Electromagnetic accompanying signal identification method and system based on collision and accompanying analysis

Abstract

The invention provides an electromagnetic accompanying signal identification method and system based on collision and accompanying analysis, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: obtaining different types of electromagnetic original signal data, and carrying out the preprocessing of the data to form a standard electromagnetic signal; extracting a central frequency point, a bandwidth and a modulation mode of a standard electromagnetic signal, and matching the central frequency point, the bandwidth and the modulation mode of the signal with a knowledge base signal according to a knowledge base matching method so as to separate an existing signal from an unknown signal; classifying the signals according to a grouping rule to form different sets, and extracting multi-scale feature data from the signals of the different sets; and performing signal collision analysis on the multi-scale feature data of different sets to obtain a collision set of the same signal, and performing adjoint analysis on the signals in the collision set to obtain adjoint signals of all the signals. According to the invention, an accompanying signal or a collision signal of the signal can be effectively found out, and an accompanying signal source can be quickly found out.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electromagnetic signal processing, and in particular to an electromagnetic accompanying signal recognition method and system based on collision and accompanying analysis. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the continuous advancement of information construction, the rapid development of cyberspace, and the serious threat to network information security. However, information security issues exist not only in the network field we are in contact with, but also in the leakage of electromagnetic radiation, wireless eavesdropping and stealing photos, illegal use of mobile terminals and other equipment, which will also put information security in crisis. Therefore, electromagnetic information security protection should not be underestimated. On the one hand, due to the deep evolution of the means of stealing secrets towards the physical or facility layer, the technical protection of discovering theft has become more difficult; on the other hand, various types of stealing equipment have developed in miniaturization, light weight, and diversified functions, making it more difficult to discover theft. Due to the continuous innovation of stealing equipment, there are not only traditional eavesdropping devices and pinhole cameras, but also new means or equipment for remote stealing secrets such as lasers, infrared, and simulated biological ones. This poses a severe challenge to the safety of the electromagnetic space environment. How to quickly and real-time identify and analyze abnormal signals will be the key capability to ensure the safety of the electromagnetic space.

[0004] The existing methods mainly merge the protocol analysis data of WiFi and Bluetooth according to the same name, merge the spectrum signals according to the same frequency bandwidth, and match the knowledge base to merge the signals of the same knowledge base. However, there is no accompanying analysis function for electromagnetic signals, which makes it difficult to discover some new eavesdropping equipment and abnormal signals, and the efficiency is low. Moreover, there is only a processing function. After the signal data is merged and deduplicated, no effective grouping analysis is performed, and related signals with correlation cannot be found. The judgment effect on abnormal signals is not ideal, and it is impossible to effectively conduct rapid and accurate analysis and processing. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes an electromagnetic accompanying signal recognition method and system based on collision and accompanying analysis, which extracts the characteristics of frequency points and bandwidths through different electromagnetic original signal data, identifies different types of signals, performs collision and accompanying analysis on different types of signals, and quickly identifies accompanying signal sources with related relationships through a two-stage signal screening method.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] The electromagnetic accompanying signal recognition method based on collision and accompanying analysis includes:

[0008] Acquire different types of electromagnetic raw signal data and pre-process them to form standard electromagnetic signals;

[0009] Extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signals into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups;

[0010] Perform signal collision analysis on multi-scale feature data of different sets to obtain collision sets of the same signal, and then perform adjoint analysis on the signals in the collision set to obtain adjoint signals of all signals;

[0011] Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions:

[0013] Electromagnetic accompanying signal recognition system based on collision and accompanying analysis, including:

[0014] The signal acquisition module is used to acquire different types of electromagnetic raw signal data and perform preprocessing to form standard electromagnetic signals;

[0015] The feature extraction module is used to extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signal into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups;

[0016] The signal analysis module is used to perform collision analysis on signals of different sets of multi-scale feature data to obtain collision sets of the same signals, and then perform concomitant analysis on the signals in the collision set to obtain concomitant signals of all signals;

[0017] Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis is implemented.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis is implemented.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the electromagnetic accompanying signal recognition method based on collision and accompanying analysis.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention discloses an electromagnetic accompanying signal identification method based on collision and accompanying analysis, which receives signal data collected by electromagnetic equipment, and completes steps such as signal formatting and signal correction to form standard electromagnetic signal format data, and then deduplicates the signal; identifies a specific signal by extracting features such as frequency and bandwidth of the original signal, classifying the signal category, and matching the knowledge base; performs collision analysis and accompanying analysis on the processed signal to find the same signal or accompanying signal, which can greatly reduce the signal magnitude and increase the signal accuracy, thereby reducing the order of magnitude of the alarm, and allowing operators to handle and judge more accurately; on the other hand, through signal collision and accompanying analysis of the signal, it is possible to more effectively find the accompanying signal or the collision signal of the signal, and quickly find the accompanying signal source.

[0026] The present invention discloses an electromagnetic companion signal recognition method based on collision and companion analysis, which uses an overlapping merging algorithm to remove duplicates, reduce the number of signals, merge the same signals as much as possible, and reduce the number and frequency of subsequent collisions and alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0028] Figure 1 This is a schematic diagram of an electromagnetic accompanying signal recognition method for collision and accompanying analysis according to an embodiment of the present disclosure;

[0029] Figure 2 It is the characteristic attribute of the electromagnetic signal of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] Explanation of terms

[0034] Center frequency: the middle frequency between the signal start frequency and the cutoff frequency;

[0035] Bandwidth: the difference between the start frequency and the cutoff frequency of the signal;

[0036] Modulation method: the method used by a signal to modulate from a low frequency to a high frequency when transmitted in a wireless channel;

[0037] RBW: The frequency resolution bandwidth used when performing FFT Fourier transform on the signal. This parameter will affect the recognition accuracy of the center frequency and bandwidth (the smaller the RBW, the more accurate the recognition of the center frequency and bandwidth);

[0038] SNR: The ratio of the measured signal power to the noise power when the signal is received (signal-to-noise ratio);

[0039] RSSI: signal strength, expressed in dBm;

[0040] Time: The time when the signal is generated

[0041] Geographic location: The location of a signal-emitting device in physical space.

[0042] Example 1

[0043] In one embodiment of the present disclosure, a method for identifying electromagnetic accompanying signals based on collision and accompanying analysis is provided, comprising the following steps:

[0044] Step 1: Obtain different types of electromagnetic raw signal data and pre-process them to form standard electromagnetic signals;

[0045] Step 2: extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, and match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal;

[0046] Step 3: Divide the existing signals into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups;

[0047] Step 4: Perform signal collision analysis on multi-scale feature data of different sets to obtain collision sets of the same signal, and then perform adjoint analysis on the signals in the collision set to obtain adjoint signals of all signals;

[0048] Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

[0049] As an embodiment, the present invention discloses an electromagnetic accompanying signal identification method based on collision and accompanying analysis. The electromagnetic signal accompanying analysis is divided into three layers according to the function, namely signal processing, signal identification and signal analysis. Among them, signal processing is to receive the signal data collected by the electromagnetic equipment, and complete the steps of signal formatting, signal correction, etc. to form standard electromagnetic signal format data, and then deduplicate the signal. Signal identification is to identify the specific signal by extracting the characteristics of the original signal such as the frequency point and bandwidth, classifying the signal category, matching the knowledge base, etc. Signal analysis is to perform collision analysis and accompanying analysis on the processed signal to find the same signal or accompanying signal. The specific implementation process is as follows:

[0050] Step 1: Acquire different types of electromagnetic raw signal data and perform preprocessing to form standard electromagnetic signals; the preprocessing includes signal formatting, signal correction and signal deduplication steps.

[0051] Specifically, step 1.1: the process of signal formatting includes:

[0052] Step 1): Load the signal standard signal formatting template and extract the standardized fields;

[0053] Step 2): According to the signal standardization format template, complete the missing fields and remove the redundant fields;

[0054] Step 3): The formatted signal data is used for the next step of signal correction.

[0055] Step 1.2: The specific steps of signal correction are as follows:

[0056] Step 1): Record the range of the signal according to the configured RBW and bandwidth fluctuation values;

[0057] Step 2): Input signal data, loop the recorded signal range list in step 1) for comparison and calculation. If the input signal frequency is within the recorded signal range, it is considered to be the same signal and the calculation is stopped. If it is not within the range, the calculation is continued until all the loop comparisons are completed;

[0058] Step 3): For signals that are not within the range, the relevant range and signal frequency bandwidth need to be added to the signal range list in step 1);

[0059] Step 4): The signals within the range need to be corrected according to the range and unified into the same signal;

[0060] Step 5): De-duplicate the remaining signal after the de-skew processing.

[0061] Step 1.3: The specific process of signal deduplication includes merging deduplication and overlapping merging deduplication.

[0062] The merging steps include:

[0063] Step 1): Circulate the signal list and compare according to the center frequency offset by n RBW;

[0064] Step 2): Circulate the current signal list and compare according to the bandwidth offset m RBW;

[0065] Step 3): If two signals satisfy the condition that the center frequency offset is within n RBWs and the bandwidth offset is within m RBWs, the two signals are considered to be the same signal and are merged and deduplicated;

[0066] Step 4): The signal list that has been merged and deduplicated by strong matching rules is then merged and deduplicated by overlapping.

[0067] Secondly, overlap merging and deduplication are performed according to the overlap merging rules. The specific rules are as follows:

[0068] Assuming that the signal involved in the comparison is 1 (center frequency f1, bandwidth bw1) and signal 2 (center frequency f2, bandwidth bw2), the frequency band span of signal 1 is [f1-bw1 / 2, f1+bw1 / 2], and the frequency band span of signal 2 is [f2-bw2 / 2, f2+bw2 / 2];

[0069] If the frequency band spans of signal 1 and signal 2 do not overlap, it is considered that signal 1 and signal 2 cannot be combined.

[0070] If the frequency band spans of signal 1 and signal 2 overlap, it is considered that signal 1 and signal 2 can be merged, and the frequency band of the merged new signal includes the frequency bands of signal 1 and signal 2.

[0071] If f1-bw1 / 2≤f2-bw2 / 2 and f2-bw2 / 2≤f1+bw1 / 2,

[0072] Or if f2-bw2 / 2≤f1-bw1 / 2 and f1-bw1 / 2≤f2+bw2 / 2, the two signals are considered to overlap.

[0073] Let fmin be the smaller value of f1-bw1 / 2 and f2-bw2 / 2, and fmax be the larger value of f1+bw1 / 2 and f2+bw2 / 2. After the signals are merged, the new frequency band span is [fmin,fmax].

[0074] When signal 1 overlaps with signal s in the signal library, the frequency band span of the new signal after merging cannot exceed the frequency band span of signal s, and the new signal is marked as a quasi-signal library signal.

[0075] Furthermore, the specific merging process is as follows: the first frame signal generated by each spectrum scan is merged with the signal of the signal library in turn, and the subsequent frame signal is merged with the previous frame signal. The signal generated by merging the signal library signal cannot exceed the corresponding signal frequency band span.

[0076] If the signal of this frame generated by spectrum scanning overlaps with multiple signals of the previous frame, the signal with the largest overlapping frequency band is merged, and the merged new signal cannot exceed the frequency band span of the signal in the signal library.

[0077] The spectrum scan considers that if the signal of this frame generated overlaps with multiple signal library signals (including quasi-signal library signals) of the previous frame, and if the overlapping signals belong to multiple signal library signals, the signal with the largest overlapping frequency band is merged; if the overlapping multiple quasi-signal library signals of the previous frame belong to the same signal library, the multiple quasi-signal library signals and the signal of this frame are merged into a new signal, and the frequency band span of the new signal cannot exceed the frequency band span of the signal library signal.

[0078] Step 2: Extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signals into different sets according to the grouping rules, and extract multi-scale feature data from the signals of different sets;

[0079] Specifically, in the signal recognition stage, steps such as signal knowledge base matching, signal classification, and feature extraction are performed on the processed signal.

[0080] Step 2.1: When matching the central frequency and bandwidth of the knowledge base, since different protocols have different error tolerances for signals, the matching rules need to be dynamically adjusted according to different protocols. The matching rules for different protocols are as follows:

[0081] 1) WIFI, Bluetooth knowledge base:

[0082] 2.4G WIFI signal: center frequency error, bandwidth is 100% to 50%;

[0083] 5.2G, 5.8G WIFI signal: center frequency error, bandwidth is 100% to 50%;

[0084] Classic Bluetooth: center frequency error, bandwidth is 100% to 50%;

[0085] Low-power Bluetooth: center frequency error, bandwidth is 100% to 50%;

[0086] 2) Radio and television knowledge base: center frequency error, bandwidth is 100% to 50%;

[0087] 3) Trunking communication knowledge base: center frequency error, bandwidth is between 100% and 50%;

[0088] 4) Mobile communication knowledge base: The center frequency falls within the frequency band, and the signal bandwidth range does not exceed the frequency band range + / -1M, which means the match is successful.

[0089] According to the characteristics of electromagnetic signal emission, the input signal is matched and identified with the knowledge base in combination with the strategy, and the signal is labeled. The specific steps are as follows:

[0090] Step 1): Loading knowledge base information such as regional radio stations, wireless audio and video frequency bandwidth, and mobile terminals to form a cache;

[0091] Step 2): According to the classification grouping in the signal classification, obtain the cache knowledge base of different classifications;

[0092] Step 3): Match the center frequency, bandwidth, modulation mode, etc. of the signal according to the knowledge base content such as center frequency, bandwidth, modulation mode, etc.;

[0093] Step 4): Circulate the knowledge base content under the category and perform step 3) matching;

[0094] Step 5): If the knowledge base is matched, the loop is stopped, the frequency, bandwidth and modulation mode of the signal are corrected, and the signal is labeled as a wireless device or an existing signal of a broadcasting station;

[0095] Step 6): If the knowledge base is matched cyclically and no match is found after all the steps are completed, the signal is marked as an unknown signal label;

[0096] Step 7): Then classify the signal after matching the knowledge base.

[0097] Further, step 2.2: classifying the signal after matching the knowledge base includes:

[0098] Step 1): Classify and group the electromagnetic signal sensors that generate the signal

[0099] Step 2): Classify and group the signals according to the time interval in which they were generated

[0100] Step 3): Group by WIFI signal

[0101] Step 4): Group by Bluetooth signal classification

[0102] Step 5): Classify and group by other signals such as trunking communication, mobile communication, etc.

[0103] Step 6): After completing the classification and grouping of the signals, a signal list is formed and the next step of signal feature extraction is carried out.

[0104] Step 2.3: The extraction of multi-scale feature data includes the following steps:

[0105] Step 1): Extract the frequency, bandwidth, time interval, signal strength, and signal-to-noise ratio of the signal according to the signal list;

[0106] Step 2): Identify the modulation mode of the signal and identify the modulation mode characteristics of the signal;

[0107] Step 3): Calculate the geographical location of the signal based on the location of the signal's transmitting source;

[0108] Step 4): Extract features of different terminal fingerprints based on the spectrum signals, frequencies of mobile terminals, WIFI, Bluetooth and other devices, and the characteristics of electromagnetic signal transmission;

[0109] Among them, terminal fingerprint mainly refers to frequency, bandwidth, strength, modulation method, etc. If it is WIFI or Bluetooth, there is also information such as name and manufacturer.

[0110] Step 5): For WIFI and Bluetooth, it is also necessary to extract characteristic attributes such as name, manufacturer, and traffic;

[0111] Step 6): Use the multi-scale feature data for subsequent signal analysis.

[0112] Step 3: Perform signal collision analysis on multi-scale feature data of different sets to obtain collision sets of the same signal, and then perform adjoint analysis on the signals in the collision set to obtain adjoint signals of all signals;

[0113] The signal analysis phase mainly includes signal collision analysis and signal concomitant analysis.

[0114] Step 3.1: Collision analysis of signals

[0115] Signal collision analysis refers to finding the same signal in two signal sets, which is similar to the set intersection operation in mathematics, but the rules are different. Not all signals with the same frequency are considered the same signal. It first performs pairwise collisions on the sets, collides the result set after the collision with other sets, and obtains the final collision set of the same signal. The specific steps are as follows:

[0116] Step 1): For signal set A and signal set B, A set = {a, b, c, d, e...}, B set = {a', b', c', d', e'...}, similarly solve A∩B;

[0117] Step 2): Loop through the B set, take the signal a', and calculate the frequency range of the signal a' by setting the bandwidth percentage. For example, the center frequency of the signal a' is 1000MHz, the bandwidth is 10MHz, and the set percentage is 2%, then the frequency range is 999.8MHz to 1000.2MHz. At this time, loop through the A set to compare whether the frequency of the signal in the A set is within this range. If it is, it means that it is the same signal as a', and finally form a'={a,c}. If there are more than one element, such as a and c, find the signal in this set whose bandwidth is closer to the bandwidth of the a' signal, for example, if it is c, then save a'={c}. If there is only one element, there is no need to compare the bandwidth proximity principle, and it is directly a'={c}. Put c into the final result set and delete the c signal from the set A; if not found, discard the a' signal, that is, the a' signal has not collided with the signal.

[0118] Step 3): Loop the second signal of set B, and continue to find the collision signal of the second signal according to step 2. If the signal x in set A can be found, put x into the final result set and delete the x signal from set A. Loop and compare until the loop of set B ends. If there are no elements in set A before set B ends, there is no need to compare and the loop ends directly.

[0119] Step 4): After the loop is finished, a list is formed, a'={c}, d'={d}, e'={f}, ..., and the final collision result is saved.

[0120] If multiple signal sets need to collide, they should first collide with each other, and the resulting collision result set should then collide with the next result set. For example, if there are three signal sets that need to collide, namely A, B, and C, A and B should be collided first, and the resulting result set D should then collide with C to obtain the final collision set of the same signal and save it.

[0121] Step 3.2: Adjoint analysis of the signal

[0122] Perform concomitant analysis on the signals in the collision set. The concomitant of a signal is simply understood as the appearance of a signal, and other signals appear within a short time range of the appearance of this signal, which is called an occurrence. If the number of occurrences meets certain conditions, it is considered to be the concomitant signal of this signal. For example: ABCDE 5 signals, take signal A as the main signal, and find its concomitant signal, as follows:

[0123] When A appears for the first time, BD also appears within 10 seconds, then BD is counted as the accompanying signal of A signal once;

[0124] The second time A appears, CD E appears within 10 seconds, then CDE is used as the accompanying signal of A signal, among which C is counted once, E is counted once, and D signal count becomes 2 times;

[0125] The third time A appears, D signal appears within 10 seconds, then D signal is used as the waiting accompanying signal of A signal, and the D signal count becomes 3 times

[0126] When it is defined that the companion signal must appear at least 3 times within 10 seconds, among the above signals, only the D signal is the companion signal of the A signal.

[0127] Although the above definition of accompanying signals is relatively simple and unambiguous, in practice the above method cannot filter out two continuous signals, such as two or more broadcast signals. These broadcast signals may appear at the same time at every moment. According to the above definition, these are all accompanying signals, but this is obviously not what the user expects. It is necessary to find sporadic accompanying signals to exclude the above broadcast signals or other continuous and stable signals.

[0128] Generally speaking, finding accompanying signals is divided into two stages. The first stage is to select the original data according to the frequency band range, time, etc., and conduct the first round of screening of the signals to be accompanied. The second stage uses the signals to be accompanied to find the accompanying signals of each signal to be accompanied.

[0129] The steps of the first phase are as follows:

[0130] Step 1): Use frequency band and time to filter out the original signal;

[0131] Step 2): The original signal after brushing is grouped by obtaining the main signal USID of the original signal;

[0132] Step 3): poll the main signal of each usid and sort them in ascending order according to the start time to carry out the selection process of the accompanying signal;

[0133] Step 4): Selection of accompanying signals, including:

[0134] For example, there are 5 data with the same usid. The main signal of this usid is A. The 5 original signals are A1 (start time t1, end time t1'), A2 (start time t2, end time t2'), A3 (start time t3, end time t3'), A4 (start time t4, end time t4'), A5 (start time t5, end time t5'). The start time of the signal acquisition task of this signal acquisition device is t, and the silent time ty = 30 seconds, then perform the following steps:

[0135] Step 4.1): First, go to the first A1 and use t1-t to determine whether it is greater than the silent time of 30 seconds. If it is greater than 30 seconds, A1 is put into the list to be selected. If it is not greater than the silent time, A1 is discarded and A2 is used to continue the comparison. Use t2-t to determine whether it is greater than the silent time. The processing is the same as A1.

[0136] Step 4.2): For example, if t2-t>ty, which exceeds the silent time, A3 is used for comparison. At this time, t3 of A3-t2' in A2 is used for comparison. If t3-t2' is greater than the silent time, A3 is also put into the candidate list. Next, A4 is compared with A3. If A3 does not meet the requirement, A4 is compared with A2.

[0137] Step 4.3): After all the original signals A1, A2, A3, A4... of the main signal A are compared, a list of to-be-selected signals A2, A5, A6... is formed. Then, all the original signals B1, B2, B3, B4... of the main signal B are found and compared according to the process of A. After the comparison is completed, a list of to-be-selected signals B1, B3, B8... of the signal B is obtained.

[0138] Step 4.4): All main signals form corresponding lists to be selected, and these lists to be selected are stored in the storage as the data combination of the first stage. The subsequent second stage data uses the data in this set.

[0139] The steps of the second phase are as follows:

[0140] Step 1): Use the data set generated in the first stage to remove duplicate USIDs and obtain all the main signals. Then poll the main signals and find the accompanying signals of each main signal. For example, the final signal consists of ABCD E...;

[0141] Step 2): Calculate the companion signal of the first main signal A. First, get the candidate list of A2, A5, A6, etc., find the time interval in each, and then look at the original signals of other main signals in this interval. For example, if there are B1, C2, D3, E2 in the interval A2, then these main signals BCDE are counted 1 time. Then look at the interval A5 of the original signal of the next A signal. For example, if there are B4 and D6, then the four places of signal B become 2, and the number of D is also 2. Repeat this step until all intervals of A are relatively completed. At this time, each main signal BCDE... is marked with the number of counts;

[0142] Step 3): Determine whether the series of main signals in step 2 meet the number condition. For example, if the number condition is 5, determine whether the number of each signal in the list of main signals accompanying signal of signal A meets 5 times. If so, put the satisfying signal into the set of accompanying signals of signal A.

[0143] Step 4): loop step 2) and step 3) to find the accompanying signals of BCDE... until all are found, and then end the accompanying signal process of matching the search signal;

[0144] Step 5): Store each final main signal and its accompanying signal, and finally find all the accompanying signals in a set.

[0145] Example 2

[0146] In one embodiment of the present disclosure, an electromagnetic accompanying signal recognition system based on collision and accompanying analysis is provided, comprising:

[0147] The signal acquisition module is used to acquire different types of electromagnetic raw signal data and perform preprocessing to form standard electromagnetic signals;

[0148] The feature extraction module is used to extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signal into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups;

[0149] The signal analysis module is used to perform collision analysis on signals of different sets of multi-scale feature data to obtain collision sets of the same signals, and then perform concomitant analysis on the signals in the collision set to obtain concomitant signals of all signals;

[0150] Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

[0151] Example 3

[0152] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis is implemented.

[0153] Example 4

[0154] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis is implemented.

[0155] Example 5

[0156] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the electromagnetic accompanying signal recognition method based on collision and accompanying analysis.

[0157] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. An electromagnetic accompanying signal recognition method based on collision and accompanying analysis, characterized in that: include: Acquire different types of electromagnetic raw signal data and pre-process them to form standard electromagnetic signals; Extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signals into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups; Perform signal collision analysis on multi-scale feature data of different sets to obtain collision sets of the same signal, and then perform adjoint analysis on the signals in the collision set to obtain adjoint signals of all signals; Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

2. The electromagnetic accompanying signal recognition method based on collision and accompanying analysis according to claim 1 is characterized in that: The preprocessing includes signal formatting, signal correction and signal deduplication. The signal deduplication includes deduplication by strong matching rules and deduplication by overlapping. The merging step includes: Step 1: Circulate the signal list and compare based on the center frequency offset by n RBWs; Step 2: Loop through the current signal list and compare based on the bandwidth offset by m RBWs; Step 3: If two signals satisfy the condition that the center frequency offset is within n RBWs and the bandwidth offset is within m RBWs, the two signals are considered to be the same signal and are merged and deduplicated; Step 4: Merge and remove duplicate signals from the signal list after strong matching rules have been used.

3. The electromagnetic accompanying signal identification method based on collision and accompanying analysis according to claim 1 is characterized in that: Extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, and match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method, including: Step 1: Load regional radio stations, wireless audio and video frequency bandwidth, and mobile terminal knowledge base information to form a cache; Step 2: According to the classification grouping in the signal classification, obtain the cache knowledge base of different classifications; Step 3: Match the center frequency, bandwidth, and modulation method of the signal according to the center frequency, bandwidth, and modulation method knowledge base content; Step 4: Loop through the knowledge base content under the category and perform matching in step 3; Step 5: If the knowledge base is matched, stop the loop, modify the frequency, bandwidth and modulation mode of the signal, and assign a label to the existing signal; Step 6: If the knowledge base is matched cyclically and no match is found after all the steps are completed, the signal is assigned an unknown signal label; Step 7: Classify the existing signal after matching the knowledge base.

4. The electromagnetic accompanying signal recognition method based on collision and accompanying analysis according to claim 1 is characterized in that: The existing signals are divided into different groups according to the grouping rules, and multi-scale feature data are extracted from the signals of different groups, including: Step 1: Classify and group the electromagnetic signal sensors according to the signal generation; Step 2: Classify and group the signals according to the time interval in which they were generated; Step 3: Classify and group by WIFI signal; Step 4: Classify and group by Bluetooth signal; Step 5: Classify and group according to trunking communication and mobile communication signals; Step 6: After the signals are classified and grouped, the frequency, bandwidth, time interval, signal strength, signal-to-noise ratio, modulation mode characteristics, geographical location characteristics and attribute characteristics of the signals are extracted respectively.

5. The electromagnetic accompanying signal identification method based on collision and accompanying analysis according to claim 1, characterized in that: Signal collision analysis is to find the same signal in two signal sets. If multiple signal sets collide, they should first collide with each other, and then the collision result set will collide with the next result set. After the cycle is completed, a list is formed and the final collision result is saved, including: Step 1: For signal set A and signal set B, A set = {a, b, c, d, e...}, B set = {a', b', c', d', e'...}, solve A∩B; Step 2: Loop through the B set, take the signal a', calculate the frequency range of the signal a' by setting the bandwidth percentage, and then loop through the A set to compare whether the frequency of the signal in the A set is within this range. If it is, it means that it is the same signal as a', and finally form a'={a,c}. If there is more than one element, find the signal in this set whose bandwidth is closer to the bandwidth of the a' signal. If it is c, save a'={c}. If there is only one element, there is no need to compare the bandwidth proximity principle, and directly a'={c}, put c into the final result set, and delete the c signal from the set A; if not found, discard the a' signal, that is, the a' signal did not collide with the signal; Step 3: Loop the second signal of set B, and continue to find the collision signal of the second signal according to step 2, and loop comparison until the loop of set B ends; if set B has not ended and there are no elements in set A, no comparison will be made and the loop will end directly. After the loop ends, a list is formed, a'={c}, d'={d}, e'={f}, ..., and the final collision result is saved.

6. The electromagnetic accompanying signal identification method based on collision and accompanying analysis according to claim 1, characterized in that: The first stage is to screen the accompanying signals according to the frequency band range and time, including: Step 1: Use frequency band and time to filter out the original signal; Step 2: The filtered original signals are grouped by obtaining the main signal USID of the original signals; Step 3: Poll the main signal of each usid and sort them in ascending order according to the start time to carry out the selection process of the accompanying signal.

7. Electromagnetic accompanying signal recognition system based on collision and accompanying analysis, characterized in that: include: The signal acquisition module is used to acquire different types of electromagnetic raw signal data and perform preprocessing to form standard electromagnetic signals; The feature extraction module is used to extract the center frequency, bandwidth and modulation mode of the standard electromagnetic signal, match the center frequency, bandwidth and modulation mode with the knowledge base signal according to the knowledge base matching method to separate the existing signal from the unknown signal; divide the existing signal into different groups according to the grouping rules, and extract multi-scale feature data from the signals of different groups; The signal analysis module is used to perform collision analysis on signals of different sets of multi-scale feature data to obtain collision sets of the same signals, and then perform concomitant analysis on the signals in the collision set to obtain concomitant signals of all signals; Among them, the collision analysis includes: first performing pairwise collisions of sets, colliding the result sets after the collision with other sets, and obtaining the final collision set of the same signal; the companion analysis includes two stages: the first stage screens the accompanying signals according to the frequency band range and time, and the second stage deduplicates the accompanying signals to obtain all the main signals, polls each main signal, and finds the accompanying signals of each main signal.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the electromagnetic accompanying signal recognition method based on collision and accompanying analysis as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the electromagnetic accompanying signal recognition method based on collision and accompanying analysis as described in any one of claims 1 to 6.

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