CN113242103B - Automatic monitoring method for interference signal source - Google Patents
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- CN113242103B CN113242103B CN202110504759.3A CN202110504759A CN113242103B CN 113242103 B CN113242103 B CN 113242103B CN 202110504759 A CN202110504759 A CN 202110504759A CN 113242103 B CN113242103 B CN 113242103B
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Abstract
The invention discloses an automatic monitoring method of an interference signal source, which carries out full-band scanning in a monitoring frequency range and outputs scanning data; extracting signals and signal parameters of a specified monitoring frequency band and an adjacent service frequency band from scanning data by adopting a signal sorting algorithm; synchronously acquiring signal data of all signals; analyzing the acquired signal data and outputting a suspected interference signal list; and carrying out correlation analysis on data of a period of time sequence correspondingly acquired by the signals in the suspected interference signal list and synchronous data correspondingly acquired by the signals in the adjacent service frequency bands, analyzing to obtain an interference source related to the suspected interference signals and outputting the interference source. The invention excavates the potential relation between the interference source and the interfered signal, realizes the automatic monitoring of the interference source by utilizing models such as signal sorting and signal correlation, and improves the discovery and investigation timeliness of the service frequency band interference.
Description
Technical Field
The invention relates to the technical field of radio monitoring, in particular to an automatic monitoring method for an interference signal source.
Background
In the prior art, interference complaints often occur in aviation frequency bands, satellite frequency bands and the like, and particularly, interference of adjacent frequency bands is prominent. For example, signals in the aviation frequency band are often subjected to spurious interference generated by illegal broadcasting stations (commonly called "black broadcasting") with low quality, which are abundant in recent years, or interference generated by intermodulation of broadcasting signals, interference generated by equipment failure of legal FM stations, and the like. While the signals in the satellite band are most susceptible to interference from adjacent 5G signals. At present, the commonly used interference signal monitoring means is still manual investigation, after interference complaints occur, signals of adjacent frequency bands are monitored one by one manually to determine an interference signal source, and the means cannot realize automatic monitoring and interference active early warning. In order to solve the problem, some currently-built radio monitoring devices perform interference early warning by analyzing data of multiple dimensions, such as intermodulation interference, voice quality detection, voice keyword detection, over-standard signal and new signal detection after template comparison, and the interference early warning analysis method specifically includes:
1. intermodulation interference: intermodulation interference is generated by a nonlinear circuit in a transmission channel, when signals of two or more different frequencies are input to the nonlinear circuit, many harmonics and combined frequency components are generated due to the action of the nonlinear device, wherein the combined frequency components close to the desired signal frequency can pass through a receiver smoothly to form the intermodulation interference.
The intermodulation interference includes:
1) third-order two-type intermodulation: 2F1-F2 ═ RF;
2) five-order two-type intermodulation: 3F1-2F2 ═ RF;
3) seven-order two-type intermodulation: 4F1-3F2 ═ RF;
4) third order type intermodulation: F1-F2+ F3 ═ RF;
5) fifth order third type intermodulation: 2F1-2F2+ F3 ═ RF;
6) seven-order three-type intermodulation: 2F1-3F2+2F3 ═ RF;
f1, F2, and F3 are interference source frequencies, and RF is a victim signal frequency.
2. Voice quality detection:
and performing quality scoring on the collected voice data, wherein the quality scoring is mainly realized by utilizing a machine learning algorithm. The voice quality is divided into four grades of excellent, good, medium and poor, and the voice data of different grades are collected for training to obtain a training model.
1) Extracting acoustic features from input voice data;
2) then, forecasting a four-classification model by using a training model;
3) and outputting the predicted quality grade, and if the quality grade is 'poor', carrying out interference early warning.
3. Voice keyword detection:
the voice keyword detection module developed by the science and technology communication airlines is used. If the module is used for detecting that the input voice data contains the preset keywords, the interference early warning is carried out.
4. Template alignment method (standard-out and new):
1) firstly, data acquisition is carried out on a frequency band to be monitored (frequency band data of a time period can be carried out, and frequency band data of a plurality of time periods can be superposed), a signal list is extracted according to a signal sorting algorithm (the algorithm refers to an article in 'application of shallow analysis broadband signal sorting in radio frequency spectrum monitoring' in 'Chinese radio' 2019 (07)), and the signal list is used as a template.
2) Extracting a signal list from newly input frequency band data through a signal sorting algorithm, comparing the signal list with a template, and performing new signal early warning if the signal list contains signals which are not contained in the template; and if the signal list contains signals with the level values exceeding the preset threshold value, performing exceeding signal early warning.
However, due to the influence of complex environment and self-factors of the equipment, the detection precision is not high, so that the false alarm rate of the interference signal is very high, and the normal work of related business departments is seriously influenced.
Disclosure of Invention
The invention aims to provide an automatic monitoring method of an interference signal source, which is used for solving the problems of low detection precision and high false alarm rate of adjacent frequency band interference monitoring in the prior art.
The invention solves the problems through the following technical scheme:
an automatic monitoring method for an interference signal source comprises the following steps:
step S1: the monitoring receiving device A carries out full-band scanning in a monitoring frequency range and outputs scanning data;
step S2: the monitoring receiving device A adopts a signal sorting algorithm to extract signals and signal parameters of a specified monitoring frequency band and an adjacent service frequency band from the scanning data;
step S3: the monitoring receiving device B synchronously acquires the signal data of all the signals sorted by the monitoring receiving device A;
step S4: performing suspected interference early warning analysis on all signals sorted by the monitoring receiving device A and signal data synchronously acquired by the monitoring receiving device B, and outputting a suspected interference signal list in a monitoring frequency band;
step S5: the monitoring receiving device B carries out correlation analysis on data of a period of time sequence correspondingly acquired by signals in the suspected interference signal list and synchronous data correspondingly acquired by signals in adjacent service frequency bands respectively, and a signal source related to the suspected interference signals, namely an interference source, is obtained through analysis;
step S6: and outputting an interference source list, and outputting the signal parameters, the interference early warning type and a period of time sequence data correspondingly acquired of each interference source signal.
The step S5 specifically includes:
step S51: it doesSignal X positioned at monitoring service frequency band and interfered j Synchronously acquiring X j And all signals S extracted from adjacent service frequency bands i At t 1 ～t m Data of time segment, i is 1,2, … …, n is number of signals extracted from adjacent service frequency band, t is m Is a time point;
step S52: will signal X j Signal S i Dividing into multiple segments of data to obtain X j (t 1 ,t 2 )，X j (t 2 ,t 3 )，...，X j (t m-1 ,t m )，S i (t 1 ,t 2 )，S i (t 2 ,t 3 )，...S i (t p-1 ,t p )...，S i (t m-1 ,t m ) P is 2,3, … …, m, and each data length is N points;
step S53: using a formula
Calculating the corresponding time slice (t) p-1 ,t p ) Middle signal X j Sum signal S i Is related to coefficient R XjSi (t p-1 ,t p )；
Step S54: setting a threshold value R of the correlation coefficient, counting all the correlation coefficients R larger than the threshold value R, and determining the time slice (t) in each period p-1 ,t p ) Neutralizing signal X j All signals S for which there is a correlation i And is marked as S' i;
step S55: statistic signal S' i The percentage of the number of the time slices is set, the percentage threshold Z is set, and the percentage is larger than the S corresponding to the threshold Z i As a final sum signal X j Correlated interferer signals are present.
In the prior art, the suspected interference signal list in the monitoring frequency band output in steps S1 to S4 is used to directly perform interference pre-warning, which results in a large number of normal signals, such as new signal pre-warning, in the pre-warning, and possibly results in part of the normal signals being listed as interference signals because signals in different time periods or bursts are not considered during template establishment, and meanwhile, the signals cannot be determined to be associated with the interfered signals. In contrast, according to the scheme, after the suspected interference signal list in the output monitoring frequency band is obtained, correlation analysis is performed on the suspected interference signal and the interfered signal, a signal really correlated with the interfered signal is screened out, and the false alarm rate is obviously reduced.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention excavates the potential relation between the interference source and the interfered signal, realizes the automatic monitoring of the interference source by utilizing models such as signal sorting and signal correlation, reduces the false alarm rate and improves the discovery and investigation timeliness of the service frequency band interference. Meanwhile, the working mode is changed from a passive receiving task to an active working mode, and the problem of tracing when the interference searching is carried out is fundamentally solved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of frequency band scan data;
FIG. 3 is a schematic diagram of signal sorting;
FIG. 4 is a schematic diagram of correlation analysis.
Detailed Description
The present invention will be described in further detail with reference to examples, but the embodiments of the present invention are not limited thereto.
Example (b):
referring to fig. 1, an automatic monitoring method for an interference signal source is divided into three parts: the first part is scanning frequency band data acquisition signals; the second part is that a suspected interference signal list is determined by a plurality of analysis methods for the extracted signal data; and the third part is to perform correlation analysis and tracing on the suspected interference signal list and all the extracted signals, determine the final interference signal and display the final interference signal on the terminal.
Specifically, the method comprises the following steps:
step S1: the monitoring receiving device a performs full-band scanning in the monitoring frequency range, and obtains the scanned spectrum data through the frequency band scanning function of the monitoring receiving device a as shown in fig. 2;
step S2: the monitoring receiving device a extracts signals and signal parameters of a designated monitoring frequency band and an adjacent service frequency band from scanning data by using a signal sorting algorithm, as shown in fig. 3, data with solid dots is used for extracting signals for the signal sorting algorithm, and the algorithm can output signal parameters such as frequency points, bandwidths, level values and the like of the signals;
step S3: the monitoring receiving device B synchronously acquires the signal data of all the signals sorted by the monitoring receiving device A; the signal data may be narrowband IQ data, spectrum data, or demodulated voice data, etc.;
what signals and parameters of the signals (including frequency point, bandwidth, level value, etc.) are output in step S2, and then according to the number of the output signals and the corresponding parameters, signal data, including narrowband IQ data, spectrum data or demodulated voice data, are continuously acquired for the signals through a data acquisition module in step S3. Since the analysis method in the suspected disturbance early warning analysis module in the monitoring receiving device B needs to perform comprehensive analysis by using the signal parameter output in step S2 and the signal data output in step S3, the input in step S4 includes the outputs in steps S2 and S3.
Step S4: inputting the output of the step S3 of the step S2 to a suspected interference early warning analysis module in the monitoring and receiving device B for suspected interference early warning analysis, which may be intermodulation interference, voice quality detection, voice keyword detection, or detection of an out-of-standard signal and a new signal after template comparison, and outputting a list of suspected interference signals in the monitoring frequency band;
step S5: the monitoring receiving device B carries out correlation analysis on data of a period of time sequence correspondingly acquired by signals in the suspected interference signal list and synchronous data correspondingly acquired by signals in adjacent service frequency bands respectively, and a signal source related to the suspected interference signals, namely an interference source, is obtained through analysis;
the method specifically comprises the following steps:
step S51: determining a disturbed signal X in a monitoring traffic band j Synchronous acquisition of X j And all signals S extracted from adjacent service frequency bands i At t 1 ～t m The data of the time period, i ═ 1,2, … …, n,n is the number of signals extracted from the adjacent service frequency band, t m Is a time point;
step S52: will signal X j Signal S i Dividing into multiple segments of data to obtain X j (t 1 ,t 2 )，X j (t 2 ,t 3 )，...，X j (t m-1 ,t m )，S i (t 1 ,t 2 )，S i (t 2 ,t 3 )，...S i (t p-1 ,t p )...，S i (t m-1 ,t m ) P is 2,3, … …, m, and each data length is N points;
step S53: using a formula
Calculating the corresponding time slice (t) p-1 ,t p ) Middle signal X j Sum signal S i Is related to coefficient R XjSi (t p-1 ,t p )；
Step S54: setting a threshold value R of the correlation coefficient, counting all the correlation coefficients R larger than the threshold value R, and determining each time slice (t) p-1 ,t p ) Neutralizing signal X j All signals S for which there is a correlation i Is recorded as S' i ；
As shown in fig. 4, assuming that 7 signals of adjacent frequency bands are selected by signal sorting, one signal of the monitoring frequency band is assumed to be a suspected interference signal, performing correlation analysis on the signal and the 7 signals of the adjacent frequency bands respectively, taking synchronous frame data of each signal in different time periods during analysis, then counting the number of signals related in different time periods, and synthesizing to obtain the interference signal. As shown, with a suspected interference signal X 1 Correlated S' i Has S 2 、S 4 、S 5 The correlation numbers are 3, 1 and 3, respectively.
Step S55: statistic signal S' i The percentage of the number of the time slices is set, the percentage threshold Z is set, and the percentage is larger than the S corresponding to the threshold Z i As a final sum signal X j There is a coherent interferer signal. As can be seen from FIG. 4, if there are 3 time slices, S 2 And S 5 100% of S 4 The percentage is 33%, and the percentage threshold Z is assumed to be 80%, thus obtaining the final sum X j The interference source signal with correlation is S 2 And S 5 。
Step S6: and outputting an interference source list, and outputting the signal parameters, the interference early warning type and a period of time sequence data correspondingly acquired of each interference source signal.
Although the present invention has been described herein with reference to the illustrated embodiments thereof, which are intended to be preferred embodiments of the present invention, it is to be understood that the invention is not limited thereto, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure.
Claims (1)
1. An automatic monitoring method for an interference signal source, comprising:
step S1: the monitoring receiving device A carries out full-band scanning in a monitoring frequency range and outputs scanning data;
step S2: the monitoring receiving device A adopts a signal sorting algorithm to extract signals and signal parameters of a specified monitoring frequency band and an adjacent service frequency band from scanning data;
step S3: the monitoring receiving device B synchronously acquires the signal data of all the signals sorted by the monitoring receiving device A;
step S4: performing suspected interference early warning analysis on all signals sorted by the monitoring receiving device A and signal data synchronously acquired by the monitoring receiving device B, and outputting a suspected interference signal list in a monitoring frequency band;
step S5: the monitoring receiving device B carries out correlation analysis on data of a period of time sequence correspondingly acquired by signals in the suspected interference signal list and synchronous data correspondingly acquired by signals in adjacent service frequency bands respectively, and a signal source related to the suspected interference signals, namely an interference source, is obtained through analysis;
step S6: outputting an interference source list, and outputting a signal parameter, an interference early warning type and a period of time sequence data which are correspondingly collected of each interference source signal;
the step S5 specifically includes:
step S51: determining a disturbed signal X in a monitoring traffic band j Synchronous acquisition of X j And all signals S extracted from adjacent service frequency bands i At t 1 ～t m Data of time segment, i is 1,2, … …, n is number of signals extracted from adjacent service frequency band, t is m Is a time point;
step S52: will signal X j Signal S i Dividing into multiple segments of data to obtain X j (t 1 ,t 2 )，X j (t 2 ,t 3 )，...，X j (t m-1 ,t m )，S i (t 1 ,t 2 )，S i (t 2 ,t 3 )，...S i (t p-1 ,t p )...，S i (t m-1 ,t m ) P is 2,3, … …, m, and each data length is N points;
step S53: using the formula
Calculating the corresponding time slice (t) p-1 ,t p ) Middle signal X j Sum signal S i Is related to coefficient R XjSi (t p-1 ,t p )；
Step S54: setting a threshold value R of the correlation coefficient, counting all the correlation coefficients R larger than the threshold value R, and determining each time slice (t) p-1 ,t p ) Neutralizing signal X j All signals S for which there is a correlation i Is recorded as S' i ；
Step S55: statistic signal S' i The percentage of the number of the time slices is set, the percentage threshold Z is set, and the percentage is larger than the S corresponding to the threshold Z i As a final sum signal X j Correlated interferer signals are present.
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| US9319916B2 (en) * | 2013-03-15 | 2016-04-19 | Isco International, Llc | Method and appartus for signal interference processing |
| JP6165348B2 (en) * | 2014-09-05 | 2017-07-19 | 三菱電機株式会社 | Interference identification apparatus, radio communication apparatus, and interference identification method |
| CN107682102B (en) * | 2017-11-24 | 2021-02-26 | 成都中星世通电子科技有限公司 | Method for automatically monitoring and identifying interference signal of GSM-R communication system |
| CN108512617B (en) * | 2018-04-18 | 2020-11-24 | 成都大公博创信息技术有限公司 | Automatic early warning method and device for potential interference of aviation frequency band |
| CN208754310U (en) * | 2018-09-13 | 2019-04-16 | 成都和跃科技有限公司 | A kind of civil aviaton's very high frequency(VHF) radio-interference signals automated watch-keeping facility |
| CN108847907A (en) * | 2018-09-13 | 2018-11-20 | 成都和跃科技有限公司 | A kind of civil aviaton's very high frequency(VHF) radio-interference signals automated watch-keeping facility |
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| CN106301623A (en) * | 2016-09-09 | 2017-01-04 | 成都定为电子技术有限公司 | The interference source detection identification method of a kind of spectrum monitoring unmanned plane and device thereof |
| CN112165365A (en) * | 2020-09-29 | 2021-01-01 | 封开低频时码授时台 | Wireless spectrum interference monitoring system and method for low-frequency time code monitoring system |
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