Spacecraft measurement and control signal real-time detection and anomaly analysis method based on modal decomposition

Abstract

The invention relates to a spacecraft measurement and control signal real-time detection and anomaly analysis method based on modal decomposition, and the method comprises the following steps: S1, real-time data collection: carrying out the data transmission of a sampling signal to an upper computer through employing a TCP / IP protocol on the basis of the real-time collection of a radio frequency signal through physical layer hardware equipment, setting a corresponding ip address and a port number, and carrying out the real-time collection of the data; real-time processing of the sampling data by the upper computer is realized; s2, locally storing the collected data and forwarding the collected data to an upper computer; s3, CFAR detection is carried out on the acquired signals; and S4, triggering an EMD decomposition algorithm after detecting that the signal is abnormal. The problem of difficulty in reproduction and tracing of the abnormal phenomenon in the test process of the spacecraft measurement and control subsystem is solved, the requirements of testers on real-time discovery and real-time processing of the abnormal phenomenon of the measurement and control signal are met, meanwhile, the reliability in the abnormality analysis and solving process is improved, and the manpower and material resource cost in the abnormality analysis process is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of spacecraft testing, and specifically relates to a real-time detection and abnormality analysis method of spacecraft measurement and control signals based on modal decomposition. Background Art

[0002] The underlying principles of the measurement and control subsystem are relatively complicated, the test equipment is highly complex, and the number of test equipment is large. When abnormal problems occur in the measurement and control system, personnel often simulate the status at the test site and wait for the problem to recur to find the source of the abnormal problem. The troubleshooting process takes test personnel a relatively long time and delays the progress of spacecraft research and development testing.

[0003] At the same time, as the needs of spacecraft users continue to increase, the complexity of spacecraft measurement and control systems is also gradually increasing. In the process of whole-satellite testing of spacecraft, the use of traditional testing methods will encounter many inconveniences, such as: 1) Because the testers are concentrated in the test hall, it is difficult to remotely monitor the spectrum analyzer, which usually leads to the failure to monitor the dynamics of the measurement and control signals in real time and the loss of characteristic information of abnormal signals; 2) When the measurement and control signal is abnormal, the tester will trace the source of the abnormality. Usually, they will first check the baseband decoding status and then analyze the spectrum of the measurement and control signal. However, for short-term and occasional abnormalities, it is usually difficult for the tester to trace the source of the problem. 3) When abnormal signals are discovered, testers usually conduct multi-dimensional analysis of the signal spectrum information, which requires a certain amount of experience and also takes a certain amount of time and energy. In relatively complex situations where the interference signal is not obvious, it will greatly affect the progress of testers in troubleshooting. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for real-time detection and anomaly analysis of spacecraft measurement and control signals based on modal decomposition, which solves the technical problems of the existing testing methods, such as the increase in the complexity of the measurement and control system and the increase in test tasks, the changes in equipment status and environmental conditions during the test process, or the presence of external interference signals, which lead to abnormal measurement and control signals, and the difficulty for testers to detect anomalies in time and trace the source of faults.

[0005] In order to solve the above problems, the technical solution of the present invention is: a method for real-time detection and abnormality analysis of spacecraft measurement and control signals based on modal decomposition, comprising the following steps: S1. Real-time data acquisition: Based on the real-time acquisition of RF signals by the physical layer hardware equipment, the TCP / IP protocol is used to transmit the sampled signals to the host computer, and the corresponding IP address and port number are set to enable the host computer to process the sampled data in real time; S2: Collect data and save them locally and forward them to the host computer: Perform FFT transformation on the real-time transmitted sampled data, calculate the real-time spectrum, power spectrum density and power spectrum of the RF signal, and display them to the host computer in real time through a graphical user interface, so that real-time test personnel can remotely monitor the RF signal; S3. Perform CFAR detection on the collected signal: The host computer performs sliding window ML-CFAR calculation on the real-time transmitted sampling data, and autonomously monitors the time domain signal through constant false alarm calculation of the time domain signal. When the measurement and control signal power jitters or changes abnormally, the host computer saves and screenshots the abnormal signal time domain data and spectrum information and sends out an alarm signal to prompt the tester; S4: The EMD decomposition algorithm is triggered after a signal anomaly is detected: When an anomaly is found through the ML-CFAR algorithm in the time domain information analysis, the abnormal data is further analyzed in time and frequency combination, and the empirical mode decomposition algorithm is used to perform modal decomposition on the abnormal signal. The spectral characteristics of the abnormal signal under different modes are further obtained and provided to the tester so that the tester can locate the anomaly and perform troubleshooting analysis.

[0006] Optionally, the sub-steps of S2 are as follows: S21: Perform FFT calculation on the real-time transmission signal to obtain the real-time spectrum of the signal; S22: Calculate the power spectrum density of the signal according to the sampling period of the hardware device; S23: Integrate the power spectrum density to obtain the power spectrum of the signal.

[0007] Optionally, the sub-steps of S3 are as follows: S31: pre-set the signal sliding window length N; S32: Apply the ML-CFAR algorithm to perform real-time data calculation based on the data in the window; S32: Perform real-time detection of whether the signal is abnormal based on the detection threshold and threshold multiplication factor pre-set by the ML-CFAR algorithm; S34: When an abnormality is detected in the measurement and control signal, an alarm message appears on the user interface, and step 4 is triggered to further decompose and analyze the abnormal information.

[0008] Optionally, the sub-steps of S4 are as follows: S41: By performing EMD decomposition on the abnormal signal, different IMF components are obtained; S42: For each modal function Obtain marginal spectra through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function; S43: The calculated IMF components are displayed in real time to the host computer via a graphical user interface, and the IMF signal spectrum is stored at a fixed time for the test personnel to review and analyze.

[0009] Optionally, the process of modal decomposition is: First, input signal X(t); Second, determine the local maximum and minimum of X(t); Third, fit the upper and lower envelope curves; Fourth, m(t) is the average value of the upper and lower envelopes; Fifth, the intermediate signal d(t) = X(t) - m(t); Sixth, determine whether d(t) satisfies the IMF condition; if not, then X(t) = d(t), and return to step 2; if so, then the IMF signal c n (t) = d(t); Seventh, after obtaining the first IMF in the sixth step, subtract IMF1 from the original signal as the new original signal, and then obtain IMF2 through the analysis of steps 2 to 5, and so on, until the decomposed signal is monotonic and the EMD decomposition is completed.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention relates to the field of spacecraft measurement and control signal analysis, anomaly detection and fault diagnosis. By remotely monitoring the spectrum of real-time acquisition signals, it is greatly convenient for spacecraft testers to remotely and uniformly monitor and analyze the quality of measurement and control signals.

[0011] 2. The present invention can autonomously monitor the abnormality of measurement and control signals through a real-time anomaly detection algorithm, effectively improving the timeliness of abnormality discovery by testers.

[0012] 3. The present invention can provide multi-component information features to spacecraft testers by performing empirical mode decomposition analysis on abnormal signals, provide testers with reference information, and efficiently and quickly locate abnormal points and conduct source tracing and troubleshooting. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Design a flow chart for an embodiment of the present invention; Figure 2 Design an application interface diagram for the present invention; Figure 3 A real-time monitoring flow chart for the implementation of the present invention; Figure 4 A flow chart of modal decomposition for implementing the present invention. DETAILED DESCRIPTION

[0014] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0015] Example: Figure 1-Figure 4 As shown, this embodiment provides a method for real-time detection and abnormality analysis of spacecraft measurement and control signals based on modal decomposition, comprising the following steps: S1. Real-time data acquisition: Based on the real-time acquisition of RF signals by the physical layer hardware equipment, the TCP / IP protocol is used to transmit the sampled signals to the host computer, and the corresponding IP address and port number are set to enable the host computer to process the sampled data in real time; S2: The collected data is locally saved and forwarded to the measurement and control server or host computer: FFT transformation is performed on the real-time transmitted sampled data to calculate the real-time spectrum, power spectrum density and power spectrum of the RF signal, and displayed in real time to the host computer through a graphical user interface, so that the real-time test personnel can remotely monitor the RF signal; S3. Perform CFAR detection on the collected signal: The host computer performs sliding window ML-CFAR calculation on the real-time transmitted sampling data, and autonomously monitors the time domain signal through constant false alarm calculation of the time domain signal. When the measurement and control signal power jitters or changes abnormally, the host computer saves and screenshots the abnormal signal time domain data and spectrum information and sends out an alarm signal to prompt the tester; S4: The EMD decomposition algorithm is triggered after a signal anomaly is detected: When an anomaly is found through the ML-CFAR algorithm in the time domain information analysis, the abnormal data is further analyzed in time and frequency combination, and the empirical mode decomposition algorithm is used to perform modal decomposition on the abnormal signal. The spectral characteristics of the abnormal signal under different modes are further obtained and provided to the tester so that the tester can locate the anomaly and perform troubleshooting analysis.

[0016] The present embodiment discloses a method for detecting anomalies in spacecraft measurement and control signals based on modal decomposition, including: real-time spectrum monitoring and power measurement of measurement and control signals, and anomaly detection and anomaly assessment based on the measured signal power; when an abnormality is found in the signal power, time-frequency analysis is performed on the measurement and control signal to obtain the main characteristic modes of the measurement and control signal to provide technical personnel with the ability to locate abnormal signals and troubleshoot faults.

[0017] By remotely monitoring the spectrum of real-time acquisition signals, it is greatly convenient for spacecraft testers to remotely and uniformly monitor and analyze the quality of measurement and control signals; through real-time anomaly detection algorithms, measurement and control signal anomalies can be monitored autonomously, effectively improving the timeliness of anomaly discovery for testers; by performing variational modal decomposition analysis on abnormal signals, multi-component information features can be provided to spacecraft testers, providing reference information for testers, and efficiently and quickly locating abnormal points and tracing the source for troubleshooting. This solves the problem of difficulty in reproducing and tracing abnormal phenomena during the testing of spacecraft measurement and control subsystems, meets the needs of testers for real-time discovery and real-time processing of measurement and control signal anomalies, and at the same time improves the reliability of the abnormal analysis and resolution process, and reduces the manpower and material costs during the abnormal analysis process.

[0018] Among them, the CFAR algorithm is a mean-type constant false alarm algorithm; the EMD decomposition algorithm is an empirical mode decomposition algorithm. In step S3, the window period can be flexibly set according to the hardware computing performance and the user's required computing accuracy. In step S4, different modes refer to each principal component.

[0019] In the real-time detection and abnormality analysis method of spacecraft measurement and control signals based on modal decomposition of this embodiment, the sub-steps of S2 are as follows: S21: Perform FFT calculation on the real-time transmission signal to obtain the real-time spectrum of the signal; S22: Calculate the power spectrum density of the signal according to the sampling period of the hardware device; S23: Integrate the power spectrum density to obtain the power spectrum of the signal.

[0020] In the real-time detection and abnormality analysis method of spacecraft measurement and control signals based on modal decomposition of this embodiment, the sub-steps of S3 are as follows: S31: pre-set the signal sliding window length N; S32: Apply the ML-CFAR algorithm to perform real-time data calculation based on the data in the window; S32: Perform real-time detection of whether the signal is abnormal based on the detection threshold and threshold multiplication factor pre-set by the ML-CFAR algorithm; S34: When an abnormality is detected in the measurement and control signal, an alarm message appears on the user interface, and step 4 is triggered to further decompose and analyze the abnormal information.

[0021] like Figure 3 As shown in the figure, the real-time monitoring process is: first, real-time sampling of the RF signal of the measurement and control link; second, setting the sliding window value; third, performing ML-CFAR processing on the data in the sliding window; fourth, performing constant false alarm comparison to determine whether there is any abnormality.

[0022] In the real-time detection and abnormality analysis method of spacecraft measurement and control signals based on modal decomposition of this embodiment, the sub-steps of S4 are as follows: S41: By performing EMD decomposition on the abnormal signal, different IMF components are obtained; S42: For each modal function Obtain marginal spectra through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function; S43: The calculated IMF components are displayed in real time to the host computer via a graphical user interface, and the IMF signal spectrum is stored at a fixed time for the test personnel to review and analyze.

[0023] Among them, Gaussian smoothing is The square root of the normed gradient.

[0024] like Figure 4 As shown, the process of modal decomposition is: First, input signal X(t); Second, determine the local maximum and minimum of X(t); Third, fit the upper and lower envelope curves; Fourth, m(t) is the average value of the upper and lower envelopes; Fifth, the intermediate signal d(t) = X(t) - m(t); Sixth, determine whether d(t) satisfies the IMF condition; if not, then X(t) = d(t), and return to step 2; if so, then the IMF signal c n (t) = d(t); Seventh, after obtaining the first IMF in the sixth step, subtract IMF1 from the original signal as the new original signal, and then obtain IMF2 through the analysis of steps 2 to 5, and so on, until the decomposed signal is monotonic and the EMD decomposition is completed.

Claims

1. A method for real-time detection and anomaly analysis of spacecraft measurement and control signals based on modal decomposition, characterized in that: The following steps are involved: S1. Real-time data acquisition: Based on the real-time acquisition of RF signals by the physical layer hardware equipment, the TCP / IP protocol is used to transmit the sampled signals to the host computer, and the corresponding IP address and port number are set to enable the host computer to process the sampled data in real time; S2. The collected data is stored locally and forwarded to the host computer: FFT transformation is performed on the real-time transmitted sampled data to calculate the real-time spectrum, power spectrum density and power spectrum of the RF signal, and displayed in real time to the host computer through a graphical user interface, so that the real-time test personnel can remotely monitor the RF signal; S3. Perform CFAR detection on the collected signal: The host computer performs sliding window ML-CFAR calculation on the real-time transmitted sampling data, and autonomously monitors the time domain signal through constant false alarm calculation of the time domain signal. When the measurement and control signal power jitters or changes abnormally, the host computer saves and screenshots the abnormal signal time domain data and spectrum information and sends out an alarm signal to prompt the tester; S4: The EMD decomposition algorithm is triggered after a signal anomaly is detected: When an anomaly is found through the ML-CFAR algorithm in the time domain information analysis, the abnormal data is further analyzed in time and frequency combination, and the empirical mode decomposition algorithm is used to perform modal decomposition on the abnormal signal. The spectral characteristics of each main component of the abnormal signal in different modes are further obtained and provided to the tester so that the tester can locate the anomaly and perform troubleshooting analysis.

2. The method for real-time detection and anomaly analysis of spacecraft measurement and control signals based on modal decomposition according to claim 1 is characterized in that: The sub-steps of S2 are as follows: S21: Perform FFT calculation on the real-time transmission signal to obtain the real-time spectrum of the signal; S22: Calculate the power spectrum density of the signal according to the sampling period of the hardware device; S23: Integrate the power spectrum density to obtain the power spectrum of the signal.

3. The method for real-time detection and abnormality analysis of spacecraft measurement and control signals based on modal decomposition according to claim 1 is characterized in that: The sub-steps of S3 are as follows: S31: pre-set the signal sliding window length N; S32: Apply the ML-CFAR algorithm to perform real-time data calculation based on the data in the window; S32: Perform real-time detection of whether the signal is abnormal based on the detection threshold and threshold multiplication factor pre-set by the ML-CFAR algorithm; S34: When an abnormality is detected in the measurement and control signal, an alarm message appears on the user interface, and step 4 is triggered to further decompose and analyze the abnormal information.

4. The method for real-time detection and abnormality analysis of spacecraft measurement and control signals based on modal decomposition according to claim 1 is characterized in that: The sub-steps of S4 are as follows: S41: By performing EMD decomposition on the abnormal signal, different IMF components are obtained; S42: For each modal function , find the marginal spectrum through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function; S43: The calculated IMF components are displayed in real time to the host computer via a graphical user interface, and the IMF signal spectrum is stored at a fixed time for the test personnel to review and analyze.

5. The method for real-time detection and abnormality analysis of spacecraft measurement and control signals based on modal decomposition according to claim 1 is characterized in that: The process of modal decomposition is: First, input signal X(t); Second, determine the local maximum and minimum of X(t); Third, fit the upper and lower envelope curves; Fourth, m(t) is the average value of the upper and lower envelopes; Fifth, the intermediate signal d(t) = X(t) - m(t); Sixth, determine whether d(t) satisfies the IMF condition; if not, then X(t) = d(t), and return to step 2; if so, then the IMF signal c n (t) = d(t); Seventh, after obtaining the first IMF in the sixth step, subtract IMF1 from the original signal as the new original signal, and then obtain IMF2 through the analysis of steps 2 to 5, and so on, until the decomposed signal is monotonic and the EMD decomposition is completed.

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