CN112994740B - Frequency hopping signal parameter estimation method and device, electronic equipment and readable storage medium - Google Patents
Frequency hopping signal parameter estimation method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for estimating parameters of frequency hopping signals, electronic equipment and a readable storage medium, and relates to the technical field of communication. And performing power time analysis processing and frequency time analysis processing on the data to be processed by acquiring the data to be processed to obtain an analysis result. And judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result. Therefore, the data to be processed is analyzed and processed by combining the power time analysis processing and the frequency time analysis processing, and the accuracy of frequency hopping parameter estimation in complex engineering application is improved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for estimating parameters of a frequency hopping signal, an electronic device, and a readable storage medium.
Background
In recent years, with the rapid development of modern information technology, frequency hopping communication technology is widely applied to the military field and the civil communication field due to the characteristics of good anti-interference performance, safe reliability, low interception capability and the like. On one hand, the frequency hopping communication technology is used as a main technical means of military communication and is applied to short-wave and ultra-short-wave radio stations, so that the fighting capacity of troops is greatly improved; on the other hand, the application of the method in electronic systems such as civil mobile communication, modern radars and sonars accelerates the development of modern communication technology. Therefore, research on frequency hopping communication technology has a profound effect on military communication and technological progress.
At present, the frequency is often clustered, and then the hopping time and the hopping time are calculated according to the hopping frame. Although the frequency hopping-based estimation method can effectively estimate the frequency, the estimation of the hopping time of the frequency can only be approximately located to the frame number, so that a large error exists, and the estimation of the switching time of the frequency hopping signal is greatly influenced.
At present, each frequency component of the frequency hopping signal is often extracted by using a filter, and time-frequency distribution with high time-frequency resolution is obtained after linear superposition, so that the residence time is obtained. However, in this method, the selection of the filter is difficult, and the time-frequency resolution is greatly affected by the sideband of the filter.
How to improve the accuracy of frequency hopping parameter estimation in complex engineering applications is a problem worthy of research at present.
Disclosure of Invention
In view of the above, embodiments of the present application provide a method and an apparatus for estimating parameters of a frequency hopping signal, an electronic device, and a readable storage medium to solve the above problem.
In a first aspect, the present application provides a method for estimating parameters of a frequency hopping signal, the method including:
acquiring data to be processed;
performing power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;
and judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating the parameters of the data to be processed according to the analysis result to obtain an estimation result.
In an optional embodiment, the frequency-time analysis processing includes short-time fourier transform processing, spectrogram processing, and frequency clustering processing, and the step of performing power-time analysis processing and frequency-time analysis processing on the data to be processed to obtain an analysis result includes:
performing power time analysis processing on the data to be processed to obtain a power time analysis result;
carrying out short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;
performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;
performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;
and taking the power time analysis result and the window frequency data of the data to be processed as analysis results.
In an optional embodiment, the data to be processed includes an in-phase and quadrature signal and a sampling frequency, the spectrogram processing result includes spectrum data, and the step of estimating the parameter of the data to be processed according to the analysis result to obtain the estimation result includes:
setting a time multiple threshold value and a power judgment threshold value according to the in-phase orthogonal signal and the sampling frequency included in the data to be processed;
calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold;
performing positive peak positioning processing on the frequency spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed;
and taking the switching time and the residence time as estimation results.
In an optional embodiment, the step of calculating the switching time of the to-be-processed data according to the power time analysis result, the power judgment threshold, and the time multiple threshold includes:
obtaining a switching time period of which the power is smaller than the power judgment threshold in the power time analysis result;
obtaining a power conversion critical point according to the time multiple threshold, and calculating a starting point and an end point of each switching time period according to the power conversion critical point;
calculating initial switching time of a starting point and an end point of each switching time period, and calculating an average value of all the initial switching time;
and taking the average value of the initial switching time as the switching time of the data to be processed.
In an optional embodiment, the positive peak location processing includes first derivative processing, smoothing processing, and zero crossing point detection, and the step of performing positive peak location processing on the spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed includes:
calculating a first derivative of the spectrum data to obtain a derivative result, wherein the derivative result represents all maximum value points of the spectrum data;
smoothing the derivation result to obtain a smoothed derivation result;
carrying out zero crossing point detection on the derivative result after the smoothing treatment to obtain a signal spectrum peak;
screening to obtain a positive spectrum peak in the signal spectrum peaks and a frequency corresponding to the positive spectrum peak;
and calculating the residence time of the data to be processed according to the frequency.
In an optional embodiment, the step of calculating the residence time of the data to be processed according to the frequency includes:
calculating the frequency step of the positive spectrum peak according to the frequency;
calculating the frequency interval of each positive spectral peak according to the frequency step, and taking the minimum frequency interval as the frequency step of the data to be processed;
calculating the frequency difference values of all adjacent frequencies, and screening to obtain a frequency set with the frequency difference value larger than half of the frequency step;
and obtaining the residence time of the data to be processed according to the frequency set.
In an alternative embodiment, the method further comprises:
judging whether the data to be processed comprises a plurality of complete switching time periods and a plurality of complete residence time periods according to the analysis result;
if it is determined that the to-be-processed data includes a plurality of complete switching time periods and a plurality of complete residence time periods, the residence time corresponding to the minimum residence time period is used as the residence time of the to-be-processed data, the average value of the switching times corresponding to the complete switching time periods is calculated, and the average value is used as the switching time of the to-be-processed data.
In a second aspect, the present application provides an apparatus for estimating parameters of a frequency hopping signal, the apparatus comprising:
the acquisition module is used for acquiring data to be processed;
the analysis processing module is used for carrying out power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;
and the estimation module is used for judging whether the data to be processed is a frequency hopping signal according to the analysis result, and estimating the parameters of the data to be processed according to the analysis result to obtain an estimation result if the data to be processed is determined to be the frequency hopping signal.
In a third aspect, the present application provides an electronic device, which includes a processor, a memory and a bus, where the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the memory communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the steps of the frequency hopping signal parameter estimation method according to any one of the foregoing embodiments.
In a fourth aspect, the present application provides a readable storage medium, which stores a computer program, and the computer program is executed to implement the steps of the frequency hopping signal parameter estimation method according to any one of the foregoing embodiments.
The embodiment of the application provides a frequency hopping signal parameter estimation method, a frequency hopping signal parameter estimation device, electronic equipment and a readable storage medium. And judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result. Therefore, the data to be processed is analyzed and processed by combining the power time analysis processing and the frequency time analysis processing, and the accuracy of frequency hopping parameter estimation in complex engineering application is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, several embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 2 is a schematic flowchart of a method for estimating parameters of a frequency hopping signal according to an embodiment of the present application.
Fig. 3 is a schematic diagram illustrating a power time critical point positioning according to an embodiment of the present disclosure.
Fig. 4 is a schematic diagram of positive peak positioning provided in the embodiment of the present application.
Fig. 5 is a schematic diagram illustrating a comparison of switching time estimation errors according to an embodiment of the present application.
FIG. 6 is a schematic diagram comparing the estimated error of the residence time provided by the embodiment of the present application.
Fig. 7 is a functional block diagram of a frequency hopping signal parameter estimation apparatus according to an embodiment of the present application.
Icon: 100-an electronic device; 110-a memory; 120-a processor; 130-frequency hopping signal parameter estimation means; 131-an acquisition module; 132-an analysis processing module; 133-an estimation module; 140-a communication unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
As described in the background art, in recent years, with the rapid development of modern information technology, frequency hopping communication technology has been widely used in the military field and the civil communication field due to its characteristics of good anti-interference, safe reliability, and low interception capability. On one hand, the frequency hopping communication technology is used as a main technical means of military communication and is applied to short-wave and ultra-short-wave radio stations, so that the fighting capacity of troops is greatly improved; on the other hand, the application of the method in electronic systems such as civil mobile communication, modern radars and sonars accelerates the development of modern communication technology. Therefore, research on frequency hopping communication technology has a profound effect on military communication and technological progress.
Aiming at the research of a parameter estimation method of a frequency hopping signal, the current research method of domestic scholars is mainly provided on the basis of time-frequency analysis. As in document von billows, yuan super great, new time-frequency analysis method of frequency hopping signal [ J ]. university of beijing post and telecommunications, 2010, 033 (003): 10-14, a time-frequency analysis method based on signal decomposition is provided. The method uses a filter to extract each frequency component of a frequency hopping signal, and obtains time-frequency distribution with high time-frequency resolution after linear superposition, thereby obtaining the residence time. However, in this method, the selection of the filter is difficult, and the time-frequency resolution is greatly affected by the sideband of the filter. In the literature populus diversifolia forest, frequency hopping signal blind detection and parameter blind estimation calculation method research and implementation [ D ]. electronic technology university, 2016, an estimation method based on frequency hopping is adopted. The method has the main idea that the frequency is clustered, and then the hopping time and the hopping time are calculated according to the hopping frame. Although the frequency hopping-based estimation method can effectively estimate the frequency, the estimation of the hopping time of the frequency can only be approximately located to the frame number, so that a large error exists, and the estimation of the switching time of the frequency hopping signal is greatly influenced. Moreover, most of the current frequency hopping signal parameter estimation methods only perform theoretical analysis in a white gaussian noise environment, and do not consider the special situation of power change when hopping occurs, so that the requirement for effective estimation of frequency hopping parameters in complex engineering application cannot be met.
How to improve the accuracy of frequency hopping parameter estimation in complex engineering applications is a problem worthy of research at present.
In view of this, embodiments of the present application provide a method, an apparatus, an electronic device, and a readable storage medium for estimating parameters of a Frequency hopping signal, which combine Power and Time (PVT) and Frequency and Time (FVT) analysis and processing techniques to analyze and process data to be processed, and comprehensively determine whether the Frequency hopping signal (data to be processed) jumps or not from Power changes and Frequency changes of a jump point, so as to enhance accuracy of determining a jump Time of the Frequency hopping signal, thereby improving accuracy of estimating the Frequency hopping parameters.
The above prior art solutions have drawbacks that are the results of practical and careful study, and therefore, the discovery process of the above problems and the solutions proposed by the following embodiments of the present application to the above problems should be the contributions of the applicant to the present application in the course of the present application.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the keys in the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a block diagram of an electronic device 100 according to an embodiment of the present disclosure. The device may include a processor 120, a memory 110, a frequency hopping signal parameter estimation apparatus 130 and a communication unit 140, where the memory 110 stores machine readable instructions executable by the processor 120, when the electronic device 100 operates, the processor 120 and the memory 110 communicate with each other through a bus, and the processor 120 executes the machine readable instructions and performs the frequency hopping signal parameter estimation method.
The elements of the memory 110, the processor 120 and the communication unit 140 are electrically connected to each other directly or indirectly to realize the transmission or interaction of signals.
For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The frequency hopping signal parameter estimation means 130 includes at least one software functional module that can be stored in the memory 110 in the form of software or firmware (firmware). The processor 120 is configured to execute an executable module stored in the memory 110, such as a software functional module or a computer program included in the frequency hopping signal parameter estimation apparatus 130.
The Memory 110 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 120 may be an integrated circuit chip having signal processing capabilities. The Processor 120 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and so on.
But may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In the embodiment of the present application, the memory 110 is used for storing a program, and the processor 120 is used for executing the program after receiving the execution instruction. The method defined by the process disclosed in any of the embodiments of the present application can be applied to the processor 120, or implemented by the processor 120.
The communication unit 140 is used to establish a communication connection between the electronic apparatus 100 and another electronic apparatus via a network, and to transmit and receive data via the network.
In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof.
In the embodiment of the present application, the electronic device 100 may be, but is not limited to, a smart phone, a personal computer, a tablet computer, or the like having a processing function.
It will be appreciated that the configuration shown in figure 1 is merely illustrative. Electronic device 100 may also have more or fewer components than shown in FIG. 1, or a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The frequency hopping communication technology is an effective anti-interference communication technology, and one of the main tasks is to estimate parameters of a frequency hopping signal, including frequency hopping rate, frequency hopping bandwidth, switching time, frequency hopping frequency number, and the like. The frequency hopping rate and the switching time are key technical indexes for representing parameter estimation of the frequency hopping signal, and the frequency hopping rate is equal to the reciprocal of the residence time, so that the research is mainly carried out on the residence time and the switching time of the frequency hopping signal.
The steps of the method, the apparatus, the electronic device and the readable storage medium for estimating the parameters of the frequency hopping signal according to the embodiment of the present application are described in detail below based on the structural diagram of the electronic device 100 shown in fig. 1.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a method for estimating parameters of a frequency hopping signal according to an embodiment of the present disclosure.
Step S1, data to be processed is acquired.
And step S2, performing power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result.
And step S3, judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating the parameters of the data to be processed according to the analysis result to obtain an estimation result.
The data to be processed may be a frequency hopping signal or may not be a frequency hopping signal, and therefore, it is necessary to determine in advance whether the data to be processed is a frequency hopping signal, and perform parameter estimation on the data to be processed by using an analysis result when the data to be processed is a frequency hopping signal, where the estimation result in this embodiment is the switching time and the residence time of the data to be processed.
The frequency hopping signal parameter estimation method provided by the embodiment of the application combines power time analysis processing and frequency time analysis processing, and simultaneously comprehensively judges whether the signal jumps or not from the power change and the frequency change of the jumping point, so that the judgment accuracy of the jumping moment of the frequency hopping signal is enhanced, and the problem of doubled estimation of the residence time caused by no jumping of the frequency hopping signal at the power of the jumping point when only the power time analysis processing is used can be avoided; the problem of overlarge estimation deviation of the switching time when only frequency time analysis processing is used can be solved, and therefore the accuracy of frequency hopping signal parameter estimation is improved.
In an alternative embodiment, the frequency-Time analysis processing includes Short-Time Fourier Transform (STFT) processing, Spectrum Picture processing (SP), and frequency clustering processing, and in step S2 shown in fig. 2, the power-Time analysis processing and the frequency-Time analysis processing are performed on the data to be processed, and the analysis result can be obtained in the following manner:
and carrying out power time analysis processing on the data to be processed to obtain a power time analysis result. And carrying out short-time Fourier transform processing on the data to be processed to obtain the time-frequency characteristics of the data to be processed. And performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result. And carrying out frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed. And taking the power time analysis result and the window frequency data of the data to be processed as the analysis result.
The power time analysis refers to a power-time analysis method for describing the joint characteristics of signal power and time. The power time analysis can display the change information of the signal power along with the time under the condition that the power of the frequency hopping signal is unknown in advance.
Optionally, provided with
For the data to be processed, the following power-time combination function can be utilized
Performing power time analysis processing on data to be processed (data to be processed):
the change condition of the power at the jumping point corresponding to the data to be processed can be displayed according to the analysis result of the power time analysis processing of the data to be processed, so that the primary judgment of the jumping moment is realized. It can be understood that in the formula
Also the power time analysis results.
Alternatively, in the SFTF process, non-stationary signals in the time domain are first windowed and segmented, i.e., the entire time domain process is decomposed into multiple processes by shifting the window in the time axis, and the signal in each segment is approximated to a stationary signal. Then, Fast Fourier Transform (FFT) is performed on each segmented signal, thereby obtaining spectral information of the signal within each segment. And finally, combining the frequency spectrum information of each section according to a time sequence to obtain the time-frequency characteristics of the signal. According to the STFT principle, data to be processed
The short-time fourier transform of (a) is defined as:
wherein the content of the first and second substances,
as a matter of time, the time is,
in order to be the frequency of the radio,
is a window function. The short-time Fourier transform algorithm does not contain cross interference terms, and can achieve higher operation speed by utilizing fast Fourier transform, and the algorithm complexity is lower. However, the STFT method has a disadvantage that the time resolution and the frequency resolution are restricted from each other and cannot be optimized. Therefore, the time frequency data after short-time Fourier transform is processedPerforming modular square processing to obtain a spectrogram so as to improve time-frequency resolution, namely:
after passing through the STFT process and the SP process, the spectral information of each segment is analyzed. The length of the window is set as
The number of segments is
Then the overall signal frequency matrix F can be expressed as:
and clustering the frequency in each window, wherein the operation process mainly comprises the steps of firstly comparing the amplitude values of adjacent frequency points in the length of each window, and then selecting the frequency point corresponding to the maximum amplitude value as the window frequency. Obtained by frequency clustering operations
Frequency of segment window
And the rough estimation of the jumping moment of the data to be processed can be realized by comparing the frequency values of the adjacent windows.
In an optional embodiment, the data to be processed includes an in-phase quadrature signal and a sampling frequency, the spectrogram processing result includes spectrum data, and the step of estimating the parameter of the data to be processed according to the analysis result to obtain the estimation result includes:
and setting a time multiple threshold value and a power judgment threshold value according to the in-phase orthogonal signal and the sampling frequency included by the data to be processed. And calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold. And performing positive peak positioning processing on the frequency spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed. And taking the switching time and the residence time as estimation results.
The set power judgment threshold may be half of the power of the data to be processed. The time multiple threshold may be a multiple of adjacent time intervals. The in-phase quadrature signal is an IQ signal, I is in-phase, Q is quadrature, and is 90 degrees out of phase with I. The signal can be conveniently represented by a complex signal method based on the IQ signal; the sampling rate of each branch can be reduced (since the sampling rate will be twice if amplitude detection is used), reducing the requirements on the ADC; while also preserving the phase information of the original signal.
As an alternative implementation, the switching time of the data to be processed may be calculated by:
and obtaining a switching time period of which the power is smaller than the power judgment threshold in the power time analysis result. And obtaining a power conversion critical point according to the time multiple threshold, and calculating a starting point and an end point of each switching time period according to the power conversion critical point. Initial switching times of a start point and an end point of each switching period are calculated, and an average value of all the initial switching times is calculated. And taking the average value of the initial switching time as the switching time of the data to be processed.
For example, the threshold value is judged according to the power
All switching time periods are screened out, namely:
then, all adjacent time points in the switching time period are differenceddiff（tswitchpart) And according to a time multiple threshold
Locate all critical points
As shown in fig. 3, fig. 3 is a schematic diagram of positioning a power time critical point according to an embodiment of the present disclosure. As shown in the enlarged area of the figure, there is a switching time period from asterisk to asterisk, and the asterisk represents the critical point of the transition, which can be expressed as:
t critical point of =t（fun(diff（t switchpar ）)＞ 
)
According to the critical point
The starting point of each switching segment can be obtained
And an end point
Therefore, all the switching times when the data to be processed jumps within the analysis duration are calculated, namely:
wherein the content of the first and second substances,
. Then, taking a plurality of switching time averages of data to be processed jumping within the analysis duration as switching times of the data to be processed, namely:
as an alternative embodiment, the residence time of the data to be processed may be calculated by:
and calculating a first derivative of the spectrum data to obtain a derivative result, wherein the derivative result represents all maximum value points of the spectrum data. And smoothing the derivative result to obtain the derivative result after smoothing. And carrying out zero crossing point detection on the derivative result after the smoothing treatment to obtain a signal spectrum peak. And screening to obtain a positive spectrum peak in the signal spectrum peaks and a frequency corresponding to the positive spectrum peak. And calculating the residence time of the data to be processed according to the frequency.
The positive peak positioning mechanism is a method for calculating the step length of frequency hopping of the data to be processed by positioning the actual peak position of the frequency spectrum of a signal, and the method can enhance the effectiveness of parameter estimation of the data to be processed. The positive peak positioning processing comprises first derivative processing, smoothing processing and zero crossing point detection.
In the first derivative processing, the first derivative processing may be regarded as obtaining a slope of the signal at each point, obtaining spectral data included in the spectrogram data, and processing the spectral data (x, y) by using the center difference first derivative, where x is a frequency vector corresponding to the data, and y is a magnitude vector. The simple calculation method is as follows:
considering the influence of random fluctuation, the central difference idea is adopted to calculate the average slope between three adjacent points of the signal, namely:
in general, in the absence of noise, the first derivative of the signal spectrum has the characteristic of a downward zero crossing at the peak point, but in an actual environment, there are noise and interference conditions, which cause many maxima in the signal spectrum, so that the first derivative of the signal spectrum generates an unnecessary downward zero crossing. Therefore, in the technical scheme, a pseudo-Gaussian (pseudo-Gaussian) smoothing operation is adopted to smooth the data after the first derivative processing, so that the redundant zero-crossing point characteristics caused by factors such as noise and interference are reduced. Let the data after first derivative processing be
Which comprises an active ingredient
And random error components due to random noise interference or the like
Namely:
wherein the content of the first and second substances,
，
is the data length. According to the principle of moving average in
In a non-stationary data, each
The inter-range points of adjacent data are nearly stationary and will, therefore, be
Sliding fetching of individual data one by one
If the adjacent data are averaged to represent the smoothed data, a rectangular smoothing can be represented as:
further, the pseudo-Gaussian smoothing process can be expressed as:
wherein the content of the first and second substances,
. After pseudo-Gaussian smoothing processing, frequent random fluctuation in the data can be filtered out, and a smooth change trend is displayed.
Zero crossing detection refers to a method for detecting a position corresponding to a function sign when the function sign changes. Which includes both downward zero crossing detection and upward zero crossing detection. The positions corresponding to the wave crest and the wave trough of the signal data can be obtained through the downward zero-crossing detection and the upward zero-crossing detection respectively. Because the spectral peak of the signal is mainly considered in the technical scheme, the positive spectral peak position of the signal can be positioned by utilizing a downward zero crossing point detection method after pseudo-Gaussian smoothing processing is carried out on signal data.
Referring to fig. 4, fig. 4 is a schematic diagram of positioning a positive peak according to an embodiment of the present disclosure. As shown in fig. 4, the spectrum data may be represented by an amplitude spectrum, for example, a gray area located above in fig. 4, data processed by the first-order derivative is a white area in the middle of fig. 4, pseudo-Gaussian smoothing is performed on the data processed by the first-order derivative, frequent random fluctuations in the data may be filtered out, a smooth variation trend is displayed, the data processed by the smoothing is shown as a gray line in fig. 4, and a circle is a peak anchor point.
By a positive peak positioning mechanism, all positive spectral peaks in the signal spectrum can be searched to obtain the frequency corresponding to each positive spectral peak
. In this way, the residence time of the data to be processed can be calculated from the frequency.
In an alternative embodiment, calculating the residence time of the data to be processed according to the frequency may be implemented by:
the frequency step of the positive spectral peak is calculated from the frequency. And calculating the frequency interval of each positive spectrum peak according to the frequency step, and taking the minimum frequency interval as the frequency step of the data to be processed. And calculating the frequency difference values of all adjacent frequencies, and screening to obtain a frequency set with the frequency difference value larger than half of the frequency step. And obtaining the residence time of the data to be processed according to the frequency set.
For example, the frequency interval of the spectral peak is first calculated
And according to the frequency interval, the frequency step of the data to be processed can be obtained
Namely:
combining frequency steps
Obtaining adjacent frequency difference larger than the frequency difference obtained by the frequency time analysis processing result and the power time analysis processing result
Frequency set of
Then the dwell time of the data to be processed
Can be expressed as:
wherein the content of the first and second substances,
。
therefore, the key parameter frequency hopping rate of the data to be processed can be obtained according to the residence time.
Because the frequency hopping rate of the data to be processed is equal to the dwellInverse of time, the frequency hopping rate
Can be expressed as:
in an alternative embodiment, the method further comprises:
and judging whether the data to be processed comprises a plurality of complete switching time periods and a plurality of complete residence time periods or not according to the analysis result.
If the data to be processed comprises a plurality of complete switching time periods and a plurality of complete residence time periods, the residence time corresponding to the minimum residence time period is used as the residence time of the data to be processed, the average value of the switching times corresponding to the complete switching time periods is calculated, and the average value is used as the switching time of the data to be processed.
And if the data to be processed only contains a complete switching time period, calculating the switching time and the residence time of the switching time period.
And if the data to be processed does not contain any complete switching time period, outputting prompt information of incomplete one hop, and calculating the residence time of the data to be processed.
It can be understood that, in the above three cases, the principle of calculating the residence time and the switching time of the data to be processed can refer to the above process and method for solving the residence time and the switching time, which are not described herein again.
Further, please refer to fig. 5 and fig. 6 in combination, in which fig. 5 is a schematic diagram illustrating a comparison between the estimation errors of the switching time according to the embodiment of the present application, and fig. 6 is a schematic diagram illustrating a comparison between the estimation errors of the dwell time according to the embodiment of the present application.
In the embodiment of the application, the switching time and residence time estimation performance of the data to be processed can be estimated through relative errors
The calculation method comprises the following steps:
the experimental environment for comparative analysis may employ a vector signal generator model of Rohde & Schwarz SMBV100A, Germany, to generate signals according to parameter settings and collect data to be processed on a real-time spectrum analyzer model of Tektronix RSA6114A, USA. The simulation experiment tests 50 sets of acquired data to be processed under MATLAB, and compares the performance of the method provided in the embodiment of the present application with the result of the frequency hopping signal parameter estimation based on only the power time analysis processing mechanism and only the frequency time analysis processing mechanism, as shown in fig. 5 and 6.
From the simulation comparison, it can be seen from fig. 5 that, for the estimation of the switching time of the data to be processed, the estimation error based on the frequency-time analysis processing mechanism is larger than the estimation error based on the power-time analysis processing mechanism, and the switching time can be estimated more accurately in this example compared with the switching time estimation based on the power-time analysis processing mechanism and the frequency-time analysis processing mechanism. As can be seen from fig. 6, for the estimation of the residence time of the to-be-processed data, the estimation performance based on the power time analysis processing mechanism is poor, and there is a case of multiple estimation, but the power time analysis processing and the frequency time analysis processing mechanism can avoid this problem, and compared with the frequency time analysis processing mechanism, a smaller estimation error can be obtained, and effective estimation of the handover time and residence time parameter of the to-be-processed data is ensured.
Based on the same inventive concept, please refer to fig. 7 in combination, and fig. 7 is a functional block diagram of a frequency hopping signal parameter estimation apparatus according to an embodiment of the present application. The embodiment of the present application further provides a frequency hopping signal parameter estimation device 130 corresponding to the frequency hopping signal parameter estimation method shown in fig. 2, where the device includes:
the obtaining module 131 is configured to obtain data to be processed.
The analysis processing module 132 is configured to perform power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result.
And the estimating module 133 is configured to determine whether the data to be processed is a frequency hopping signal according to the analysis result, and estimate a parameter of the data to be processed according to the analysis result if the data to be processed is determined to be the frequency hopping signal, so as to obtain an estimation result.
Because the principle of the apparatus in the embodiment of the present application for solving the problem is similar to the principle of the above-mentioned method for estimating the parameters of the frequency hopping signal in the embodiment of the present application, the implementation principle of the apparatus can be referred to the implementation principle of the method, and repeated details are not repeated.
The embodiment of the application also provides a readable storage medium, in which a computer program is stored, and when the computer program is executed, the method, the apparatus, the electronic device and the readable storage medium for estimating the parameters of the frequency hopping signal are implemented.
In summary, the embodiments of the present application provide a method and an apparatus for estimating parameters of a frequency hopping signal, an electronic device, and a readable storage medium, where an analysis result is obtained by obtaining data to be processed, and performing power time analysis processing and frequency time analysis processing on the data to be processed. And judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result. Therefore, the data to be processed is analyzed and processed by combining the power time analysis processing and the frequency time analysis processing, and the accuracy of frequency hopping parameter estimation in complex engineering application is improved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for estimating parameters of a frequency hopping signal, the method comprising:
acquiring data to be processed;
performing power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;
judging whether the data to be processed is a frequency hopping signal or not according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result;
the frequency-time analysis processing comprises short-time Fourier transform processing, spectrogram processing and frequency clustering processing, the power-time analysis processing and the frequency-time analysis processing are carried out on the data to be processed, and the step of obtaining the analysis result comprises the following steps:
performing power time analysis processing on the data to be processed to obtain a power time analysis result;
performing power time analysis processing on the data to be processed according to the following formula:
wherein the content of the first and second substances,
the results of the power-time analysis are shown,
representing the data to be processed;
carrying out short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;
performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;
performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;
and taking the power time analysis result and the window frequency data of the data to be processed as analysis results.
2. The method according to claim 1, wherein the data to be processed includes an in-phase quadrature signal and a sampling frequency, the spectrogram processing result includes spectrum data, and the step of estimating the parameter of the data to be processed according to the analysis result to obtain the estimation result includes:
setting a time multiple threshold value and a power judgment threshold value according to the in-phase orthogonal signal and the sampling frequency included in the data to be processed;
calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold;
performing positive peak positioning processing on the frequency spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed;
and taking the switching time and the residence time as estimation results.
3. The method according to claim 2, wherein the step of calculating the switching time of the data to be processed according to the power time analysis result, the power judgment threshold and the time multiple threshold comprises:
obtaining a switching time period of which the power is smaller than the power judgment threshold in the power time analysis result;
obtaining a power conversion critical point according to the time multiple threshold, and calculating a starting point and an end point of each switching time period according to the power conversion critical point;
calculating initial switching time of a starting point and an end point of each switching time period, and calculating an average value of all the initial switching time;
and taking the average value of the initial switching time as the switching time of the data to be processed.
4. The method according to claim 2, wherein the positive peak location processing includes first derivative processing, smoothing processing, and zero crossing point detection, and the step of performing positive peak location processing on the spectrum data included in the spectrogram processing result to obtain the residence time of the data to be processed includes:
calculating a first derivative of the spectrum data to obtain a derivative result, wherein the derivative result represents all maximum value points of the spectrum data;
smoothing the derivation result to obtain a smoothed derivation result;
carrying out zero crossing point detection on the derivative result after the smoothing treatment to obtain a signal spectrum peak;
screening to obtain a positive spectrum peak in the signal spectrum peaks and a frequency corresponding to the positive spectrum peak;
and calculating the residence time of the data to be processed according to the frequency.
5. The method according to claim 4, wherein the step of calculating the dwell time of the data to be processed according to the frequency comprises:
calculating the frequency step of the positive spectrum peak according to the frequency;
calculating the frequency interval of each positive spectral peak according to the frequency step, and taking the minimum frequency interval as the frequency step of the data to be processed;
calculating the frequency difference values of all adjacent frequencies, and screening to obtain a frequency set with the frequency difference value larger than half of the frequency step;
and obtaining the residence time of the data to be processed according to the frequency set.
6. The method of claim 1, further comprising:
judging whether the data to be processed comprises a plurality of complete switching time periods and a plurality of complete residence time periods according to the analysis result;
if it is determined that the to-be-processed data includes a plurality of complete switching time periods and a plurality of complete residence time periods, the residence time corresponding to the minimum residence time period is used as the residence time of the to-be-processed data, the average value of the switching times corresponding to the complete switching time periods is calculated, and the average value is used as the switching time of the to-be-processed data.
7. An apparatus for estimating parameters of a frequency hopping signal, the apparatus comprising:
the acquisition module is used for acquiring data to be processed;
the analysis processing module is used for carrying out power time analysis processing and frequency time analysis processing on the data to be processed to obtain an analysis result;
the estimation module is used for judging whether the data to be processed is a frequency hopping signal according to the analysis result, and if the data to be processed is determined to be the frequency hopping signal, estimating parameters of the data to be processed according to the analysis result to obtain an estimation result;
the frequency-time analysis processing comprises short-time Fourier transform processing, spectrogram processing and frequency clustering processing, the power-time analysis processing and the frequency-time analysis processing are carried out on the data to be processed, and the analysis result is obtained by the following steps:
performing power time analysis processing on the data to be processed to obtain a power time analysis result;
performing power time analysis processing on the data to be processed according to the following formula:
wherein the content of the first and second substances,
the results of the power-time analysis are shown,
representing the data to be processed;
carrying out short-time Fourier transform processing on the data to be processed to obtain time-frequency characteristics of the data to be processed;
performing spectrogram processing on the time-frequency characteristics of the data to be processed to obtain a spectrogram processing result;
performing frequency clustering processing on the spectrogram processing result to obtain window frequency data of the data to be processed;
and taking the power time analysis result and the window frequency data of the data to be processed as analysis results.
8. An electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for estimating parameters of a frequency hopping signal according to any one of claims 1 to 6.
9. A readable storage medium, characterized in that the readable storage medium stores a computer program which, when executed, implements the steps of the frequency hopping signal parameter estimation method of any one of claims 1 to 6.
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