CN116112038A - Frequency hopping signal network table sorting method and system based on image processing - Google Patents
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Abstract
The invention relates to the technical field of signal processing, and discloses a frequency hopping signal network table sorting method and a system based on image processing, wherein the method comprises the following steps: s1, data generation and acquisition: generating and collecting frequency hopping signal data of the multi-network station, and representing two network stations of the frequency hopping signal of the multi-network station in a time-frequency waterfall diagram; s2, learning: learning data of a specific network station by using an SSD image processing detection frame; s3, identifying: and recognizing the coordinates and the frequency band of the frequency hopping network station to be sorted by using the SSD detection frame after learning. The invention solves the problems of low sorting success rate and the like in the prior art when estimating the parameters of the frequency hopping signals of the multi-network station.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a frequency hopping signal network table sorting method and system based on image processing.
Background
With the development of the frequency hopping communication technology, a plurality of frequency hopping network stations are often encountered in the actual process of interfering the frequency hopping communication, the anti-interference capability of the multi-network stations is very strong, any jammer cannot directly interfere the frequency hopping signals of the multi-network stations, the multi-network stations are required to be sorted, specific network stations needing to be interfered are sorted out to conduct targeted interference, the interference success rate of the frequency hopping signals of the multi-network stations can be greatly improved, and therefore the importance of the network station sorting in the process of interfering the frequency hopping signals can be seen.
In recent years, intelligent sensing signal research panelists of the internet of things and an intelligent sensing laboratory (Internet of things and intelligent sensor laboratory, IOTS Lab) have made certain progress in the field of frequency hopping signal processing, and when the panelists estimate frequency hopping parameters of frequency hopping signals through a time-frequency ridge line diagram method under a distributed MWC (Modulated Wideband Converter) sampling and recovery framework, the frequency hopping parameters such as frequency hopping speed, frequency hopping period and the like are frequency hopping parameters of the most mainstream frequency hopping signal parameter estimation method at present. However, this mainstream method is not suitable for parameter estimation of the frequency hopping signal of the multi-network station.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for sorting a frequency hopping signal network station based on image processing, which solve the problems of low sorting success rate and the like in the prior art when estimating parameters of frequency hopping signals of a plurality of network stations.
The invention solves the problems by adopting the following technical scheme:
a frequency hopping signal network table sorting method based on image processing comprises the following steps:
s1, data generation and acquisition: generating and collecting frequency hopping signal data of the multi-network station, and representing two network stations of the frequency hopping signal of the multi-network station in a time-frequency waterfall diagram;
s2, learning: learning data of a specific network station by using an SSD image processing detection frame;
s3, identifying: and recognizing the coordinates and the frequency band of the frequency hopping network station to be sorted by using the SSD detection frame after learning.
As a preferred technical solution, in step S1, the frequency hopping signal sent by the transmitter is:
wherein h is 1 (t) is a frequency hopping signal sent by a transmitter, u (t) is a digital information stream to be transmitted,
frequency hopping signal, ω, representing a digital information stream of amplitude 1, without the addition of a digital information stream to be transmitted 0 For transmitting an initial operating frequency point for hopping a frequency hopping signal, m is the number of hopping points, m=0, 1,2,..n-1, N is the number of total hopping points, t is time, ω s The spacing of the individual hopping frequencies synthesized for the frequency synthesizer of the transmitter,/->
Is the initial phase.
As a preferred technical solution, in step S1, the signal received by the frequency hopping signal receiver is:
wherein h is r (t) is the signal received by the receiver, n is the total number of the frequency hopping network stations, j is the number of the current network station, and h j (t) is a frequency hopping signal corresponding to the jth network station, g (t) is noise added to a channel, and i (t) is artificial applicationAnd adding the interference signal.
As a preferred technical solution, in step S1, if the transmitting end and the receiving end of the frequency hopping system have completed synchronization, then:
wherein omega r Representing the center frequency, ω, of the local frequency synthesizer i Represents the intermediate frequency, omega t ＝ω r -ω 0 
Initial phase of the receiver local signal, +.>
As a preferred technical solution, in step S1, when T e [ nT, (n+1) T ], each jump of the frequency hopping signal causes the mixer to output a specific intermediate frequency, and when the intermediate frequency filtering is performed, the following signal components are obtained:
wherein h is 12 (t) represents the useful signal component obtained by intermediate frequency filtering, and h is 12 And (t) is fed into a demodulator to demodulate the transmitted digital information stream u (t).
As a preferable technical solution, in step S1, one frequency hopping network station of one frequency hopping period and the other frequency hopping network station of two frequency hopping periods are both represented in one time-frequency waterfall diagram, and the set of time-frequency waterfall diagrams is a target time-frequency waterfall diagram for being detected.
As a preferable technical solution, in step S1, the frequency hopping network stations with one time of frequency hopping period are simultaneously represented in a time-frequency waterfall chart.
In step S1, the signal modulation scheme is QAM, BPSK, or QPSK.
In step S3, the detected normalized coordinates are multiplied by the sampling frequency to obtain the center frequency and the instantaneous bandwidth of the hopping signal hopping, and the selected specific network station needing interference can be determined according to the two parameters and the coordinates.
The frequency hopping signal network table sorting system based on the image processing is used for realizing the frequency hopping signal network table sorting method based on the image processing, and comprises the following modules connected in sequence:
the data generation and acquisition module: the method comprises the steps of generating and collecting frequency hopping signal data of a plurality of network stations, and representing two network stations of the frequency hopping signal of the plurality of network stations in a time-frequency waterfall diagram;
and a learning module: the SSD image processing detection framework is used for learning data of a specific network station;
and an identification module: the method is used for identifying the coordinates and the frequency bands of the frequency hopping network stations to be sorted by utilizing the SSD detection frame after learning.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for sorting a plurality of frequency hopping network stations by utilizing an image processing method aiming at the problem that the frequency hopping signal network stations based on the multi-network station are difficult to sort, and the result shows that the image processing method can greatly improve the sorting success rate of the frequency hopping signal network stations of the multi-network station.
Drawings
Fig. 1 is a schematic diagram of steps of a method for sorting a frequency hopping signal network table based on image processing according to the present invention;
FIG. 2 is a diagram of a mathematical model of a frequency hopping system;
FIG. 3 is an internal schematic diagram of an SSD image processing monitoring framework;
FIG. 4 is one of the partial enlarged views of FIG. 3;
FIG. 5 is a second enlarged view of a portion of FIG. 3;
fig. 6 is a schematic diagram of selecting a designated network station from the frequency hopping signals of the two network stations.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Example 1
As shown in fig. 1 to 6, the invention provides a method for sorting a plurality of frequency hopping network stations by using an image processing method aiming at the problem that the frequency hopping signal network stations based on the multi-network station are difficult to sort, thereby greatly improving the sorting success rate of the frequency hopping signal network stations of the multi-network station.
Aiming at the problem that the frequency hopping signal network stations based on the multi-network station are difficult to sort, a plurality of frequency hopping network stations are sorted by using an image processing method, experimental simulation is carried out, and the result shows that the image processing method can greatly improve the sorting success rate of the frequency hopping signal network stations of the multi-network station.
The invention discloses a method for sorting a frequency hopping signal network table based on image processing, belongs to the field of signal processing, and aims at solving the problem that the frequency hopping signal network table based on a plurality of network tables is difficult to sort. The method comprises the following steps: 1) Generating and collecting frequency hopping signal data of the multi-network station, and representing two network stations of the frequency hopping signal of the multi-network station in a time-frequency waterfall diagram; 2) Learning data of a specific network station by using an SSD image processing detection frame; 3) And recognizing the coordinates and the frequency band of the frequency hopping network station to be sorted by using the SSD detection frame after learning.
1. Mathematical model of frequency hopping system
Let h 1 (t) is a frequency hopping signal sent by a transmitter, then the expression is:
in the above formula, m=0, 1,2, …, N-1; wherein the method comprises the steps of
Digital information representing amplitude 1 without additional transmissionStream frequency hopping signal omega 0 Representing the initial operating frequency, omega, of the frequency-hopped signal ε For the interval of the hopping frequencies synthesized by the frequency synthesizer of the transmitter, the dwell time of each hop is denoted by T, ω can be obtained s =2pi/T, u (T) represents the digital information stream to be transmitted, +.>
Is the initial phase.
Frequency hopping signal h in a channel 1 (t) frequency hopping signal h with other network stations j And (t), gaussian noise g (t) and interference signal i (t) are mixed and then received by a frequency hopping signal receiver, and the signal expression received by the frequency hopping signal receiver is as follows:
in the above formula, h 1 (t) useful frequency hopping signal transmitted by frequency hopping signal transmitter, h j (t) is a frequency hopping signal of other network stations, j=2, 3, …, n.
As shown in fig. 2, the signal h received by the receiver r (t) and receiver local signals
Multiplication results in the following formula: />
In the above formula, ω r For the center frequency of the receiver local signal, it is equal to ω 0 Differ by one omega i 。
If the transmitting end and the receiving end of the frequency hopping system have completed synchronization, the following formula can be deduced:
in the above formula, ω i Represents the intermediate frequency, ω i ＝ω r -ω 0 ；
Initial phase of receiver local signal, then +.>
When T epsilon [ nT, (n+1) T ], each jump of the frequency hopping signal causes the mixer to output a specific intermediate frequency, and then the following signal components are obtained after intermediate frequency filtering:
as shown in the above formula, h 12 And (t) is fed into a demodulator to demodulate the transmitted digital information stream u (t). Of particular note is h j (t) is the frequency hopping signal of other network stations, and the frequency hopping signals of all the network stations have different frequency hopping patterns. In order to avoid mutual interference among network stations when each network station is in networking, frequency hopping signal hopping is designed into a mode of orthogonality and mutual non-overlapping, so that interference caused by other network stations to the current demodulated network station is not needed to be considered in the patent.
2. Generation and acquisition of frequency hopping signal data of multi-network station
The generation and collection of the frequency hopping signal data of the multi-network station mainly comprises the step of representing one frequency hopping network station with one frequency hopping period and the other frequency hopping network station with two frequency hopping periods in a time-frequency waterfall chart, wherein the time-frequency waterfall chart set is a target time-frequency waterfall chart set used for being detected. Meanwhile, the frequency hopping network station with one time hopping period represents a time-frequency waterfall diagram, the frequency hopping signal network station with one time hopping period with a modulation mode of QAM, BPSK, QPSK is used as a starting material of machine learning in a time-frequency waterfall diagram, the time-frequency waterfall diagram is used as an initial material of machine learning in the time-frequency waterfall diagram, and then the learned SSD monitoring frame is used for carrying out the following steps, namely, the required designated network station is selected from the double network stations in the target time-frequency waterfall diagram set according to the signal-to-noise ratio SNR = 10dB, the SNR= -8dB, the SNR= -6dB, the SNR = 4dB, the SNR = 2dB, the SNR = 0dB, the SNR = 2dB, the SNR = 4dB, the SNR = 6dB, the SNR = 8dB and the SNR = 10 dB.
3. Introduction of image processing detection framework and SSD-based frequency hopping signal network table sorting
The SSD detection framework for image processing used in this patent is implemented by Python source code and is available on its functional network through an open source license at the university of Conneler (https:// gitub. Com/weiliu 89/caffe/tree/SSD). In 2017, shaoqing Ren and Ross Girsheck and other scholars propose Fast RCNN detection frames, and the recognition accuracy and recognition speed of the RCNN detection frames are remarkably improved. To solve a series of problems of RCNN generation candidate regions, joseph redson et al have proposed a YOLO target monitoring framework. In 2016, weiLiu and Dragomir Anguelov et al have proposed an SSD detection framework that not only employs the anchor mechanism of FastRCNN, but also inherits the regression strategy of YOLO. The SSD detection framework has the main idea that the type and the offset coordinates of the detection targets on the feature diagrams with different sizes are predicted by utilizing convolution check, so that the accuracy and the instantaneity of target detection are greatly improved. The SSD detection framework model is based on a multi-box target theory, which can be used to train multiple targets.
The SSD detection frame can return to the normalized coordinates (y) of each hop frequency band of the appointed network station detected in the time-frequency waterfall diagram 1 ,x 1 ,y 2 ,x 2 ) This coordinate, i.e. the coordinates of the four anchor points of the rectangular box, is then scaled by the sampling frequency f s The center frequency and the instantaneous bandwidth of the hopping signal can be obtained, and the two parameters and the coordinates can determine the selected specific network stations needing interference.
Fig. 3 illustrates the internal principles of the SSD image processing monitoring framework, where for convolution prediction of image detection, only one set of convolution kernels may be used for stationary detection when adding each feature layer or adding the current feature layer of the available underlying network. For a feature layer comprising n channels and having a size a×b, detection can be performed by converting into a small convolution kernel of 3×3×n, as shown in fig. 3. The convolution kernel generates not only the evaluation score of the detection target but also the coordinate shift of the marker frame of the detection recognition.
Fig. 6 shows the frequency hopping signal of a sorting-designated network station among the frequency hopping signals of the two network stations (x-axis represents time in seconds; y-axis represents frequency in Hz).
As shown in fig. 6, the two network stations select the frequency hopping signal of the designated network station, and we designate the frequency hopping signal of which only the frequency hopping period is shorter, and it can be seen that most of the frequency hopping signals of short frequency hopping period can be identified under the SSD detection frame, but if part of the frequency hopping signals are identified incorrectly, the identification effect score of each frequency hopping signal is acceptable, and all the frequency hopping signals of which the frequency hopping period is shorter can be identified. From this we can conclude that the method of the invention can be used for identifying and detecting the frequency hopping signals of the multi-network station, and has wider applicability than the current mainstream detection method.
The invention provides a method for sorting a plurality of frequency hopping network stations by utilizing an image processing method aiming at the problem that the frequency hopping signal network stations based on the multi-network station are difficult to sort, and the result shows that the image processing method can greatly improve the sorting success rate of the frequency hopping signal network stations of the multi-network station.
As described above, the present invention can be preferably implemented.
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
The foregoing description of the preferred embodiment of the invention is not intended to limit the invention in any way, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The frequency hopping signal network table sorting method based on image processing is characterized by comprising the following steps of:
s1, data generation and acquisition: generating and collecting frequency hopping signal data of the multi-network station, and representing two network stations of the frequency hopping signal of the multi-network station in a time-frequency waterfall diagram;
s2, learning: learning data of a specific network station by using an SSD image processing detection frame;
s3, identifying: and recognizing the coordinates and the frequency band of the frequency hopping network station to be sorted by using the SSD detection frame after learning.
2. The method for sorting a frequency hopping signal network station based on image processing according to claim 1, wherein in step S1, the frequency hopping signal sent by the transmitter is:
wherein h is 1 (t) is a frequency hopping signal sent by a transmitter, u (t) is a digital information stream to be transmitted,
frequency hopping signal, ω, representing a digital information stream of amplitude 1, without the addition of a digital information stream to be transmitted 0 For transmitting an initial operating frequency point for hopping a frequency hopping signal, m is the number of hopping points, m=0, 1,2,..n-1, N is the number of total hopping points, t is time, ω ε The spacing of the individual hopping frequencies synthesized for the frequency synthesizer of the transmitter,/->
Is the initial phase.
3. The method for sorting a frequency hopping signal network according to claim 2, wherein in step S1, the signal received by the frequency hopping signal receiver is:
wherein h is r (t) is the signal received by the receiver, n is the total number of the frequency hopping network stations, j is the number of the current network station, and h j (t) is a hopping signal corresponding to the jth network station, g (t) is noise added by a channel, and i (t) is an artificially applied interference signal.
4. A method for sorting a frequency hopping signal network station based on image processing as claimed in claim 3, wherein in step S1, if the transmitting end and the receiving end of the frequency hopping system have completed synchronization, then:
wherein omega r Representing the center frequency, ω, of the local frequency synthesizer t Represents the intermediate frequency, w l ＝w r -w o ，
Initial phase of the receiver local signal, +.>
5. The method for sorting a frequency hopping signal network table based on image processing as claimed in claim 4, wherein in step S1, when T e [ nT, (n+1) T ], each hop of the frequency hopping signal causes the mixer to output a specific intermediate frequency, and when the intermediate frequency is filtered, the following signal components are obtained:
wherein h is 12 (t) represents the useful signal component obtained by intermediate frequency filtering, and h is 12 And (t) is fed into a demodulator to demodulate the transmitted digital information stream u (t).
6. The method according to any one of claims 1 to 5, wherein in step S1, one frequency hopping network station of one frequency hopping period and the other frequency hopping network station of two frequency hopping periods are both represented in a time-frequency waterfall diagram, and the set of time-frequency waterfall diagrams is a target time-frequency waterfall diagram for the detected object.
7. The method according to claim 6, wherein in step S1, the frequency hopping tables of one time of the frequency hopping period are displayed in a time-frequency waterfall chart.
8. The method according to claim 7, wherein in step S1, the signal modulation scheme is QAM, BPSK, or QPSK.
9. The method for sorting frequency hopping signal network stations based on image processing according to claim 8, wherein in step S3, the detected normalized coordinates are multiplied by the sampling frequency in proportion to obtain the center frequency and the instantaneous bandwidth of frequency hopping signal hopping, and the specific network stations needing interference can be determined according to the two parameters and the coordinates.
10. A frequency hopping signal network station sorting system based on image processing, which is characterized in that the method for realizing the frequency hopping signal network station sorting method based on image processing according to any one of claims 1 to 9 comprises the following modules which are connected in sequence:
the data generation and acquisition module: the method comprises the steps of generating and collecting frequency hopping signal data of a plurality of network stations, and representing two network stations of the frequency hopping signal of the plurality of network stations in a time-frequency waterfall diagram;
and a learning module: the SSD image processing detection framework is used for learning data of a specific network station;
and an identification module: the method is used for identifying the coordinates and the frequency bands of the frequency hopping network stations to be sorted by utilizing the SSD detection frame after learning.
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