution status in real time.

[0519] (7) Data storage service: manages observation data and system status information; adopts a hybrid storage strategy, with hot data stored in an in-memory database (Redis), warm data stored in a NoSQL database (MongoDB), and cold data stored in a distributed file system (HDFS).

[0520] (8) User interface service: Provides visual interfaces for Web and mobile terminals; developed based on the React and Flutter frameworks, supports responsive layout and multi-terminal adaptation, with an interface refresh rate of no less than 30Hz, and supports 2D / 3D visualization and data drill-down analysis.

[0521] The software stack configuration is shown in Table 9.

[0522] Table 9:

[0523] ;

[0524] The present invention has carried out system performance tests in different application scenarios, and the main performance indicators are shown in Table 10.

[0525] Table 10:

[0526] ;

[0527] Among them, standard conditions refer to sunny days with no obvious atmospheric disturbances; mild disturbances refer to light rain or slight ionospheric fluctuations; moderate disturbances refer to thunderstorms or moderate ionospheric storms; and strong disturbances refer to typhoon rainstorms or strong ionospheric storms.

[0528] The positioning accuracy was monitored over a long period of time, and the results are shown in Table 11.

[0529] Table 11:

[0530] ;

[0531] The system's performance is superior to existing technologies under various conditions, especially under strong disturbance conditions, where the positioning accuracy can still be maintained within 1.2 meters and the system response time does not exceed 100 milliseconds, fully meeting the requirements of real-time early warning and control for air defense and disaster prevention.

[0532] The comparative test results show that compared with the traditional single model correction method, the performance advantage is more significant, especially under extreme atmospheric conditions, as shown in Table 12.

[0533] Table 12:

[0534] ;

[0535] The present invention solves the technical problems of insufficient accuracy and weak real-time performance of traditional atmospheric error correction methods in complex environments, provides reliable technical support for air defense and disaster prevention early warning control systems, and has strong application prospects and socio-economic value.

Claims

1. A real-time control system for air defense and disaster prevention warning alarms based on BeiDou III communications, characterized in that: Includes the following subsystems: The data acquisition subsystem includes a distributed MIMO antenna array for simultaneously receiving multi-frequency signals from the BeiDou III satellites B1I / B3I / B1C / B2a / B2b and auxiliary sensor data; Signal processing subsystem for multi-channel signal synchronization, channel estimation, space-time decoding and wavelet decomposition; The atmospheric error correction subsystem is used to realize the ionospheric delay estimation and tropospheric delay estimation, and to reduce the total delay of satellite signals. Decomposition into ionospheric delay and tropospheric delay ; The early warning and control subsystem is used to realize space threat assessment, meteorological disaster assessment, early warning information release and control command execution based on the corrected high-precision positioning information and combined with the Beidou short message communication function.

2. The system according to claim 1, wherein: The spatial layout of the MIMO antenna array adopts a non-uniform distribution, and the antenna spacing matrix Defined as: ； in, Representative Rank The distance between the column antenna unit and the array center; an adaptive beamforming algorithm is used, and the beamforming weight vector calculation adopts the minimum variance distortion-free response criterion.

3. The system according to claim 1, wherein: The wavelet decomposition adopts a fast algorithm of discrete wavelet transform, which is expressed as: ； ； in, Representative Tier The approximation coefficients, Representative Tier The detail coefficient, and Represent the wavelet low-pass and high-pass filter coefficients respectively; the system adaptively selects the optimal wavelet basis from the Daubechies wavelet family, Symlet wavelet family and Coiflet wavelet family according to different atmospheric error characteristics, and eliminates the displacement variability in traditional wavelet transform through translation-invariant wavelet transform technology.

4. The system according to claim 1, wherein: The BeiDou-3 multi-frequency point coordinated observation constructs the multi-frequency observation equation: ； ； in, Representative receiver Receiving satellite of Frequency pseudorange observation value, represents the corresponding carrier phase observation value, represents the geometric distance, represents the speed of light, and represent the receiver and satellite clock errors, respectively, represent The wavelength of the frequency point, Represents the whole-cycle ambiguity; the system uses precise point positioning technology and differentiated weighting strategies to process satellite data of different orbit types.

5. The system according to claim 1, wherein: The ionospheric delay is estimated using the formula: ； in, The representative frequency is The signal is delayed by the ionosphere. represents the total electron content of the ionosphere, Represents the elevation angle to the satellite Related mapping functions; the system introduces a TEC estimation method based on wavelet transform, and expresses TEC as a two-dimensional wavelet expansion: ； in, represents the wavelet detail coefficient, represents the two-dimensional wavelet basis function, represents the wavelet approximation coefficient, represents a two-dimensional scaling function.

6. The system according to claim 1, wherein: The tropospheric delay estimation includes two parts: dry delay and wet delay: ； The dry delay part adopts the improved Saastamoinen model: ； in, stands for dry delay, represents the surface atmospheric pressure, represents the latitude of the observation point, represents the height of the observation point, Represents the satellite elevation angle; the wet delay part adopts a prediction model based on a neural network: ； in, Represents a neural network model, with input parameters including temperature , relative humidity , air pressure , satellite elevation angle and the day of the year .

7. The system according to claim 1, wherein: The early warning control subsystem has a three-layer architecture: Data acquisition layer: consists of MIMO antenna array, BeiDou receiver and auxiliary sensors; Data processing layer: consists of wavelet transform module, atmospheric error separation module, multi-source data fusion module and signal enhancement module; Application service layer: consists of situation awareness module, risk assessment module, decision control module and information release module.

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