CN114067614A - Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium - Google Patents
Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium Download PDFInfo
- Publication number
- CN114067614A CN114067614A CN202111169772.4A CN202111169772A CN114067614A CN 114067614 A CN114067614 A CN 114067614A CN 202111169772 A CN202111169772 A CN 202111169772A CN 114067614 A CN114067614 A CN 114067614A
- Authority
- CN
- China
- Prior art keywords
- signal
- response signal
- mode
- response
- pulse
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0004—Transmission of traffic-related information to or from an aircraft
- G08G5/0013—Transmission of traffic-related information to or from an aircraft with a ground station
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0073—Surveillance aids
- G08G5/0082—Surveillance aids for monitoring traffic from a ground station
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
- H04K3/00—Jamming of communication; Counter-measures
- H04K3/80—Jamming or countermeasure characterized by its function
- H04K3/82—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection
- H04K3/822—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection by detecting the presence of a surveillance, interception or detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
- H04K3/00—Jamming of communication; Counter-measures
- H04K3/80—Jamming or countermeasure characterized by its function
- H04K3/86—Jamming or countermeasure characterized by its function related to preventing deceptive jamming or unauthorized interrogation or access, e.g. WLAN access or RFID reading
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Computer Security & Cryptography (AREA)
- Electromagnetism (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for extracting characteristics of an air traffic control monitoring response signal, wherein the method comprises the following steps: acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to obtain a response signal set with an A mode code label and a response signal set with an S mode code label; performing self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label, and taking absolute values to obtain a matrix used as characteristic extraction input; and calculating Fisher discrimination ratios of the matrixes used as characteristic extraction input, and selecting the points with the maximum Fisher discrimination ratios as signal characteristics according to the preset number of the characteristics to be extracted. According to the invention, the collected response signals are subjected to preprocessing and time-frequency conversion, and then the characteristics are extracted, so that each airborne response device forms the signal level characteristics thereof, and a signal level characteristic library of the airborne response device is formed.
Description
Technical Field
The invention belongs to the technical field of radar signals, and particularly relates to a method, a device, equipment and a storage medium for extracting characteristics of an air traffic control monitoring response signal.
Background
The secondary radar system and the ADS-B (Automatic dependent Surveillance-Broadcast) system are the main means for air traffic monitoring. The secondary radar system comprises a ground device and an airborne device, and the working principle of the secondary radar system is that a ground interrogator transmits an interrogation signal through an antenna, an airborne transponder receives the interrogation signal and analyzes the interrogation signal to determine interrogation content, then transmits a response signal according to the interrogation content, and a ground receiver receives the response signal, demodulates and decodes the response signal to determine information such as distance, direction, height and code of an airplane so as to ensure normal operation of the airplane. The ADS-B system takes position data provided by a satellite navigation system as a main information source, a 1090ES (1090MHz Extended subscriber, 1090MHz Extended text) data chain periodically broadcasts information such as position, speed and state of the ADS-B system to the outside, and a ground station and other airplanes ADS-B IN (ADS-B receiving) receive ADS-BOUT (ADS-B transmitting) broadcast messages, so that air-space and air-ground integrated aerial monitoring among airplanes and between the airplanes and the ground station is realized.
In recent years, with the rapid development of the aviation industry and the radio field, the number of aircrafts is increasing, the air electromagnetic environment is becoming more and more complex, and the difficulty of air traffic management is becoming greater and greater. For a secondary radar system, the processing difficulty of a ground receiver on a monitoring response signal is increased due to the problems of asynchronization, reflection, synchronous crosstalk, multipath and the like, and the use efficiency and the air traffic control performance of data are seriously influenced by the problems of decoding errors of the response signal, false targets and the like. For the ADS-B system, due to the openness of its signal definition, spoofing and interference and signal interleaving are major factors affecting its functional performance.
For secondary radar and ADS-B response signals, the currently adopted mode is to demodulate radio frequency signals, then decode baseband signals, and analyze relevant information for air traffic control services. In the face of increasingly complex electromagnetic environments, the drawbacks and disadvantages that are increasingly highlighted by the current approaches include:
(1) the signal data utilization rate is low: when the secondary radar response signal or the ADS-B signal encounters synchronous or asynchronous interleaving interference, the decoding error probability of the secondary radar is increased, so that data invalidation is caused, the probability that the ADS-B response signal cannot pass verification is increased, the response signal is discarded, and the data utilization rate is reduced.
(2) The false targets increase: due to the existence of interference and a deceptive target, a secondary radar signal or an ADS-B signal can be simulated in a forwarding or intelligent generation mode, so that the occurrence of the deceptive target is caused, and the air traffic control service is influenced.
(3) Insufficient data utilization dimensionality: at present, the processing mode of air traffic control monitoring generally does not carry out classified storage and deep analysis and mining on answer signals, so that the dimensionality of data utilization is insufficient.
Based on the above factors, the conventional processing and decoding method for the response signal will be unable to meet the requirement of the air traffic control service more and more, and it is urgently needed to solve the problem encountered in the monitoring service by mining the information of the response signal through a new dimension and a new method.
Disclosure of Invention
The present invention provides a method, an apparatus, a device and a storage medium for extracting characteristics of an empty pipe monitoring response signal, which aims to solve the above problems.
The purpose of the invention is realized by the following technical scheme:
the method for extracting the characteristics of the air traffic control monitoring response signal comprises the following steps:
acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to obtain a response signal set with an A mode code label and a response signal set with an S mode code label;
performing self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label, and taking absolute values to obtain a matrix used as characteristic extraction input;
and calculating Fisher discrimination ratios of the matrixes used as characteristic extraction input, and selecting the points with the maximum Fisher discrimination ratios as signal characteristics according to the preset number of the characteristics to be extracted.
Further, the signal preprocessing of the a/C mode response signal includes the steps of:
executing response decoding on the obtained A/C mode response signal, sampling a target zero intermediate frequency IQ decoded by the response, obtaining target zero intermediate frequency IQ sampling data, and performing classified storage by taking an A mode code as a label;
performing intermediate frequency IQ sampling on the obtained A/C mode response signal, and intercepting the signal by taking the leading edge of a first frame pulse of the response signal every time as a starting point and the trailing edge of a second frame pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each frame pulse;
dividing all sampling points of each frame pulse from the front edge to the back edge by the average pulse amplitude of the frame pulse to serve as the new amplitude of each sampling point;
and carrying out noise filling on sampling data from the next sampling point of the back edge of the first frame pulse to the previous sampling point of the front edge of the second frame pulse of each response signal.
Further, the signal preprocessing of the S-mode response signal includes the following steps:
executing response decoding on the obtained S-mode response signal, sampling a target zero intermediate frequency IQ of the response decoding, obtaining target zero intermediate frequency IQ sampling data, and performing classified storage by taking an S-mode code as a label;
performing intermediate frequency IQ sampling on the acquired S-mode response signal, and intercepting the signal by taking the leading edge of the first leading pulse of each response signal as a starting point and the trailing edge of the last leading pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each leading pulse;
dividing the average pulse amplitude of each leading pulse by all sampling points of each leading pulse from the leading edge to the trailing edge respectively to serve as the new amplitude of each sampling point;
and carrying out noise filling on the sampled data between the next sampling point at the back edge of the first leading pulse of each response signal and the previous sampling point at the front edge of the next leading pulse.
Further, the noise filling adopts analog white noise, and the noise amplitude level is 1/5 to 1/20 of the pulse amplitude.
Further, the noise amplitude level is 1/10 of the pulse amplitude.
Further, the method further comprises establishing a signal feature library according to the extracted signal features.
In another aspect, the present invention further provides an empty pipe monitoring response signal feature extraction device, where the device includes:
the signal preprocessing module is used for acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to acquire a response signal set with an A mode code label and a response signal set with an S mode code label;
the time-frequency transformation module is used for carrying out self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label and taking absolute values to obtain a matrix used as characteristic extraction input;
and the characteristic extraction module is used for calculating the Fisher discrimination ratio of the matrix used as the characteristic extraction input, and selecting the point with the maximum Fisher discrimination ratio as the signal characteristic according to the preset quantity of the characteristics to be extracted.
Optionally, the signal preprocessing module executes response decoding on the acquired a/C mode response signal, samples a target zero intermediate frequency IQ of the response decoding, acquires target zero intermediate frequency IQ sample data, and performs classified storage by using an a mode code as a tag;
performing intermediate frequency IQ sampling on the obtained A/C mode response signal, and intercepting the signal by taking the leading edge of a first frame pulse of the response signal every time as a starting point and the trailing edge of a second frame pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each frame pulse;
dividing all sampling points of each frame pulse from the front edge to the back edge by the average pulse amplitude of the frame pulse to serve as the new amplitude of each sampling point;
and carrying out noise filling on sampling data from the next sampling point of the back edge of the first frame pulse to the previous sampling point of the front edge of the second frame pulse of each response signal.
Optionally, the signal preprocessing module samples the intermediate frequency IQ of the acquired S-mode reply signal, and intercepts the signal with a leading edge of a first leading pulse of each reply signal as a start point and a trailing edge of a last leading pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each leading pulse;
dividing the average pulse amplitude of each leading pulse by all sampling points of each leading pulse from the leading edge to the trailing edge respectively to serve as the new amplitude of each sampling point;
and carrying out noise filling on the sampled data between the next sampling point at the back edge of the first leading pulse of each response signal and the previous sampling point at the front edge of the next leading pulse.
Optionally, the noise filling of the signal preprocessing module is analog white noise, and the noise amplitude level is 1/5 to 1/20 of the pulse amplitude.
Optionally, the noise amplitude level employed by the signal pre-processing module is 1/10 of the pulse amplitude.
Further, the device also comprises a signal feature library establishing module for establishing a signal feature library according to the extracted signal features.
In another aspect, the present invention further provides a computer device, where the computer device includes a processor and a memory, where the memory stores a computer program, and the computer program is loaded by the processor and executed to implement any one of the above-mentioned methods for extracting characteristics of an empty pipe monitoring reply signal.
In another aspect, the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program is loaded and executed by a processor to implement any one of the above-mentioned methods for extracting characteristics of an empty management monitoring answer signal.
The invention has the beneficial effects that:
(1) according to the invention, the collected response signals are preprocessed and stored in a classified manner, then time-frequency conversion is carried out to obtain an input matrix for feature extraction, and finally feature extraction is carried out to enable each airborne response device to form own signal level features.
(2) According to the invention, the deep-level signal level characteristics of the response signals are mined from the other dimension, and the response signals which cannot pass the verification due to interference can be in one-to-one correspondence with the airborne response equipment through signal level characteristic matching, so that the air traffic control monitoring service is assisted, and the data utilization rate is improved.
(3) According to the method, after a signal feature library is established, the deep-level signal level features of the excavated response signals correspond to the airborne response equipment one by one through feature matching unknown response signals (real targets or interference deception signals), so that false deception targets can be identified, and the security index is improved.
(4) The invention stores the response signals in a classified manner so as to facilitate post analysis, provides effective data support for air traffic situation analysis, air traffic big data service and the like, and improves the utilization dimension of the response signal data.
Drawings
Fig. 1 is a schematic flow chart of a method for extracting characteristics of an empty pipe monitoring response signal according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a flow of preprocessing an a/C mode reply signal of the method for extracting characteristics of an empty pipe monitoring reply signal according to embodiment 1 of the present invention;
fig. 3 is a schematic diagram of an a/C mode reply signal format in the method for extracting characteristics of an empty pipe monitoring reply signal according to embodiment 1 of the present invention;
fig. 4 is a schematic diagram of a preprocessing flow of an S-mode response signal of an empty pipe monitoring response signal feature extraction method according to embodiment 1 of the present invention;
fig. 5 is a schematic diagram of an S-mode reply signal format in the method for extracting characteristics of an empty pipe monitoring reply signal according to embodiment 1 of the present invention;
fig. 6 is a schematic flow chart of a matching method for an unknown empty pipe monitoring response signal according to embodiment 2 of the present invention;
fig. 7 is a block diagram of a configuration of an empty pipe monitoring response signal feature extraction device according to embodiment 3 of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
For secondary radar and ADS-B response signals, the currently adopted mode is to demodulate radio frequency signals, then decode baseband signals, and analyze relevant information for air traffic control services. In the face of increasingly complex electromagnetic environments, the drawbacks and disadvantages that are increasingly highlighted by the current approaches include: the signal data utilization rate is low: when the secondary radar response signal or the ADS-B signal encounters synchronous or asynchronous interleaving interference, the decoding error probability of the secondary radar is increased, so that data invalidation is caused, the probability that the ADS-B response signal cannot pass verification is increased, the response signal is discarded, and the data utilization rate is reduced. The false targets increase: due to the existence of interference and a deceptive target, a secondary radar signal or an ADS-B signal can be simulated in a forwarding or intelligent generation mode, so that the occurrence of the deceptive target is caused, and the air traffic control service is influenced. Insufficient data utilization dimensionality: at present, the processing mode of air traffic control monitoring generally does not carry out classified storage and deep analysis and mining on answer signals, so that the dimensionality of data utilization is insufficient.
In order to solve the technical problem, various embodiments of the method for extracting the characteristics of the empty pipe monitoring response signal are provided.
Referring to fig. 1, as shown in fig. 1, a schematic flow chart of a method for extracting characteristics of an empty pipe monitoring response signal according to this embodiment is shown. The method specifically comprises the following steps:
step S100: and acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to obtain a response signal set with an A mode code tag and a response signal set with an S mode code tag.
Specifically, the a/C mode reply signal format is as shown in fig. 3, the reply code is composed of 16 information code bits, the code of these code bits is F1, C1, a1, C2, a2, C4, a4, X, B1, D1, B2, D2, B4, D4, F2 and SPI in this order, each code bit has two states, i.e., pulse or no pulse, pulse is "1" state, and pulse is "0" state. F1 and F2 are called frame pulses, are mark pulses of A/C mode echo, are constantly in a '1' state, X bits are standby bits and are constantly in 0, and other code bits set a response state according to corresponding situations. Based on the above analysis, considering that F1 and F2 are always in "1" state, the present embodiment uses the F1 and F2 pulses of the preprocessed reply signal as the input of feature extraction, and the signal preprocessing process is as shown in fig. 2, and specifically includes the following steps:
step S101: and executing response decoding on the obtained A/C mode response signal, sampling the target zero intermediate frequency IQ subjected to response decoding, obtaining target zero intermediate frequency IQ sampling data, and performing classified storage by taking an A mode code as a label. The sampling rate of this embodiment is 20 MHz. Steps S102-S105 are then repeatedly performed.
Step S102: and (3) performing intermediate frequency IQ sampling on the acquired A/C mode response signal, and intercepting the signal by taking the leading edge of a first frame pulse of the response signal every time as a starting point and the trailing edge of a second frame pulse as an end point as input of signal preprocessing.
Step S103: and calculating the average pulse amplitude Amp of all the sampling points from the next sampling point of the front edge of each frame pulse to the previous sampling point of the back edge.
Step S104: and dividing all sampling points of each frame pulse from the front edge to the back edge by the average pulse amplitude Amp of the frame pulse as the new amplitude of each sampling point, namely inputting the time frequency conversion, wherein the average pulse amplitude is Amp/Amp and is 1.
Step S105: and carrying out noise filling on sampling data from the next sampling point of the back edge of the first frame pulse to the previous sampling point of the front edge of the second frame pulse of each response signal. And performing filling replacement by using simulated white noise, wherein the noise amplitude level is 1/5-1/20 of the pulse amplitude, and the preprocessed signal is used as the input of time-frequency transformation and feature extraction. The noise amplitude level is 1/10 of the pulse amplitude, the effect is optimal, overfitting can be prevented, and signal features can be effectively extracted.
The S-mode reply signal (including ADS-B signal) format is shown in fig. 5, where the whole signal is divided into 8us header signal and 112us (or 56us) data field, the header contains 4 leading pulses, the 4 pulses are respectively located at 0us, 1us, 3.5us and 4.5us, the 8us data bit is started, the data bit is 112us (or 56 bits), there are 112 (or 56) bits, each bit contains two symbols, if the pulse is located at the leading bit symbol, the symbol represents bit 1, and if the pulse is located at the trailing bit symbol, the bit is represented. Since 4 leading pulses of the S-mode reply signal are formatted, they are preprocessed and used as input for feature extraction, and the preprocessing process is as shown in fig. 4, and specifically includes the following steps:
step S111: and executing response decoding on the acquired S-mode response signal, sampling the target zero intermediate frequency IQ subjected to response decoding, acquiring target zero intermediate frequency IQ sampling data, and performing classified storage by taking an S-mode code as a label. The sampling rate of this embodiment is 10 MHz. Steps S112-S115 are then repeatedly performed.
Step S112: and performing intermediate frequency IQ sampling on the acquired S-mode response signal, and intercepting the signal by taking the leading edge of the first leading pulse of each response signal as a starting point and the trailing edge of the last leading pulse as an end point as input of signal preprocessing.
Step S113: and calculating the average pulse amplitude Amp of all the sampling points from the next sampling point at the front edge of each leading pulse to the previous sampling point at the back edge.
Step S114: and dividing all sampling points of each leading pulse from the leading edge to the trailing edge by the average pulse amplitude Amp of the leading pulse as the new amplitude of each sampling point, namely inputting the time frequency conversion, wherein the average pulse amplitude is Amp/Amp and is 1.
Step S115: and carrying out noise filling on the sampled data between the next sampling point at the back edge of the first leading pulse (not containing the last leading pulse) of each response signal and the previous sampling point at the front edge of the next leading pulse. And performing filling replacement by using simulated white noise, wherein the noise amplitude level is 1/5-1/20 of the pulse amplitude, and the preprocessed signal is used as the input of time-frequency transformation and feature extraction. The noise amplitude level is 1/10 of the pulse amplitude, the effect is optimal, overfitting can be prevented, and signal features can be effectively extracted.
Step S200: and (3) performing self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label, and taking absolute values to obtain a matrix used as characteristic extraction input.
Besides time, frequency is an important expression of signal characteristics, and a frequency representation method is based on Fourier analysis. The time-frequency analysis method provides the joint distribution information of the time domain and the frequency domain, and clearly describes the relation of the signal frequency changing along with the time. The basic idea of time-frequency analysis is to design a joint function of time and frequency, which is used to describe the energy density or strength of a signal at different times and frequencies simultaneously. This joint function of time and frequency is referred to as the time-frequency distribution. In this embodiment, the wigner time-frequency distribution is used for time-frequency analysis.
Assuming that the fourier transforms of the signals X (t), Y (t) are X (j Ω), Y (j Ω), respectively, then the joint wigner distribution of X (t), Y (t) is defined as:
the self wigner distribution of signal x (t) is defined as:
for the preprocessed A/C mode response signals and S mode response signals, all signals in the response signal set are subjected to self-Wegener distribution, absolute values are obtained, and the converted data are still classified according to the original labels and serve as input of feature extraction. And (4) performing self-Wegener distribution on all signals in the response signal set, and obtaining an absolute value to obtain a matrix used as characteristic extraction input.
Step S300: and calculating Fisher discrimination ratios of the matrixes used as characteristic extraction input, and selecting the points with the maximum Fisher discrimination ratios as signal characteristics according to the preset number of the characteristics to be extracted.
During feature extraction, Fisher discriminant is generally used for selecting a feature subset with superior classification performance from original features in a pattern recognition problem. The Fisher discriminant ratio is represented by the ratio of the inter-class variance and the intra-class variance of the signal, and is a very common feature extraction method. The inter-class variance represents the degree of inter-class separation between different classes of signal samples, and the intra-class variance represents the degree of intra-class aggregation of the same class of signal samples. The Fisher discriminant ratio reaches the maximum when the variance between classes is maximum and the variance within the classes is minimum, namely the classification performance is the best.
The Fisher discriminant ratio is generally expressed as:
wherein the content of the first and second substances,
is the average, σ, of the time-frequency transformation of the H training samples of the class c signal at point (η, τ)(c)[η,τ]Is the standard deviation of the time-frequency transform at the point (η, τ) of the H training samples of the class c signal. The points with larger Fisher discrimination can be used as the characteristics of the signals for identification.
After the Fisher discrimination ratio is calculated, if the number N of the features to be extracted is set, taking N points with the maximum Fisher discrimination ratio as signal features. The signal characteristics are used for identifying the answering equipment, and when an unknown answering signal (a real target or an interference deception signal) or an answering signal which cannot pass verification due to interference arrives, the unknown answering signal can be intercepted, preprocessed and subjected to time-frequency transformation, and then the answering signal and the airborne answering equipment are in one-to-one correspondence through signal level characteristic matching. The signal characteristics may be stored by building a database of signal characteristics.
According to the method for extracting the characteristics of the air traffic control monitoring response signals, the collected response signals are preprocessed and stored in a classified mode, then time-frequency conversion is carried out to obtain an input matrix for characteristic extraction, and finally characteristic extraction is carried out to enable each airborne response device to form signal level characteristics of the airborne response device. In the embodiment, the deep-level signal level characteristics of the response signals are mined from the other dimension, the response signals which cannot pass the verification due to interference can be in one-to-one correspondence with the airborne response equipment through signal level characteristic matching, the air traffic control monitoring service is assisted, and the data utilization rate is improved. The response signals are classified and stored so as to facilitate post analysis, provide effective data support for air traffic situation analysis, air traffic big data service and the like, and improve the utilization dimensionality of the response signal data.
Example 2
Referring to fig. 6, as shown in fig. 6, the method for matching an unknown empty management monitoring answer signal provided in this embodiment is to establish a feature database by using the method for extracting features of an empty management monitoring answer signal provided in the foregoing embodiment.
The method receives unknown target signals through a communication receiver and carries out preprocessing, the preprocessing method is the same as the method for extracting the characteristics of the air traffic control monitoring response signals provided by the embodiment, the same time-frequency transformation method and the same characteristic extraction method are adopted to extract the deep signal level characteristics of the unknown target signals, and the characteristics are matched with the characteristic signals in the characteristic database through a design classifier, so that the identification of response equipment is completed.
According to the matching method of the unknown air traffic control monitoring response signal, after the deep signal level characteristics of the response signal are mined and the signal characteristic library is established through the air traffic control monitoring response signal characteristic extraction method provided by the embodiment, the unknown response signal (the real target or the interference deception signal) is matched with the airborne response equipment in a one-to-one correspondence mode through characteristic matching, the false deception target can be identified, and the security index is improved.
Example 3
Referring to fig. 6, as shown in fig. 6, a block diagram of a structure of an empty pipe monitoring response signal feature extraction apparatus according to this embodiment is provided, where the apparatus includes:
the signal preprocessing module is used for acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to acquire a response signal set with an A mode code label and a response signal set with an S mode code label;
the time-frequency transformation module is used for carrying out self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label and taking absolute values to obtain a matrix used as characteristic extraction input;
and the characteristic extraction module is used for calculating the Fisher discrimination ratio of the matrix used as the characteristic extraction input, and selecting the point with the maximum Fisher discrimination ratio as the signal characteristic according to the preset quantity of the characteristics to be extracted.
And the signal characteristic library establishing module is used for establishing a signal characteristic library according to the extracted signal characteristics.
As a specific implementation manner, the signal preprocessing module executes response decoding on the acquired a/C mode response signal, samples a target zero intermediate frequency IQ of the response decoding, acquires target zero intermediate frequency IQ sample data, and performs classified storage by using an a mode code as a tag;
performing intermediate frequency IQ sampling on the obtained A/C mode response signal, and intercepting the signal by taking the leading edge of a first frame pulse of the response signal every time as a starting point and the trailing edge of a second frame pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each frame pulse;
dividing all sampling points of each frame pulse from the front edge to the back edge by the average pulse amplitude of the frame pulse to serve as the new amplitude of each sampling point;
and carrying out noise filling on sampling data from the next sampling point of the back edge of the first frame pulse to the previous sampling point of the front edge of the second frame pulse of each response signal.
As a specific implementation manner, the signal preprocessing module samples the intermediate frequency IQ of the acquired S-mode reply signal, and intercepts the signal with the leading edge of the first leading pulse of each reply signal as the start and the trailing edge of the last leading pulse as the end;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each leading pulse;
dividing the average pulse amplitude of each leading pulse by all sampling points of each leading pulse from the leading edge to the trailing edge respectively to serve as the new amplitude of each sampling point;
and carrying out noise filling on the sampled data between the next sampling point at the back edge of the first leading pulse of each response signal and the previous sampling point at the front edge of the next leading pulse.
In one embodiment, the noise filling of the signal preprocessing module is analog white noise, and the noise amplitude level is 1/5 to 1/20 of the pulse amplitude.
In one embodiment, the noise amplitude level used by the signal preprocessing module is 1/10 pulses.
The empty pipe monitoring answer signal feature extraction device provided by this embodiment preprocesses and stores the collected answer signals in a classified manner, then performs time-frequency conversion to obtain an input matrix for feature extraction, and finally performs feature extraction to enable each airborne answer device to form its own signal level features. In the embodiment, the deep-level signal level characteristics of the response signals are mined from the other dimension, the response signals which cannot pass the verification due to interference can be in one-to-one correspondence with the airborne response equipment through signal level characteristic matching, the air traffic control monitoring service is assisted, and the data utilization rate is improved. The response signals are classified and stored so as to be convenient for post analysis, effective data support is provided for air traffic situation analysis, air traffic big data service and the like, and utilization dimensionality of the response signal data is improved
Example 4
The preferred embodiment provides a computer device, which can implement the steps in any embodiment of the method for extracting characteristics of an empty pipe monitoring response signal provided in the embodiment of the present application, and therefore, the beneficial effects of the method for extracting characteristics of an empty pipe monitoring response signal provided in the embodiment of the present application can be achieved, for details, see the foregoing embodiment, and are not described herein again.
Example 5
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor. To this end, the present invention provides a storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps of any embodiment of the method for extracting characteristics of an empty pipe monitoring response signal provided by the present invention.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium may execute the steps in any of the embodiments of the method for extracting characteristics of empty pipe monitoring response signals provided in the embodiments of the present invention, beneficial effects that can be achieved by any of the methods for extracting characteristics of empty pipe monitoring response signals provided in the embodiments of the present invention may be achieved, for details, see the foregoing embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The method for extracting the characteristics of the response signal of the air traffic control monitoring is characterized by comprising the following steps of:
acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to obtain a response signal set with an A mode code label and a response signal set with an S mode code label;
performing self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label, and taking absolute values to obtain a matrix used as characteristic extraction input;
and calculating Fisher discrimination ratios of the matrixes used as characteristic extraction input, and selecting the points with the maximum Fisher discrimination ratios as signal characteristics according to the preset number of the characteristics to be extracted.
2. The empty pipe monitoring answer signal feature extraction method of claim 1, wherein the signal preprocessing of the a/C mode answer signal comprises the steps of:
executing response decoding on the obtained A/C mode response signal, sampling a target zero intermediate frequency IQ decoded by the response, obtaining target zero intermediate frequency IQ sampling data, and performing classified storage by taking an A mode code as a label;
performing intermediate frequency IQ sampling on the obtained A/C mode response signal, and intercepting the signal by taking the leading edge of a first frame pulse of the response signal every time as a starting point and the trailing edge of a second frame pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each frame pulse;
dividing all sampling points of each frame pulse from the front edge to the back edge by the average pulse amplitude of the frame pulse to serve as the new amplitude of each sampling point;
and carrying out noise filling on sampling data from the next sampling point of the back edge of the first frame pulse to the previous sampling point of the front edge of the second frame pulse of each response signal.
3. The empty pipe monitoring answer signal feature extraction method of claim 1, wherein the signal preprocessing of the S-mode answer signal comprises the steps of:
executing response decoding on the obtained S-mode response signal, sampling a target zero intermediate frequency IQ of the response decoding, obtaining target zero intermediate frequency IQ sampling data, and performing classified storage by taking an S-mode code as a label;
performing intermediate frequency IQ sampling on the acquired S-mode response signal, and intercepting the signal by taking the leading edge of the first leading pulse of each response signal as a starting point and the trailing edge of the last leading pulse as an end point;
calculating the average pulse amplitude of all sampling points from the rear sampling point to the front sampling point of the rear edge of each leading pulse;
dividing the average pulse amplitude of each leading pulse by all sampling points of each leading pulse from the leading edge to the trailing edge respectively to serve as the new amplitude of each sampling point;
and carrying out noise filling on the sampled data between the next sampling point at the back edge of the first leading pulse of each response signal and the previous sampling point at the front edge of the next leading pulse.
4. An empty pipe monitoring answer signal feature extraction method as claimed in any one of claims 2 or 3, wherein the noise filling is of analogue white noise, and the noise amplitude level is 1/5 to 1/20 of the pulse amplitude.
5. The empty pipe monitoring answer signal feature extraction method of claim 4, wherein the noise amplitude level is 1/10 of pulse amplitude.
6. The empty pipe monitoring answer signal feature extraction method of claim 1, further comprising building a signal feature library from the extracted signal features.
7. An empty pipe monitoring response signal feature extraction device, characterized by comprising:
the signal preprocessing module is used for acquiring an A/C mode response signal and an S mode response signal, and performing signal preprocessing on the A/C mode response signal and the S mode response signal to acquire a response signal set with an A mode code label and a response signal set with an S mode code label;
the time-frequency transformation module is used for carrying out self-Wegener distribution on all signals in the response signal set with the A-mode code label and the response signal set with the S-mode code label and taking absolute values to obtain a matrix used as characteristic extraction input;
and the characteristic extraction module is used for calculating the Fisher discrimination ratio of the matrix used as the characteristic extraction input, and selecting the point with the maximum Fisher discrimination ratio as the signal characteristic according to the preset quantity of the characteristics to be extracted.
8. The empty pipe monitoring reply signal feature extraction device according to claim 7, wherein the device further comprises a signal feature library creation module for creating a signal feature library based on the extracted signal features.
9. A computer device, characterized in that the computer device comprises a processor and a memory, wherein the memory stores a computer program which is loaded and executed by the processor to implement the empty pipe monitoring answer signal feature extraction method according to any one of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored, which is loaded and executed by a processor to implement the empty pipe monitoring response signal feature extraction method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111169772.4A CN114067614B (en) | 2021-10-08 | 2021-10-08 | Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111169772.4A CN114067614B (en) | 2021-10-08 | 2021-10-08 | Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114067614A true CN114067614A (en) | 2022-02-18 |
| CN114067614B CN114067614B (en) | 2022-08-16 |
Family
ID=80234196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111169772.4A Active CN114067614B (en) | 2021-10-08 | 2021-10-08 | Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114067614B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115097411A (en) * | 2022-08-24 | 2022-09-23 | 四川九洲空管科技有限责任公司 | Method for extracting response code bit amplitude based on adaptive sampling secondary radar |
| CN115456033A (en) * | 2022-10-26 | 2022-12-09 | 四川九洲空管科技有限责任公司 | Recognition system enhanced recognition method based on signal feature recognition |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110128180A1 (en) * | 2007-09-03 | 2011-06-02 | Selex Sistemi Integrati S.P.A. | Reply detection in a secondary surveillance radar |
| CN104253721A (en) * | 2014-09-23 | 2014-12-31 | 北京航空航天大学 | Data bit extracting method of mode S answering signal in ADS-B system |
| CN104993908A (en) * | 2015-06-12 | 2015-10-21 | 陕西宝成航空仪表有限责任公司 | Method of increasing S mode response signal decoding reliability |
| CN107831483A (en) * | 2017-11-24 | 2018-03-23 | 中国电子科技集团公司第四十研究所 | A kind of reception device and coding/decoding method of aviation secondary radar signals |
| CN107942297A (en) * | 2017-10-26 | 2018-04-20 | 四川大学 | A kind of A/C patterns answer signal processing method |
| CN110646766A (en) * | 2019-09-23 | 2020-01-03 | 四川九洲电器集团有限责任公司 | S-mode interrogation signal detection method, storage medium and detection device |
-
2021
- 2021-10-08 CN CN202111169772.4A patent/CN114067614B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110128180A1 (en) * | 2007-09-03 | 2011-06-02 | Selex Sistemi Integrati S.P.A. | Reply detection in a secondary surveillance radar |
| CN104253721A (en) * | 2014-09-23 | 2014-12-31 | 北京航空航天大学 | Data bit extracting method of mode S answering signal in ADS-B system |
| CN104993908A (en) * | 2015-06-12 | 2015-10-21 | 陕西宝成航空仪表有限责任公司 | Method of increasing S mode response signal decoding reliability |
| CN107942297A (en) * | 2017-10-26 | 2018-04-20 | 四川大学 | A kind of A/C patterns answer signal processing method |
| CN107831483A (en) * | 2017-11-24 | 2018-03-23 | 中国电子科技集团公司第四十研究所 | A kind of reception device and coding/decoding method of aviation secondary radar signals |
| CN110646766A (en) * | 2019-09-23 | 2020-01-03 | 四川九洲电器集团有限责任公司 | S-mode interrogation signal detection method, storage medium and detection device |
Non-Patent Citations (6)
| Title |
|---|
| 吴秀明等: "S模式应答机中频数字信号处理方法研究", 《现代导航》 * |
| 吴鑫炜等: "复杂环境下的A/C模式信号处理算法及其实现", 《航空电子技术》 * |
| 张建峰等: "一种基于相关的ADS-B前导脉冲检测方法", 《通信对抗》 * |
| 李富森: "综合化防撞系统数字接收机关键技术研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
| 程旗等: "一种基于决策树的信号分类方法", 《电子世界》 * |
| 赵浩然等: "信号源个体识别方法简介", 《电子世界》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115097411A (en) * | 2022-08-24 | 2022-09-23 | 四川九洲空管科技有限责任公司 | Method for extracting response code bit amplitude based on adaptive sampling secondary radar |
| CN115097411B (en) * | 2022-08-24 | 2022-11-15 | 四川九洲空管科技有限责任公司 | Method for extracting amplitude of response code bit based on adaptive sampling secondary radar |
| CN115456033A (en) * | 2022-10-26 | 2022-12-09 | 四川九洲空管科技有限责任公司 | Recognition system enhanced recognition method based on signal feature recognition |
| CN115456033B (en) * | 2022-10-26 | 2023-02-03 | 四川九洲空管科技有限责任公司 | Recognition system enhanced recognition method based on signal feature recognition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114067614B (en) | 2022-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114067614B (en) | Method, device and equipment for extracting characteristics of air traffic control monitoring response signal and storage medium | |
| CN108226921B (en) | Secondary radar information processing system based on CPU and GPU architecture | |
| CN109886116A (en) | Specific emitter identification method and device based on ADS-B | |
| CN102279390B (en) | Intra-pulse modulation and recognition method of low signal-to-noise radar radiation source signal | |
| Zhao et al. | Classification of small UAVs based on auxiliary classifier Wasserstein GANs | |
| CN106330385A (en) | Interference type identification method | |
| CN105528595A (en) | Method for identifying and positioning power transmission line insulators in unmanned aerial vehicle aerial images | |
| CN112818891B (en) | Intelligent identification method for communication interference signal type | |
| CN114295137B (en) | Takang signal identification method and system based on FPGA | |
| Liu | Multi-feature fusion for specific emitter identification via deep ensemble learning | |
| CN111461037B (en) | End-to-end gesture recognition method based on FMCW radar | |
| Zha et al. | Real-world ADS-B signal recognition based on radio frequency fingerprinting | |
| CN106027201A (en) | Correlation-based satellite-borne ADS-B header detection method | |
| CN112528774B (en) | Intelligent unknown radar signal sorting system and method in complex electromagnetic environment | |
| CN108197581A (en) | A kind of unmanned plane signal identification detection algorithm based on improvement AC-WGANs | |
| CN106100786A (en) | A kind of spaceborne ADS B detection of preamble method relevant based on header entirety | |
| CN114978231B (en) | Single query rapid identification method and system based on information redundancy | |
| CN105550702A (en) | GNSS deception jamming recognition method based on SVM | |
| CN109948731A (en) | A kind of communication station individual discrimination method, system, storage medium and terminal | |
| CN109061632A (en) | A kind of unmanned plane recognition methods | |
| CN104253721A (en) | Data bit extracting method of mode S answering signal in ADS-B system | |
| CN109995450B (en) | Cloud technology based voice recognition and intelligent black broadcast identification method | |
| Zhang et al. | Radar emitter signal recognition based on support vector machines | |
| CN111682958B (en) | Environmental backscattering signal detection method based on cluster analysis | |
| CN112187314A (en) | Signal transmitter identification method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |