A model library-based multi-domain evaluation and compensation method for space radio frequency signal quality

Abstract

A model library-based multi-domain evaluation and compensation method for space radio frequency signal quality belongs to the technical field of wireless communication. The method comprises the following steps: determining signal system parameters based on various CCSDS standards and space protocol systems, extracting relevant parameters of space standards in the time-frequency domain, generating standard time-frequency domain radio frequency signals, and converting them to related domains, modulation domains, energy domains and polarization domains to establish a sub-model library of space radio frequency signals in each domain; obtaining time-frequency domain measured signals through a receiver, comprehensively analyzing time domain information and frequency domain information, and constructing a time-frequency domain sub-evaluation model; constructing a multi-domain comprehensive evaluation model by using a parallel data transmission method and a weight data processing method; calculating the weight of each domain evaluation result by using an entropy weight method to obtain a comprehensive quality evaluation result of the spacecraft radio frequency signal; and based on distortion coefficients, using a robust signal equalization algorithm to process the signal, compensating for the influence of distortion effects on signal quality, and improving the robustness and anti-interference ability of signal transmission.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and relates to a multi-domain evaluation and compensation method for multi-system space radio frequency signal quality based on a model library. BACKGROUND

[0002] Ensuring the high quality of spacecraft radio frequency signals is crucial for maintaining the stability of space communication, navigation and inter-satellite TT&C systems. By comprehensively and reasonably evaluating the quality of spacecraft radio frequency signals, reliable communication between spacecraft and the ground can be ensured, supporting the positioning, navigation and TT&C functions of the spacecraft, and thus ensuring the reliability and safety of the system.

[0003] In recent years, with the development of modern space signal systems, the complexity of spacecraft radio frequency signal quality characterization has been increasing, and the corresponding evaluation requirements have become more complex and diversified. Early evaluation methods mainly focused on the usability and reliability of signals, relying on the strength or signal-to-noise ratio (SNR) of the received signal to judge its quality. For example, the NASA Spacecraft Rendezvous Evaluation and Collision Avoidance Best Practices Manual states that signal strength and signal-to-noise ratio are important factors in evaluating signal quality. However, a single indicator signal evaluation system cannot fully describe the quality of radio frequency signals in a multi-system complex environment.

[0004] On the other hand, in actual communication systems, the space environment where the spacecraft is located is harsh, and the hardware of the spacecraft radio frequency signal transceiver often has non-ideal characteristics, including hardware distortion, amplitude and phase noise, non-linear effects, frequency drift, etc. These factors increase the complexity and uncertainty of signal processing and signal quality evaluation processes. However, existing researches mostly assume ideal transceiver hardware, which has a significant impact on subsequent signal processing and quality evaluation.

[0005] To address the above problems, some signal quality evaluation methods consider more potential characterization dimensions, including signal integrity, clock accuracy and multipath effects. In addition, some related researches consider non-ideal hardware characteristics and compensate for them in communication system design. However, these studies mostly focus on single-dimensional analysis and are difficult to fully cope with complex space communication scenarios.

[0006] In high dynamic scenarios, rapid movement and frequent attitude changes of spacecraft can cause significant Doppler shift, affecting the frequency stability, positioning accuracy, and reception and demodulation of signals. At the same time, distortion effects and environmental interference have an increasingly significant impact on signal quality during signal transmission. To meet the comprehensive and accurate evaluation requirements of high characterization complexity, multi-system, non-ideal hardware and high dynamics of space radio frequency signals, a new evaluation method is urgently needed, which not only can perform real-time signal quality evaluation, but also can effectively identify distortion effects and perform signal compensation, providing strong support for the normal operation of spacecraft in complex space environments. SUMMARY

[0007] In order to meet the high real-time, high dynamic and reliability requirements of the wide application of spacecraft radio frequency signals in different satellite communication systems, the purpose of the present application is to provide a model library based multi-domain evaluation and compensation method for spacecraft radio frequency signal quality, which can perform multi-dimensional hierarchical comprehensive evaluation on spacecraft radio frequency signals, realize fast, reliable and efficient radio frequency signal evaluation, and based on the mapping relationship between quality evaluation indexes and compensation coefficients, further compensate the noise interference and distortion effect in the signal transmission process, restore the original characteristics of the signal, and significantly improve the transmission rate and anti-interference ability of the signal.

[0008] The purpose of the present application is realized by the following technical solutions:

[0009] The model library based multi-domain evaluation and compensation method for spacecraft radio frequency signal quality disclosed by the present application comprises the following steps:

[0010] Step 1: Determine the signal system parameters based on the CCSDS standards and the spacecraft protocol system, extract the related parameters of the spacecraft standard in the time-frequency domain, generate the standard time-frequency domain radio frequency signal, and convert it to the related domain, modulation domain, energy domain and polarization domain, and establish the spacecraft radio frequency signal sub-model library in each domain. Based on the orthogonal time-frequency-space OTFS transformation method, the standard time-frequency domain signal generated by the model library is transformed to the time-delay Doppler domain, and a time-delay Doppler domain signal sub-model library is established. The signal system parameters include carrier frequency, coding mode, modulation mode, frame length, code rate, spread spectrum mode, spread spectrum code type, spread spectrum code length, spread spectrum code rate, Doppler frequency offset range and Doppler frequency offset change rate.

[0011] Step 2: Obtain the time-frequency domain measured signal through the receiver, extract the time domain parameters spread spectrum signal noise size n, noise tolerance N , pseudo code zero point distortion v parameters, extract the frequency domain parameters combined power spectrum deviation |ΔPSD(f)|, carrier leakage L carrier , construct the time-frequency domain sub-evaluation model by combining the time domain information and frequency domain information; convert the received time-frequency domain measured signal to the related domain, modulation domain, energy domain, polarization domain and time-delay Doppler domain, and construct independent evaluation models in the six domains respectively, and construct a multi-domain comprehensive evaluation model by using parallel data transmission method and weight data processing method to combine the six independent evaluation models.

[0012] Step 2.1: Obtain the time-frequency domain measured signal through the receiver.

[0013] Step 2.1.1: Connect the receiving antenna to the software defined radio receiver.

[0014] Step 2.1.2: Perform amplification and filtering of the analog signal, and then convert the analog signal into a digital signal through a high-speed analog-to-digital converter (ADC).

[0015] Step 2.1.3: Perform demodulation and processing of the signal with a digital signal processing (DSP) unit inside or externally connected to the receiver, and finally obtain the time-domain waveform and spectral information of the signal.

[0016] Step 2.2: Extract time-domain parameters, including spread spectrum signal noise size n, noise margin ε N , pseudo-code zero point distortion v, from the measured signal obtained in step 2.1, and then construct a time-domain sub-evaluation model.

[0017] Step 2.2.1: Perform coherent accumulation averaging on the measured signal obtained in step 2.1 to obtain the superimposed chip after coherent accumulation averaging;

[0018] Step 2.2.2: Analyze the waveform of the superimposed chip in step 2.2.1, check whether there is obvious distortion. Count the duration of each positive and negative chip in the code period, and compare the duration with the ideal signal chip generated by the model library in step 1 to calculate the difference sequence between the superimposed chip in step 2.2.1 and the ideal signal chip in step 1.

[0019] Step 2.2.3: Perform statistical analysis on the difference sequence obtained in step 2.2.2 to calculate its maximum value, minimum value, peak value, standard deviation and mean value. Based on the statistical results, determine the time-domain parameters, including spread spectrum signal noise size n, noise margin ε N , and pseudo-code zero point distortion v.

[0020] Step 2.3: Extract frequency-domain parameters, including integrated power spectrum deviation |ΔPSD(f)|, carrier leakage L carrier , from the measured signal obtained in step 2.1 to construct a frequency-domain sub-evaluation model.

[0021] Step 2.3.1: Derive the signal power spectrum curve from the measured radio frequency signal obtained in step 2.1, and perform fitting analysis with the ideal signal power spectrum generated by the model library in step 1 to test and compare the signal bandwidth of the fitting curve.

[0022] The power spectrum curve PSD meas (f) of the measured signal is obtained by Fourier transform of the time-domain measured radio frequency signal in step 2.1: that is, where, represents the Fourier transform of the signal x(t).

[0023] Step 2.3.2: The measured RF signal in step 2.1 is introduced into a spectrum analyzer or phase noise analyzer for detailed power spectrum analysis. Then, the standard power spectrum PSD ideal (f) of the ideal signal generated by the model library in step 1 is compared, and the frequency domain combined power spectrum deviation |ΔPSD(f)| at the main energy distribution point of the signal is calculated, which can be expressed as:

[0024] |ΔPSD(f)| = |PSD meas (f) - PSD ideal (f)|

[0025] Step 2.3.3: Through power spectrum analysis, the carrier leakage L carrier of the measured RF signal in step 2.1 is detected.

[0026] The carrier leakage ratio L carrier is expressed as the ratio of the measured carrier leakage power P carrier to the total received signal power P total : The carrier leakage power P carrier can be directly read from the power spectrum of the measured signal: P carrier = PSD meas (f carrier ), where f carrier is the carrier frequency.

[0027] Step 2.4: Extract the correlation domain parameters cross-correlation function CCF(τ), correlation loss zero drift ε b (δ) from the measured signal obtained in step 2.1, and construct a correlation domain sub-evaluation model.

[0028] Step 2.4.1: Perform carrier stripping on the measured signal obtained in step 2.1 to obtain the baseband waveform of the measured signal;

[0029] Step 2.4.2: Calculate the normalized cross-correlation function CCF(τ) of the signal obtained in step 2.4.1 and the local reference code in step 1, and calculate the difference between the ideal power of the local reference code in step 1 and the actual power of the signal obtained in step 2.4.1, i.e. the correlation loss

[0030] where the correlation function CCF(τ) refers to the normalized cross-correlation function of the baseband signal s rec1 (t) to be evaluated and the ideal band-limited signal s0(t) reproduced by the local signal model library, and the correlation function CCF(τ) is expressed as:

[0031]

[0032] where T is the integration time p is the time length of the primary code period.

[0033] correlation loss The time domain expression is:

[0034]

[0035] where is the s rec1 cross-correlation function of s is the s rec1 autocorrelation function of s is the autocorrelation function of s0(t).

[0036] Step 2.4.2: Draw the phase discrimination curve of the measured signal obtained in step 2.1 to obtain the zero-crossing point drift ε b of the curve of (δ) with respect to the lead-lag spacing δ.

[0037] where the expression of the zero-crossing point drift of the discrimination function curve is:

[0038]

[0039] where ε1 and ε2 are two adjacent time delay sampling points, δ is the lead-lag spacing of the correlator, and D(ε, δ) is the expression of the discrimination function curve, i.e.

[0040] Step 2.4.3: Within the transmission bandwidth of the measured signal obtained in step 2.1, within the correlator interval of 0-1 chip, according to ε b (δ), further analyze the zero-crossing point deviation SCB of the S curve and the change of the slope within several code periods of data segments, and construct a correlation domain sub-evaluation model.

[0041] Step 2.5: Extract the modulation domain parameters I / Q branch amplitude ratio Δa, I / Q branch phase orthogonality deviation Δθ, error vector magnitude ε EVM of the constellation diagram, signal component amplitude difference Δp of the measured signal obtained in step 2.1, and construct a modulation domain sub-evaluation model.

[0042] Step 2.5.1: The measured signal obtained in step 2.1 is filtered by an ideal FIR cutoff filter with a bandwidth of the transmission bandwidth, and a baseband signal is obtained after demodulation.

[0043] Step 2.5.2: Sample the baseband signal obtained in step 2.5.1, draw a constellation diagram, and calculate the I / Q branch amplitude ratio where |I (t) | and |Q(t) | represents the absolute amplitude average value of I branch and Q branch signals respectively.

[0044] Step 2.5.3: According to the constellation diagram obtained in step 2.5.2, calculate the phase quadrature deviation of I / Q branch

[0045] Step 2.5.4: According to the constellation diagram obtained in step 2.5.2, calculate the deviation between the ideal signal point generated by the signal model library in step 1 and the actual received signal point in step 2.5.1, and calculate the error vector magnitude of the constellation diagram

[0046] Step 2.5.5: Calculate the component amplitude difference between the ideal signal generated by the signal model library in step 1 and the actual measured baseband signal obtained in step 2.5.1

[0047] Step 2.6: Measure the power of the downlink actual signal obtained in step 2.1 using a power meter or a vector network analyzer VNA. Obtain a series of power readings by measuring multiple times, and statistically analyze the power reading data to extract the power variation range ∈ P and stability σ P . Among them, the power variation range ∈ P represents the maximum fluctuation amplitude of the signal power during the measurement process, and the stability σ P is obtained by calculating the standard deviation of the power readings, reflecting the fluctuation degree of the signal power. According to the downlink power variation range ∈ P and stability σ P , the energy domain sub-evaluation model is constructed.

[0048] Among them, the power variation range ∈ P = P max -P min , where P max and P min are the maximum and minimum values of the actual signal power respectively; the stability of the actual signal power

[0049] Step 2.7: For the actual signal obtained in step 2.1 and the ideal signal generated by the signal model library in step 1, use professional spectrum test tools such as a vector network analyzer VNA or a spectrum analyzer to measure these signals respectively. By comparing the frequency domain response ε Polar of the actual signal obtained in step 2.1 with the frequency domain response ε′ Polar, the polarization mode of the signal is preliminarily determined. Then, the signal is tested using different polarization measurement settings such as linear and circular polarization antennas. By gradually adjusting the polarization direction of the receiving antenna (e.g., from horizontal polarization to vertical polarization, or from right-hand circular polarization to left-hand circular polarization), the change in the received signal power ΔP of step 2.1 and the change in the ideal signal power ΔP' generated by the model library of step 1 are observed and recorded. Polar ΔP-ε′ Polar ΔP' is the polarization domain quantization index, thereby constructing a polarization domain sub-evaluation model.

[0050] Step 2.8: Transform the measured signal obtained in step 2.1 and the ideal signal generated by the model library in step 1 into the time-delay Doppler domain. According to the time-delay Doppler signal matrix of the measured signal of step 2.1 and the ideal signal of step 1, respectively, extract the mean square error MSE, peak signal-to-noise ratio PSNR, and cross-correlation coefficient COR Dopper , and construct a time-delay Doppler domain sub-evaluation model.

[0051] Step 2.8.1: Frame the measured time-domain signal obtained in step 2.1 and the ideal time-domain signal of step 1, respectively, and divide the continuous time-domain signal into multiple frames (or blocks).

[0052] Step 2.8.2: Perform discrete-time Fourier transform (DTFT) on each frame (block) to convert the time-domain signal to the frequency domain signal.

[0053] Step 2.8.3: Perform symplectic Fourier transform (SFT) on the converted frequency-domain signal to convert it to a time-delay-Doppler domain signal.

[0054] Step 2.8.4: Arrange the obtained time-delay-Doppler domain signal into a matrix form, where each matrix element corresponds to a specific time delay and Doppler shift.

[0055] Step 2.8.5: According to the time-delay Doppler signal matrix of the measured signal in step 2.1 and the ideal signal in step 1, extract the mean square error MSE, peak signal-to-noise ratio PSNR, and cross-correlation coefficient COR Dopper , and construct a time-delay Doppler domain sub-evaluation model.

[0056] Specifically, the mean square error is represented as The peak signal-to-noise ratio is represented as The cross-correlation coefficient is represented as where X dd represents the time-delay Doppler domain matrix form of the standard signal of step 1; Y dd represents the time-delay Doppler domain matrix form of the received signal of step 2.1.

[0057] Step 3: Compare the measured spacecraft radio frequency signal with the standard signal generated by the model library, and calculate the normalized results of each quantitative indicator in the six domains: time-frequency domain indicator S tf , correlation domain indicator S r , modulation domain indicator S m , energy domain indicator S e , polarization domain indicator S p , and time delay Doppler domain indicator S d ; according to the normalized results of the six domain sub-evaluation models under the measured data, calculate the weight of each domain evaluation result by entropy weight method, and then calculate the weighted average value of each domain evaluation result to obtain the comprehensive quality evaluation result S of the spacecraft radio frequency signal.

[0058] Step 3.1: Calculate the calculation indicators in the six domains respectively.

[0059] Time-frequency domain indicator where the weights w tf1 , w tf2 , w tf3 , w tf4 , w tf5 all need to be adjusted according to the actual measurement situation and w tf1 + w tf2 + w tf3 + w tf4 + w tf5 = 1.

[0060] Correlation domain indicator where the weights w r1 , w r2 , w r3 all need to be adjusted according to the actual measurement situation by expert experience method and w r1 + w r2 + w r3 = 1.

[0061] Modulation domain indicator where the weights w m1 , w m2 , w m3 , w m4 all need to be adjusted according to the actual measurement situation by expert experience method and w m1 + w m2 + w m3 + w m4 = 1.

[0062] Energy domain indicator

[0063] Polarization domain indicator

[0064] Time delay Doppler domain indicator S dd= MSE · 1 / PSNR + 1 / PSNR · 1 / COR Dopper + MSE · 1 / COR Dopper .

[0065] Step 3.2: Obtain the respective weights {w tf ,w r ,w m ,w e ,w p ,w dd} of the six domain sub-evaluation results by entropy weight method.

[0066] Step 3.2.1: Standardize the relevant original data involved in the sub-evaluation results of the six domains (i.e., 6 indicators) to eliminate the influence of dimension. Standardize the indicator x ij :

[0067]

[0068] wherein the jthindicator value of the ithsample, j = 1, 2, 3, 4, 5, 6.

[0069] Step 3.2.2: Calculate the entropy value of each of the six indicators:

[0070]

[0071] wherein p ij is the proportion of the ithsample under the jthindicator: n is the sample quantity.

[0072] Step 3.2.3: Calculate the difference coefficient of each indicator:

[0073] Calculate the difference coefficient d j of the jthindicator:

[0074] d j = 1 - E j

[0075] Step 3.2.4: According to the difference coefficient, calculate the weight w j of each indicator:

[0076]

[0077] Step 3.3: Calculate the comprehensive quality evaluation result S = w tf S tf + w r S r + w m S m + w e S e + w p Sp +w dd S dd Among them, {w tf ,w r ,w m ,w e ,w p ,w dd}={w1,w2,w3,w4,w5,w6}.

[0078] Step 4: Based on the real-time quality assessment results of the spacecraft radio frequency signal obtained in Step 3, further analyze the noise interference and distortion effects experienced by the signal during transmission, and calculate and obtain the distortion effect coefficient β of the transmitter and its related links. T The distortion effect coefficient β of the receiver and its associated links R Based on this distortion coefficient, a robust signal equalization algorithm is used to process the signal, compensate for the impact of distortion on signal quality, restore the original characteristics of the signal, and improve the robustness and anti-interference capability of signal transmission.

[0079] Distortion effect coefficient β of the transmitter and its related links T The distortion effect coefficient β of the receiver and its associated links R Represented as

[0080] β T =β R =S=w rf S tf +w r S r +w m S m +w e S e +w p S p +w dd S dd

[0081] Furthermore, based on the obtained distortion effect coefficient β of the transmitter and its related links... T The distortion effect coefficient β of the receiver and its related links R An equalization matrix G is designed based on the minimum mean square error criterion to address the effects of distortion and noise. Robust equalization processing of the received signal reduces the impact of non-ideal characteristics and distortion of the transceiver hardware on the signal evaluation results.

[0082] Specifically, in a multi-antenna system, the receiver robust equalization matrix G based on the minimum mean square error criterion is expressed as:

[0083] G = W H H H (HWWH H H +Z) -1 ,

[0084] wherein,

[0085]

[0086] and W represents a transmit precoding matrix, and H represents an equivalent baseband channel matrix between the transmitter and the receiver obtained through channel estimation.

[0087] Advantages:

[0088] 1. The spaceflight radio frequency signal quality multi-domain evaluation and compensation method based on a model library disclosed in the application determines signal system parameters based on various CCSDS standards and spaceflight protocol systems, extracts relevant parameters of spaceflight standards in the time-frequency domain, generates standard time-frequency domain radio frequency signals, and converts them to relevant domains, modulation domains, energy domains and polarization domains, thereby establishing a spaceflight radio frequency signal sub-model library in each domain to provide accurate and comprehensive benchmark models for signal evaluation, and helping to improve the accuracy and efficiency of spaceflight radio frequency signal quality multi-domain evaluation and compensation.

[0089] 2. The spaceflight radio frequency signal quality multi-domain evaluation and compensation method based on a model library disclosed in the application converts the standard time-frequency domain signals generated by the model library to the time-delay Doppler domain based on the orthogonal time-frequency space modulation (OTFS) method, establishes a time-delay Doppler domain signal sub-model library, and judges whether the signal still has high frequency stability and reception demodulation accuracy in the case of drastic frequency change, which can effectively deal with the Doppler shift problem of the spacecraft in the high dynamic scene.

[0090] 3. The spaceflight radio frequency signal quality multi-domain evaluation and compensation method based on a model library disclosed in the application obtains time-frequency domain measured signals through a receiver, and converts them to relevant domains, modulation domains, energy domains, polarization domains and time-delay Doppler domains. Based on the comparison of the standard signals generated based on the multi-system signal model library and the measured signals in the time-frequency domain, the relevant domain, the modulation domain, the energy domain, the polarization domain and the time-delay Doppler domain, a multi-dimensional signal quality evaluation model is established, the evaluation indexes of each domain are integrated through the entropy weight method, parallel data transmission is performed to obtain the comprehensive quality evaluation result of the spacecraft radio frequency signal, and multi-dimensional, rapid, efficient and reliable evaluation of the comprehensive quality of the radio frequency signal is realized.

[0091] 4. The spaceflight radio frequency signal quality multi-domain evaluation and compensation method based on a model library disclosed in the application analyzes the noise interference and distortion effect suffered by the signal in the transmission process according to the real-time evaluation result, calculates and obtains the distortion effect coefficient of the transmitting end, the receiving end and the related link, and performs robust equalization and efficient compensation to restore the original characteristics of the signal and improve the robustness and anti-interference ability of the signal transmission, thereby providing important protection for the signal transmission stability and reliability of the spaceflight system. Attached Figure Description

[0092] Figure 1 A flowchart of a multi-domain assessment and compensation method for aerospace radio frequency signal quality based on a model library, according to the present invention;

[0093] Figure 2 The mean square error of the received signal varies with the system distortion coefficient. Detailed Implementation

[0094] The embodiments of the present invention are described in detail below. These embodiments are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0095] This embodiment determines signal system parameters based on various CCSDS standards and aerospace protocol systems, extracts relevant parameters of aerospace standards in the time-frequency domain, generates standard time-frequency domain radio frequency signals, and transforms them to the correlation domain, modulation domain, energy domain, and polarization domain, establishing aerospace radio frequency signal sub-model libraries for each domain. Based on the Orthogonal Time-Frequency Spatial Modulation (OTFS) method, the standard time-frequency domain signals generated by the model library are transformed to the time-delay Doppler domain, establishing a time-delay Doppler domain signal sub-model library. Next, this invention obtains the measured time-frequency domain signal through a receiver and transforms it to the correlation domain, modulation domain, energy domain, polarization domain, and time-delay Doppler domain. Based on the comparison between the standard signal generated by the multi-system signal model library and the measured signal in the time-frequency domain, correlation domain, modulation domain, energy domain, polarization domain, and time-delay Doppler domain, a multi-dimensional signal quality assessment model is established. The assessment indicators of each domain are integrated through the entropy weight method, and the comprehensive quality assessment result of the spacecraft radio frequency signal is obtained through parallel data transmission. The specific structure is shown in Table 1:

[0096] Table 1. Contents of the Multi-System Aerospace Signal Multi-Domain Evaluation Architecture

[0097]

[0098] Based on the real-time evaluation results, the noise interference and distortion effects experienced by the signal during transmission are analyzed. The distortion effect coefficients of the transmitter, receiver and their related links are calculated and obtained. Robust equalization and efficient compensation are then performed to restore the original characteristics of the signal and improve the robustness and anti-interference capability of signal transmission.

[0099] like Figure 1 As shown in the figure, this embodiment discloses a multi-domain evaluation and compensation method for aerospace radio frequency signal quality based on a model library. The specific implementation steps are as follows:

[0100] Step 1: Based on the CCSDS 131.0-B-3 standard (a standard for low-density parity-check code LDPC) and the CCSDS 413.0-G-1 standard (data compression recommended practice) and other standards, combined with the space protocol system (such as ISO 22665:2009 for satellite communication Radio frequency and modulation mode selection protocol), determine the signal system parameters, extract the key parameters in the time-frequency domain, generate standard time-frequency domain radio frequency signals, and convert them to related domains, modulation domains, energy domains and polarization domains, and establish a space radio frequency signal sub-model library in each domain. Based on the orthogonal time-frequency-space OTFS transformation method, the standard time-frequency domain signals generated by the model library are transformed into the time-delay Doppler domain, and a time-delay Doppler domain signal sub-model library is established. The signal system parameters include:

[0101] Carrier frequency: S-band (2-4 GHz), X-band (7-8.5 GHz),

[0102] Encoding method: LDPC encoding (based on CCSDS 131.0-B-3 standard),

[0103] Modulation method: QPSK (quadrature phase shift keying), 8PSK (8 phase shift keying),

[0104] Frame length: 2048 bits, 4096 bits,

[0105] Code rate: 10 Mbps to 100 Mbps,

[0106] Spread spectrum method: DSSS (direct sequence spread spectrum),

[0107] Spread spectrum code type: PN code (pseudo-random noise code),

[0108] Spread spectrum code length: 1023 bits,

[0109] Spread spectrum code rate: 10 MHz to 100 MHz,

[0110] Doppler frequency offset range: ±40 kHz (determined according to satellite orbital speed),

[0111] Doppler frequency offset rate: 1 kHz / s to 20 kHz / s (affected by spacecraft motion state).

[0112] Step 2: Obtain the time-frequency domain measured signal through the receiver, extract the time domain parameters spread spectrum signal noise size n, noise margin ε N , pseudo code zero distortion v parameters, extract the frequency domain parameters combined power spectrum deviation |ΔPSD(f)|, carrier leakage L carrier, the time domain information and the frequency domain information are integrated, a time-frequency domain sub-evaluation model is constructed; the received time-frequency domain measured signals are converted to the correlation domain, the modulation domain, the energy domain, the polarization domain and the time delay Doppler domain, and independent evaluation models in the six domains are respectively constructed, the parallel data transmission mode and the weight data processing method are used to integrate the six independent evaluation models, and a multi-domain comprehensive evaluation model is constructed.

[0113] Step 2.1: Obtain the time-frequency domain measured signal through the receiver.

[0114] Step 2.1.1: Connect the receiving antenna to the software-defined radio receiver.

[0115] Step 2.1.2: Perform amplification and filtering of the analog signal, and then convert the analog signal to a digital signal through a high-speed analog-to-digital converter ADC.

[0116] Step 2.1.3: Demodulate and process the signal by using a digital signal processing (DSP) unit inside or externally connected to the receiver, and finally obtain the time domain waveform and spectrum information of the signal. In a specific embodiment, a satellite communication signal with a bandwidth of 5 MHz is received by connecting to a software-defined radio receiver, and the sampling rate is set to 10 MHz. Signal data with a duration of 1 ms is collected.

[0117] Step 2.2: Extract time domain parameters from the measured signal obtained in step 2.1, including spread spectrum signal noise size n, noise tolerance ε N , pseudo code zero point distortion v parameters, and then construct a time domain sub-evaluation model.

[0118] Step 2.2.1: Perform coherent accumulation averaging on the measured signal obtained in step 2.1 to obtain the superimposed chip after coherent accumulation averaging.

[0119] Step 2.2.2: Analyze the waveform of the superimposed chip in step 2.2.1, check whether there is obvious distortion. Count the duration of each positive and negative chip in the code period, and compare the duration with the ideal signal chip generated by the model library in step 1 to calculate the difference sequence of the superimposed chip in step 2.2.1 and the ideal signal chip in step 1.

[0120] Step 2.2.3: Perform statistical analysis on the difference sequence obtained in step 2.2.2 to calculate its maximum value, minimum value, peak value, standard deviation and mean value. Based on the statistical results, determine the time domain parameters, including the spread spectrum signal noise size n, the noise tolerance ε N and the pseudo code zero point distortion v.

[0121] In the embodiment, the collected spread spectrum signal is subjected to coherent accumulation average processing, the noise size of the obtained superimposed chip compared with the ideal chip is-95dBm, the noise tolerance is 2dB, and the zero-crossing distortion v is 0.01. The time domain evaluation model is established through these parameters to determine the quality of the signal.

[0122] Step 2.3: Extracting the frequency domain parameters of the measured signal obtained in step 2.1, including the integrated power spectrum deviation |ΔPSD(f)|, carrier leakage L carrier , to construct a frequency domain sub-evaluation model.

[0123] Step 2.3.1: Deriving the signal power spectrum curve from the measured radio frequency signal obtained in step 2.1, and performing fitting analysis with the ideal signal power spectrum generated by the model library in step 1, testing and comparing the signal bandwidth of the fitting curve.

[0124] The power spectrum curve PSD meas (f) of the measured signal is obtained by Fourier transform of the time domain measured radio frequency signal in step 2.1. Wherein, represents the Fourier transform of the signal x(t).

[0125] Step 2.3.2: Importing the measured radio frequency signal in step 2.1 into a spectrum analyzer or a phase noise analyzer for detailed power spectrum analysis. Then, compared with the standard power spectrum PSD ideal (f) of the ideal signal generated by the model library in step 1, the frequency domain integrated power spectrum deviation |ΔPSD(f)| at the main energy distribution point of the signal is calculated, and the calculation formula can be expressed as:

[0126] |ΔPSD(f)|=|PSD meas (f)-PSD ideal (f)|

[0127] Step 2.3.3: Through power spectrum analysis, detecting the carrier leakage L carrier of the measured radio frequency signal in step 2.1, and evaluating the spectral distortion degree of the signal in the case.

[0128] The carrier leakage ratio L carrier is expressed by the ratio of the measured carrier leakage power P carrier to the total received signal power P total : The carrier leakage power P carrier can be directly read from the power spectrum of the measured signal: P carrier =PSD meas (f carrier ), wherein f carrier is the carrier frequency.

[0129] In the embodiment, the signal is analyzed by a spectrum analyzer, and the power spectrum deviation is 0.1 dB, and the carrier leakage is -40 dBc. No serious carrier leakage is detected in the spectrum analysis, and the signal quality is good.

[0130] Step 2.4: Extracting the correlation domain parameter cross-correlation function CCF(τ) of the measured signal obtained in step 2.1, and calculating the correlation loss Zero drift ε b , and constructing a correlation domain sub-evaluation model.

[0131] Step 2.4.1: Carrier stripping removal is performed on the measured signal obtained in step 2.1 to obtain a baseband waveform of the measured signal;

[0132] Step 2.4.2: Calculate the normalized cross-correlation function CCF(τ) of the signal obtained in step 2.4.1 and the local reference code in step 1, and calculate the difference between the ideal power of the local reference code in step 1 and the actual power of the signal obtained in step 2.4.1, i.e. the correlation loss

[0133] Wherein, the correlation function CCF(τ) refers to the normalized cross-correlation function of the baseband signal s rec1 (t) to be evaluated and the ideal band-limited signal s0(t) reproduced by the local signal model library, and the expression of the correlation function CCF(τ) is:

[0134]

[0135] In the formula, the integration time T p is the time length of the main code period.

[0136] Correlation loss The time domain expression is:

[0137]

[0138] In the formula, is the cross-correlation function of s rec1 (t) and s0(t), is the autocorrelation function of s rec1 (t), is the autocorrelation function of s0(t).

[0139] Step 2.4.2: Draw the phase discrimination curve of the measured signal obtained in step 2.1, and draw the curve of the zero drift ε b (δ) of the phase discrimination curve with respect to the lead-lag spacing δ.

[0140] Wherein, the expression of the zero drift of the discrimination function curve is:

[0141]

[0142] where ε1 and ε2 are two adjacent time delay sampling points, δ is the leading-lag interval of the correlator, and D(ε, δ) is the discriminant function curve expression, i.e.

[0143] Step 2.4.3: Within the transmission bandwidth of the measured signal obtained in step 2.1, within the correlator interval of 0-1 chip, the zero-crossing point deviation SCB of the S curve and the change of the slope in the data segment of several code periods are further analyzed according to ε b (δ), and a correlation domain sub-evaluation model is constructed.

[0144] In the embodiment, the cross-correlation function calculation is used to obtain CCF(τ=0) of 0.95, indicating high correlation. The correlation loss is -3 dB, and the zero-crossing point drift is 0.02, indicating that the pseudo code drift is within the allowable range.

[0145] Step 2.5: The modulation domain parameters I / Q branch amplitude ratio Δa, I / Q branch phase orthogonality deviation Δθ, error vector magnitude ε EVM of the constellation diagram, and signal component amplitude difference Δp of the measured signal obtained in step 2.1 are extracted to construct a modulation domain sub-evaluation model.

[0146] Step 2.5.1: The measured signal obtained in step 2.1 is subjected to ideal FIR cutoff filtering with a bandwidth of the transmission bandwidth, and a baseband signal is obtained after demodulation.

[0147] Step 2.5.2: The baseband signal obtained in step 2.5.1 is sampled, a constellation diagram is drawn, and the I / Q branch amplitude ratio Δa is calculated. where |I (t) | and |Q (t) | represent the absolute amplitude average values of the I branch and Q branch signals, respectively.

[0148] Step 2.5.3: According to the constellation diagram obtained in step 2.5.2, the phase orthogonality deviation Δθ of the I / Q branch is calculated.

[0149] Step 2.5.4: According to the constellation diagram obtained in step 2.5.2, the deviation between the ideal signal point generated by the signal model library in step 1 and the actual received signal point in step 2.5.1 is calculated, and the error vector magnitude ε

[0150] Step 2.5.5: The component amplitude difference Δp between the ideal signal generated by the signal model library in step 1 and the measured baseband signal obtained in step 2.5.1 is calculated.

[0151] Specifically in the embodiment, constellation diagram is drawn for the demodulated signal, the amplitude ratio of I / Q branch is calculated as 1.02, the phase quadrature deviation is 0.03 rad, and the error vector magnitude is 2.5%. The modulation quality is good, and the signal is relatively stable.

[0152] Step 2.6: Measure the power of the downlink measured signal obtained in step 2.1 using a power meter or a vector network analyzer VNA. Obtain a series of power readings by measuring multiple times, and statistically analyze the power reading data to extract the power variation range ∈ P and stability σ P . Among them, the power variation range ∈ P represents the maximum fluctuation amplitude of the signal power in the measurement process, and the stability σ P is obtained by calculating the standard deviation of the power readings, reflecting the fluctuation degree of the signal power. According to the downlink power variation range ∈ P and stability σ P , the energy domain sub-evaluation model is constructed.

[0153] Among them, the power variation range ∈ P = P max -P min , where P max and P min are the maximum and minimum values of the measured signal power respectively; the stability of the measured signal power

[0154] Specifically in the embodiment, the signal power is measured multiple times, and the maximum power fluctuation amplitude is 0.5 dB and the stability is 0.1 dB. The signal fluctuation is small, indicating that the energy of the signal transmission is relatively stable.

[0155] Step 2.7: For the measured signal obtained in step 2.1 and the ideal signal generated by the signal model library in step 1, use professional spectrum test tools such as a vector network analyzer VNA or a spectrum analyzer to measure these signals respectively. By comparing the response ε Polar of the measured signal in step 2.1 in the frequency domain with the response ε′ Polar of the ideal signal generated by the signal model library in step 1 in the frequency domain, the polarization mode of the signal is preliminarily judged. Then, use different polarization measurement settings such as linear polarization and circular polarization antennas to test the signal. By gradually adjusting the polarization direction of the receiving antenna (such as from horizontal polarization to vertical polarization, or from right circular polarization to left circular polarization), observe and record the change ΔP of the received power of the measured signal in step 2.1 and the change ΔP′ of the received power of the ideal signal generated by the signal model library in step 1.ε Polar ΔP-ε′ PolarΔP' is a polarization domain quantization index, thereby constructing a polarization domain sub-evaluation model.

[0156] Specifically, in the embodiment, compared with the polarization response of the ideal signal, the polarization difference of the measured signal is 0.02 dB, and the change of the received signal power is 0.5 dB, which indicates that the polarization characteristics are basically matched, and the polarization domain performs well.

[0157] Step 2.8: Transform the measured signal obtained in step 2.1 and the ideal signal generated in the model library in step 1 into the time delay Doppler domain. According to the time delay Doppler signal matrix of the measured signal in step 2.1 and the ideal signal in step 1, respectively, extract the mean square error MSE, the peak signal-to-noise ratio PSNR, and the cross-correlation coefficient COR Dopper , and construct a time delay Doppler domain sub-evaluation model.

[0158] Step 2.8.1: Frame the measured time domain signal obtained in step 2.1 and the ideal time domain signal in step 1, respectively, divide the continuous time domain signal into multiple frames (or blocks), and each frame contains 1024 sampling points.

[0159] Step 2.8.2: Perform a 2048-point discrete-time Fourier transform (DTFT) on each frame (block) to convert the time domain signal into a frequency domain signal.

[0160] Step 2.8.3: Perform a symplectic Fourier transform (SFT) on the converted frequency domain signal to convert it into a time delay-Doppler domain signal, where the resolution of the symplectic Fourier transform is determined by the resolution of the time delay and the Doppler shift. The time delay resolution is 0.5 nanoseconds, and the Doppler resolution is 5 hertz.

[0161] Step 2.8.4: Arrange the obtained time delay-Doppler domain signal into a matrix form, where each matrix element corresponds to a specific time delay and Doppler shift. The rows and columns of the matrix represent the time delay and the Doppler shift, respectively, which determines the dimension of the matrix, denoted as M (time delay dimension) and N (Doppler dimension). M = 128, N = 256 indicates that the time delay dimension of the matrix is 128 and the Doppler dimension is 256.

[0162] Step 2.8.5: According to the time delay Doppler signal matrix of the measured signal in step 2.1 and the ideal signal in step 1, extract the mean square error MSE, the peak signal-to-noise ratio PSNR, and the cross-correlation coefficient COR Dopper , and construct a time delay Doppler domain sub-evaluation model.

[0163] Specifically, the mean square error is represented as The peak signal-to-noise ratio is represented as The cross-correlation coefficient is represented as where X ddThe time delay-Doppler domain matrix form of the standard signal representing step 1; Y dd The time delay-Doppler domain matrix form of the received signal representing step 2.1.

[0164] In the embodiment, by comparing the time delay-Doppler matrix of the ideal signal and the measured signal, the calculated MSE = 0.002, and by the ratio of the maximum value of the signal and the mean square error, the calculated PSNR = 35 dB. Based on the cross-correlation calculation of the measured signal and the ideal signal, the calculated COR Dopper = 0.98.

[0165] Step 3: Compare the measured spacecraft radio frequency signal with the standard signal generated by the model library, and calculate the normalized results of each quantitative index in the six domains respectively: the time-frequency domain index S tf , the correlation domain index S r , the modulation domain index S m , the energy domain index S e , the polarization domain index S p , and the time delay-Doppler domain index S d ; according to the normalized results of the six domain sub-evaluation models under the measured data, calculate the weight of each domain evaluation result by the entropy weight method, and then calculate the weighted average value of each domain evaluation result to obtain the comprehensive quality evaluation result S of the spacecraft radio frequency signal.

[0166] Step 3.1: Calculate the calculation indexes in the six domains respectively.

[0167] Time-frequency domain index wherein the weights w tf1 , w tf2 , w tf3 , w tf4 , w tf5 all need to be adjusted according to the actual measurement and w tf1 + w tf2 + w tf3 + w tf4 + w tf5 = 1,

[0168] Correlation domain index wherein the weights w r1 , w r2 , w r3 all need to be adjusted according to the actual measurement by the expert experience method and w r1 + w r2 + w r3 = 1,

[0169] Modulation domain index wherein the weights w m1 , w m2 , w m3 , wm4 All need to be adjusted by expert experience method according to actual measurement and w m1 +w m2 +w m3 +w m4 =1,

[0170] Energy domain index

[0171] Polarization domain index

[0172] Delay Doppler domain index S dd =MSE·1 / PSNR+1 / PSNR1 / COR Dopper +MSE·1 / COR Dopper .

[0173] Step 3.2: Obtain the respective weights {w tf ,w r ,w m ,w e ,w p ,w dd} of the six domain sub-evaluation results by entropy weight method,

[0174] Step 3.2.1: Standardize the relevant original data involved in the sub-evaluation results of the six domains (i.e. 6 indexes) to eliminate the influence of dimension. Standardize the index x ij :

[0175]

[0176] Wherein, the jth index value of the ith sample, j=1, 2, 3, 4, 5, 6.

[0177] Step 3.2.2: Calculate the entropy value of each of the six indexes:

[0178]

[0179] Wherein, p ij is the proportion of the ith sample under the jth index: n is the sample size.

[0180] Step 3.2.3: Calculate the difference coefficient of each index:

[0181] Calculate the difference coefficient d j of the jth index:

[0182] d j =1-E j

[0183] Step 3.2.4: According to the difference coefficient, calculate the weight wj :

[0184]

[0185] Step 3.3: Calculate the comprehensive quality evaluation result S = w tf S tf +w r S r +w m S m +w e S e +w p S p +w dd S dd . Wherein, {w tf ,w r ,w m ,w e ,w p ,w dd} = {w1, w2, w3, w4, w5, w6}.

[0186] Step 4: According to the real-time quality evaluation result of the spacecraft radio frequency signal obtained in step 3, further analyze the noise interference and distortion effect of the signal in the transmission process, calculate and obtain the distortion effect coefficient β T of the transmitting end and its related link R and the distortion effect coefficient β R of the receiving end and its related link. Based on the distortion coefficient, a robust signal equalization algorithm is used to process the signal, compensate for the influence of distortion effect on signal quality, restore the original characteristics of the signal, and improve the robustness and anti-interference ability of signal transmission.

[0187] The distortion effect coefficient β T of the transmitting end and its related link R and the distortion effect coefficient β R of the receiving end and its related link are represented as

[0188] β T = β R = S = w tf S tf +w r S r +w m S m +w e S e +w p S p +w dd S dd

[0189] According to the distortion effect coefficient β T, distortion effect coefficient β of the receiving end and its related link R , and an equalization processing matrix G is designed based on the minimum mean square error criterion to cope with the influence of distortion and noise. By performing robust equalization processing on the received signal, the influence of non-ideal characteristics and distortion of the transmitting and receiving ends on the signal evaluation result is reduced.

[0190] In a multi-antenna system, the receiving end robust equalization matrix G based on the minimum mean square error criterion is expressed as:

[0191] G = W H H H (HWW H H H + Z) -1 ,

[0192] wherein,

[0193]

[0194] and W represents a transmitting end precoding matrix, and H represents an equivalent baseband channel matrix between the transmitting and receiving ends obtained through channel estimation.

[0195] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary and cannot be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application without departing from the principles and purposes of the present application.

Claims

1. A multi-domain evaluation and compensation method for aerospace radio frequency signal quality based on a model library, characterized in that: Includes the following steps, Step 1: Determine the signal system parameters based on various CCSDS standards and aerospace protocol systems, extract the relevant parameters of aerospace standards in the time-frequency domain, generate standard time-frequency domain radio frequency signals, and convert them to the correlation domain, modulation domain, energy domain, and polarization domain to establish a sub-model library of aerospace radio frequency signals in each domain; Based on the orthogonal time-frequency space-time (OTFS) transform method, the standard time-frequency domain signal generated by the model library is transformed to the time-delay Doppler domain, and a time-delay Doppler domain signal sub-model library is established; the signal system parameters include carrier frequency, coding method, modulation method, frame length, code rate, spreading method, spreading code type, spreading code length, spreading code rate, Doppler frequency offset range, and Doppler frequency offset rate of change; Step 2: Obtain the measured time-frequency domain signal through the receiver, and extract the time-domain parameters: spread spectrum signal noise magnitude n and noise margin ε. N Pseudocode zero-crossing distortion v parameter, extract frequency domain parameters to synthesize power spectrum deviation |ΔPSD(f)|, carrier leakage L carrier By integrating time-domain and frequency-domain information, a time-frequency domain sub-evaluation model is constructed. The received time-frequency domain measured signal is converted to the correlation domain, modulation domain, energy domain, polarization domain, and time-delay Doppler domain, and independent evaluation models are constructed in each of the six domains. The six independent evaluation models are then integrated using parallel data transmission and weighted data processing methods to construct a multi-domain comprehensive evaluation model. Step 2 is implemented as follows: Step 2.1: Obtain the measured time-frequency domain signal through the receiver; Step 2.1.1: Connect the receiving antenna to the software-defined radio receiver; Step 2.1.2: Amplify and filter the analog signal, and then convert the analog signal into a digital signal using a high-speed analog-to-digital converter (ADC); Step 2.1.3: Demodulate and process the signal using the receiver's internal or external digital signal processing (DSP) unit to finally obtain the signal's time-domain waveform and spectrum information; Step 2.2: Extract time-domain parameters from the measured signal obtained in Step 2.1, including the spread spectrum signal noise magnitude n and the noise margin ε. N The pseudocode zero-crossing distortion v parameter is used to construct a time-domain sub-evaluation model. Step 2.2.1: Perform coherent cumulative averaging on the measured signal obtained in Step 2.1 to obtain the superimposed chip after coherent cumulative averaging; Step 2.2.2: Analyze the waveform of the superimposed chips in Step 2.2.1 to check for obvious distortion; count the duration of each positive and negative chip of the signal within the code period, and compare the duration with the ideal signal chip generated by the model library in Step 1, and calculate the difference sequence between the superimposed chips in Step 2.2.1 and the ideal signal chip in Step 1. Step 2.2.3: Perform statistical analysis on the difference sequence obtained in Step 2.2.2, and calculate its maximum value, minimum value, peak value, standard deviation and mean; Based on the statistical results, the time-domain parameters are determined, including the spread spectrum signal noise magnitude n and the noise margin ε. N And the pseudocode zero-crossing distortion v; Step 2.3: Extract frequency domain parameters from the measured signal obtained in Step 2.1, including the synthesized power spectrum deviation |ΔPSD(f)| and carrier leakage L. carrier To construct a frequency domain sub-evaluation model; Step 2.3.1: Derive the signal power spectrum curve from the measured RF signal obtained in Step 2.1, and perform fitting analysis with the ideal signal power spectrum generated by the model library in Step 1. Test and compare the signal bandwidth of the fitted curve. The power spectrum curve (PSD) of the measured radio frequency signal is obtained by Fourier transforming the time-domain measured radio frequency signal in step 2.

1. meas (f): that is in, Represents the Fourier transform of signal x(t); Step 2.3.2: Import the measured RF signal from Step 2.1 into a spectrum analyzer or phase noise analyzer for detailed power spectrum analysis; then, compare it with the standard power spectrum PSD of the ideal signal generated by the model library in Step 1. ideal (f) By comparison, the frequency domain synthesized power spectrum deviation |ΔPSD(f)| at the main energy distribution points of the signal is calculated, and its calculation formula is expressed as: |ΔPSD(f)|=|PSD meas (f)-PSD ideal (f)| Step 2.3.3: Detect the carrier leakage L of the measured RF signal in Step 2.1 through power spectrum analysis. carrier The situation is assessed to determine the degree of spectral distortion in the signal. Carrier leakage ratio L carrier Using the measured carrier leakage power P carrier With total received signal power P total The ratio is expressed as: Carrier leakage power P carrier P can be directly read from the power spectrum of the measured signal. carrier =PSD meas (f carrier ), where f carrier It is the carrier frequency; Step 2.4: Extract the correlation domain parameters, cross-correlation function CCF(τ), and correlation loss from the measured signal obtained in Step 2.

1. Zero-crossing drift ε b (δ), construct the relevant domain sub-evaluation model; Step 2.4.1: Perform carrier stripping on the measured signal obtained in Step 2.1 to obtain the baseband waveform of the measured signal; Step 2.4.2: Calculate the normalized cross-correlation function CCF(τ) of the signal obtained in Step 2.4.1 and the local reference code in Step 1, and calculate the difference between the ideal power of the local reference code in Step 1 and the actual power of the signal obtained in Step 2.4.1, i.e., the correlation loss. Here, the correlation function CCF(τ) refers to the baseband signal s to be evaluated. rec1 The normalized cross-correlation function of (t) and the ideal band-limited signal s0(t) reproduced by the local signal model library is expressed as follows: In the formula, the integration time T p The duration of the main code cycle; Related losses The time-domain expression is: In the formula, For s rec1 The cross-correlation function of s(t) and s0(t), For s rec1 The autocorrelation function of (t), Let be the autocorrelation function of s0(t); Step 2.4.2: Plot the phase detection curve of the measured signal obtained in Step 2.1, showing the zero-crossing drift ε. b (δ) curve as a function of the lead-lag distance δ; The expression for the zero-crossing drift of the discrimination function curve is as follows: In the formula, ε1 and ε2 are two adjacent time-delay sampling points, δ is the lead-lag distance of the correlator, and D(ε,δ) is the expression of the discrimination function curve, i.e. Step 2.4.3: Within the transmission bandwidth of the measured signal obtained in Step 2.1, within the correlator interval of 0 to 1 chip, according to ε b (δ), further analyze the changes in the zero-crossing deviation SCB and its slope of the S-curve within the data segment of several code periods, and construct a related domain sub-evaluation model; Step 2.5: Extract the modulation domain parameters I / Q branch amplitude ratio Δa, I / Q branch phase orthogonality deviation Δθ, and constellation diagram error vector magnitude ε from the measured signal obtained in Step 2.

1. EVM Based on the amplitude difference Δp between signal components, a modulation domain sub-evaluation model is constructed. Step 2.5.1: The measured signal obtained in Step 2.1 is passed through an ideal FIR cutoff filter with a bandwidth equal to the transmit bandwidth, and then demodulated to obtain the baseband signal; Step 2.5.2: Sample the baseband signal obtained in Step 2.5.1, plot the constellation diagram, and calculate the I / Q branch amplitude ratio. Where |I (t) |and|Q (t) | represent the absolute amplitude averages of the I-branch and Q-branch signals, respectively; Step 2.5.3: Based on the constellation diagram obtained in Step 2.5.2, calculate the phase orthogonality deviation of the I / Q branches. Step 2.5.4: Based on the constellation diagram obtained in Step 2.5.2, calculate the deviation between the ideal signal points generated by the signal model library in Step 1 and the actual received signal points in Step 2.5.1, and calculate the error vector amplitude of the constellation diagram. Step 2.5.5: Calculate the component amplitude difference between the ideal signal generated by the signal model library in Step 1 and the measured baseband signal obtained in Step 2.5.

1. Step 2.6: Measure the power of the downlink measured signal obtained in Step 2.1 using a power meter or a Vector Network Analyzer (VNA); obtain a series of power readings through multiple measurements, perform statistical analysis on the power reading data, and extract the power variation range ∈ [value missing] from the energy domain parameters. P and stability σ P Among them, the power variation range is ∈ P This represents the maximum fluctuation amplitude of the signal power during the measurement process, while the stability σ P This is obtained by calculating the standard deviation of the power readings, reflecting the degree of signal power fluctuation; based on the downlink power variation range ∈ P and stability σ P Construct an energy domain sub-evaluation model; Wherein, the power variation range ∈ P =P max -P min , where P max and P min These represent the maximum and minimum values ​​of the measured signal power, respectively; the stability of the measured signal power. Step 2.7: For the measured signal obtained in Step 2.1 and the ideal signal generated by the signal model library in Step 1, use professional spectrum testing tools such as a Vector Network Analyzer (VNA) or a spectrum analyzer to measure these signals respectively; compare the frequency domain response ε of the measured signal obtained in Step 2.

1. Polar The frequency domain response ε' of the ideal signal generated by the signal model library in step 1 Polar First, determine the signal polarization. Then, test the signal using different polarization measurement settings, such as linear and circular polarized antennas. By gradually adjusting the polarization direction of the receiving antenna, observe and record the change in the measured signal power ΔP in step 2.1 and the change in the ideal signal power ΔP' generated by the model library in step 1. Polar ΔP-ε' Polar ΔP' is the quantitative index of the polarization domain, and a polarization domain sub-evaluation model is constructed. Step 2.8: Transform the measured signal obtained in Step 2.1 and the ideal signal generated by the model library in Step 1 to the time-delay Doppler domain. Based on the time-delay Doppler signal matrices of the measured signal in Step 2.1 and the ideal signal in Step 1, respectively, extract the mean square error (MSE), peak signal-to-noise ratio (PSNR), and cross-correlation coefficient (COR). Dopper Construct a time-delay Doppler domain sub-evaluation model; Step 2.8.1: Perform frame segmentation processing on the measured time-domain signal obtained in Step 2.1 and the ideal time-domain signal obtained in Step 1 respectively, dividing the continuous time-domain signal into multiple frames; Step 2.8.2: Perform a Discrete-Time Fourier Transform (DTFT) on each frame to convert the time-domain signal into a frequency-domain signal; Step 2.8.3: Perform a Sine Fourier Transform (SFT) on the converted frequency domain signal to convert it into a time-delay-Doppler domain signal; Step 2.8.4: Arrange the obtained time-delay-Doppler domain signals into a matrix form, where each matrix element corresponds to a specific time delay and Doppler frequency shift; Step 2.8.5: Based on the time-delay Doppler signal matrices of the measured signal in Step 2.1 and the ideal signal in Step 1, extract the mean square error (MSE), peak signal-to-noise ratio (PSNR), and cross-correlation coefficient (COR). Dopper Construct a time-delay Doppler domain sub-evaluation model; Mean square error is expressed as Peak signal-to-noise ratio is expressed as Cross-correlation coefficient is represented as Among them, X dd The time-delay Doppler domain matrix form of the standard signal from step 1; Y dd This represents the time-delay Doppler domain matrix form of the received signal from step 2.1; Step 3: Compare the measured spacecraft radio frequency signal with the standard signal generated by the model library, and calculate the normalized results of each quantization index in the six domains: Time-frequency domain index S tf Related domain indicators S r Modulation domain index S m Energy Domain Index S e Polarization domain index S p and delay Doppler domain index S d Based on the normalization results of the six domain sub-evaluation models under measured data, the weights of the evaluation results of each domain are calculated using the entropy weight method, and then the weighted average of the evaluation results of each domain is calculated to obtain the comprehensive quality evaluation result S of the spacecraft radio frequency signal. Step 4: Based on the real-time quality assessment results of the spacecraft radio frequency signal obtained in Step 3, further analyze the noise interference and distortion effects experienced by the signal during transmission, and calculate and obtain the distortion effect coefficient β of the transmitter and its related links. T The distortion effect coefficient β of the receiver and its associated links R Based on this distortion coefficient, a robust signal equalization algorithm is used to process the signal, compensate for the impact of distortion on signal quality, restore the original characteristics of the signal, and improve the robustness and anti-interference capability of signal transmission.

2. The method for multi-domain evaluation and compensation of aerospace radio frequency signal quality based on a model library as described in claim 1, characterized in that: Step 3 is implemented as follows: Step 3.1: Calculate the computational indices in each of the six domains; Time-frequency domain indicators Where the weight w tf1 ,w tf2 ,w tf3 ,w tf4 ,w tf5 All need to be adjusted according to the actual measurement situation and w tf1 +w tf2 +w tf3 +w tf4 +w tf5 =1, Related domain indicators Where the weight w r1 ,w r2 ,w r3 All need to be adjusted based on actual measurements using expert experience, and w r1 +w r2 +w r3 =1, Modulation domain index Where the weight w m1 ,w m2 ,w m3 ,w m4 All need to be adjusted based on actual measurements using expert experience, and w m1 +w m2 +w m3 +w m4 =1, Energy Domain Indicators Polarization domain index Delay Doppler Domain Index S dd =MSE·1 / PSNR+1 / PSNR·1 / COR Dopper +MSE·1 / COR Dopper ; Step 3.2: Obtain the respective weights {w} of the evaluation results for the six domains using the entropy weight method. tf ,w r ,w m ,w e ,w p ,w dd }, Step 3.2.1: Standardize the relevant raw data involved in the sub-evaluation results of the six domains to eliminate the influence of dimensions; for the indicator x ij Standardization process: Wherein, the j-th index value of the i-th sample, j = 1, 2, 3, 4, 5, 6; Step 3.2.2: Calculate the entropy value of each of the six indicators: Where, p ij It is the proportion of the i-th sample under the j-th indicator: n is the number of samples; Step 3.2.3: Calculate the coefficient of variation for each indicator: Calculate the difference coefficient d of the j-th indicator. j : d j =1-E j Step 3.2.4: Calculate the weight w of each indicator based on the difference coefficient. j : Step 3.3: Calculate the overall quality assessment result S = w tf S tf +w r S r +w m S m +w e S e +w p S p +w dd S dd ; where, {w tf ,w r ,w m ,w e ,w p ,w dd }={w1,w2,w3,w4,w5,w6}.

3. The method for multi-domain evaluation and compensation of aerospace radio frequency signal quality based on a model library as described in claim 2, characterized in that: In step 4, Distortion effect coefficient β of the transmitter and its related links T The distortion effect coefficient β of the receiver and its associated links R Represented as β T =β R =S=w tf S tf +w r S r +w m S m +w e S e +w p S p +w dd S dd Based on the obtained distortion effect coefficient β of the transmitter and its related links T The distortion effect coefficient β of the receiver and its related links R An equalization processing matrix G is designed based on the minimum mean square error criterion to address the effects of distortion and noise. By performing robust equalization processing on the received signal, the impact of non-ideal characteristics and distortion of the transceiver hardware on the signal evaluation results is reduced.

4. The method for multi-domain evaluation and compensation of aerospace radio frequency signal quality based on a model library as described in claim 3, characterized in that: In a multi-antenna system, the receiver robust equalization matrix G based on the minimum mean square error criterion is expressed as: G=W H H H (HWW H H H +Z) -1 , in, Furthermore, W represents the transmitting end precoding matrix, and H represents the equivalent baseband channel matrix between the transmitting and receiving ends obtained through channel estimation.

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