Sequential Frequency Hopping Signal Detection Method, Apparatus, Computer Device, and Storage Medium

Abstract

This application belongs to the field of radio detection and perception, and relates to a sequential frequency hopping signal detection method, including: obtaining the single-frame spectrum data of the current signal to obtain frequency data and amplitude data; calculating the contrast of the current signal using the sliding window detection method and obtaining multiple pulses of the current signal; obtaining the historical target set, and calculating the likelihood probabilities of each pulse of the current signal belonging to each historical target and belonging to a new target; according to the feature distributions of each historical target, calculating the prior probability of the new mode and the prior probabilities of each historical target, then calculating the posterior probabilities of each pulse of the current signal belonging to each historical target, and updating the feature distributions of each historical target to obtain the current feature distributions of each historical target; when it is determined that the current feature distribution meets the preset conditions, outputting the current signal as a frequency hopping signal. This method realizes sequential frequency hopping signal detection based on single-frame data and under the condition that the frequency table is unknown.

Description

Technical Field

[0001] The present application relates to the technical field of radio detection and perception, and particularly to a sequential frequency-hopping signal detection method, device, computer device, and storage medium. Background Technique

[0002] One of the main technical means for unmanned aerial vehicle (UAV) detection is to passively receive and process the communication signals transmitted by UAVs for passive detection, identification, and positioning of UAVs. Frequency-hopping spread spectrum (FHSS) signals are a common signal format used in UAV communication, and frequency-hopping signal detection is a key technology to be solved in anti-UAV passive detection.

[0003] Frequency-hopping signal detection in anti-UAV passive detection faces two problems: 1) The duty cycle of frequency-hopping signals is relatively small, and in order to cover a wide enough frequency band range, the receiver cannot stay on a frequency band for a long time; 2) The frequency table of the frequency-hopping signals of UAVs with unknown models cannot be predicted or there are frequency jumps.

[0004] Therefore, in the prior art, it is necessary to perform time-sequence correlation detection on frequency-hopping signals through multi-frame stream data, and frequency-hopping signal detection needs to be carried out under the condition that the frequency table is known. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a sequential frequency-hopping signal detection method, device, computer device, and storage medium, which can realize frequency-hopping signal detection based on single-frame data and under the condition that the frequency table is unknown.

[0006] A sequential frequency-hopping signal detection method includes:

[0007] Obtain and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data;

[0008] Set a left window, a right window, and a center window, and use a sliding window detection method to calculate the contrast of the current signal; screen the contrast according to a preset first condition, and obtain multiple pulses of the current signal according to the frequency data and the amplitude data;

[0009] Obtain the historical target set, obtain the feature distributions of the respective historical targets, and calculate the likelihood probabilities that the respective pulses of the current signal belong to the respective historical targets and the likelihood probabilities that the respective pulses of the current signal belong to a new target; according to the feature distributions of the respective historical targets, calculate the prior probability of the new modality and the prior probabilities of the respective historical targets; according to the prior probability of the new modality, the prior probabilities of the respective historical targets, the likelihood probabilities that the respective pulses of the current signal belong to the respective historical targets, and the likelihood probabilities that the respective pulses of the current signal belong to a new target, calculate the posterior probabilities that the respective pulses of the current signal belong to the respective historical targets;

[0010] According to the posterior probabilities that the respective pulses of the current signal belong to the respective historical targets, update the feature distributions of the respective historical targets to obtain the current feature distributions of the respective historical targets;

[0011] When it is determined that the current feature distribution satisfies a preset second condition, output that the current signal is a frequency-hopping signal.

[0012] In one embodiment, it further includes:

[0013] According to the prior probability of the new modality, the prior probabilities of the respective historical targets, the likelihood probabilities that the respective pulses of the current signal belong to the respective historical targets, and the likelihood probabilities that the respective pulses of the current signal belong to a new target, calculate the posterior probabilities that the respective pulses of the current signal belong to the new target;

[0014] When it is determined that the posterior probabilities that the respective pulses of the current signal belong to the new target satisfy a preset third condition, establish a new target; initialize the feature distribution of the new target to obtain an initial feature distribution, and update the historical target set according to the new target and the respective historical targets to obtain the next target set and the corresponding feature distributions of the respective next targets;

[0015] Obtain the single-frame spectrum data of the next signal and obtain multiple pulses of the next signal; according to the next target set and the corresponding feature distributions of the respective next targets, calculate the posterior probabilities that the respective pulses of the next signal belong to the respective next targets; according to the posterior probabilities that the respective pulses of the next signal belong to the respective next targets, update the corresponding feature distributions of the respective next targets, and determine and output whether the next signal is a frequency-hopping signal.

[0016] In one embodiment, when it is determined that the posterior probabilities that the respective pulses of the current signal belong to the new target do not satisfy a preset third condition, do not establish a new target, and directly update the historical target set to obtain the next target set and the corresponding feature distributions of the respective next targets.

[0017] In one embodiment, obtaining the historical target set, obtaining the feature distributions of the respective historical targets, and calculating the likelihood probabilities that the respective pulses of the current signal belong to the respective historical targets specifically includes:

[0018] The feature distributions of each historical target include: historical frequency mean, historical frequency mean square deviation, historical amplitude mean, and historical amplitude mean square deviation;

[0019]

[0020] wherein, is the likelihood probability that the nth pulse of the current signal belongs to the mth historical target, is the likelihood probability that the nth pulse of the current signal belongs to the mth historical target in the frequency dimension, is the likelihood probability that the nth pulse of the current signal belongs to the mth historical target in the amplitude dimension, is the frequency of the nth pulse of the current signal, is the historical frequency mean of the mth historical target, is the root mean square of the historical frequency of the mth historical target, is the amplitude of the nth pulse of the current signal, is the historical amplitude mean of the mth historical target, is the root mean square of the historical amplitude of the mth historical target.

[0021] In one embodiment, calculating the likelihood probability that each pulse of the current signal belongs to a new target is specifically as follows:

[0022]

[0023]

[0024]

[0025] wherein, is the likelihood probability that the nth pulse of the current signal belongs to the new target, r f is the frequency distribution range of the current signal and the preset parameter, r a is the amplitude distribution range of the current signal and the preset parameter, is the frequency distribution range of the current signal, is the amplitude distribution range of the current signal, and are preset parameters.

[0026] In one embodiment, according to the prior probability of the new modality, the prior probabilities of each historical target, the likelihood probabilities that each pulse of the current signal belongs to each historical target, and the likelihood probabilities that each pulse of the current signal belongs to the new target, calculating the posterior probabilities that each pulse of the current signal belongs to each historical target is specifically as follows:

[0027]

[0028]

[0029]

[0030] Wherein, is the posterior probability that the nth pulse of the current signal belongs to the mth historical target, is the prior probability that the nth pulse belongs to the mth historical target, is the prior probability that the nth pulse belongs to a new mode, c m is the number of pulses of the current signal contained in the mth historical target within a certain time window, and α is the Dirichlet distribution function.

[0031] In one embodiment, according to the posterior probabilities that each pulse of the current signal belongs to each historical target, the feature distribution of each historical target is updated to obtain the current feature distribution of each historical target, specifically:

[0032] The current feature distribution of each historical target includes: current frequency mean, current frequency mean square deviation, current amplitude mean, and current amplitude mean square deviation;

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] Wherein, is the number of pulses of the current signal contained in the mth historical target within a certain time window, γ is the forgetting factor, and slide_frames is the number of data frames corresponding to the sliding window, is the current frequency mean of the mth historical target, is the current root mean square of the frequency of the mth historical target, is the current amplitude mean of the mth historical target, is the current root mean square of the amplitude of the mth historical target.

[0040] A sequential frequency hopping signal detection device, comprising:

[0041] An acquisition module, configured to acquire and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data;

[0042] A setting module, configured to set a left window, a right window, and a center window, and calculate the contrast of the current signal using a sliding window detection method; screen the contrast according to a preset first condition, and obtain multiple pulses of the current signal based on the frequency data and the amplitude data;

[0043] A calculation module, configured to obtain a historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; calculate the prior probability of the new modality and the prior probability of each historical target according to the feature distribution of each historical target; calculate the posterior probability that each pulse of the current signal belongs to each historical target according to the prior probability of the new modality, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target;

[0044] An update module, configured to update the feature distribution of each historical target according to the posterior probability that each pulse of the current signal belongs to each historical target, and obtain the current feature distribution of each historical target;

[0045] An output module, configured to output that the current signal is a frequency hopping signal when it is determined that the current feature distribution meets a preset second condition.

[0046] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0047] Obtain and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data;

[0048] Set a left window, a right window, and a center window, and calculate the contrast of the current signal using a sliding window detection method; screen the contrast according to a preset first condition, and obtain multiple pulses of the current signal based on the frequency data and the amplitude data;

[0049] Obtain a historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; calculate the prior probability of the new modality and the prior probability of each historical target according to the feature distribution of each historical target; calculate the posterior probability that each pulse of the current signal belongs to each historical target according to the prior probability of the new modality, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target;

[0050] Update the feature distribution of each historical target according to the posterior probability that each pulse of the current signal belongs to each historical target, and obtain the current feature distribution of each historical target;

[0051] When it is determined that the current feature distribution meets a preset second condition, output that the current signal is a frequency-hopping signal.

[0052] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0053] Obtain and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data;

[0054] Set a left window, a right window, and a center window, and use a sliding window detection method to calculate the contrast of the current signal; screen the contrast according to a preset first condition, and obtain multiple pulses of the current signal according to the frequency data and the amplitude data;

[0055] Obtain a set of historical targets, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; according to the feature distribution of each historical target, calculate the prior probability of the new mode and the prior probability of each historical target; according to the prior probability of the new mode, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target, calculate the posterior probability that each pulse of the current signal belongs to each historical target;

[0056] Update the feature distribution of each historical target according to the posterior probability that each pulse of the current signal belongs to each historical target, and obtain the current feature distribution of each historical target;

[0057] When it is determined that the current feature distribution meets a preset second condition, output that the current signal is a frequency-hopping signal.

[0058] The above sequential frequency-hopping signal detection method, device, computer device, and storage medium include a frequency-hopping signal detection method based on single-frame stream data and an unknown frequency table. Based on sequence spectrum data, fully considering prior knowledge such as the spectrum characteristics of frequency-hopping pulses and inter-pulse correlation, and comprehensively using methods such as signal processing and adaptive correlation estimation, it can effectively capture frequency-hopping characteristics only by using single-frame data, thereby detecting frequency-hopping signals with unknown frequency tables, solving the problem in the prior art that multi-frame stream data and a known frequency table are required for frequency-hopping signal detection, and is particularly suitable for anti-drone passive detection. Description of the Drawings

[0059] Figure 1It is a diagram of the application scenario of the sequential frequency hopping signal detection method in an embodiment;

[0060] Figure 2 It is a schematic flowchart of the sequential frequency hopping signal detection method in an embodiment;

[0061] Figure 3 It is a schematic flowchart of the sequential frequency hopping signal detection method in another embodiment;

[0062] Figure 4 It is a narrowband pulse detection diagram in an embodiment;

[0063] Figure 5 It is an effect diagram of the sequential frequency hopping signal detection of typical measured data in an embodiment;

[0064] Figure 6 It is a structural block diagram of the sequential frequency hopping signal detection device in an embodiment;

[0065] Figure 7 It is an internal structure diagram of a computer device in an embodiment. Specific embodiments

[0066] In order to make the objectives, technical solutions and advantages of this application clearer, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0067] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0068] In addition, the descriptions such as "first" and "second" in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "multiple groups" is at least two groups, such as two groups, three groups, etc., unless otherwise specifically defined.

[0069] In this application, unless otherwise clearly specified or limited, terms such as "connection" and "fixation" shall be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0070] In addition, the technical solutions between various embodiments of this application can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0071] The method provided by this application can be applied to, for example, Figure 1 the application environment shown in the figure. Among them, the terminal 102 communicates with the server 104 through the network. The terminal 102 can include but is not limited to various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The server 104 can be various portal websites, servers corresponding to the background of the work system, etc.

[0072] This application provides a method for detecting sequential frequency hopping signals. As Figure 2 shown, in one embodiment, taking the application of this method to the Figure 1 terminal in the figure as an example for illustration, it includes:

[0073] Step 202: Obtain and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data.

[0074] In this step, obtaining the single-frame spectrum data of the current signal means the IQ timing data of radio frequency reception and down-conversion to the baseband, and using Fourier transform to process this data to obtain the spectrum data of the current signal, including: frequency data, amplitude data, and phase data, so as to complete the generation of the timing spectrum data.

[0075] Step 204: Set the left window, right window, and center window, and use the sliding window detection method to calculate the contrast of the current signal; screen the contrast according to the preset first condition, and obtain multiple pulses of the current signal according to the frequency data and amplitude data.

[0076] In this step, set the number and size of the windows. Specifically, the size of the center window is generally set to 1-3, and the left and right windows are set to 1-2, and then calculate the contrast of the current narrowband signal, that is, the minimum value of the difference between the center average and the left and right averages:

[0077]

[0078] Wherein, contrast_nb is the contrast of the current signal, is the average amplitude of the central window, is the average amplitude of the left window, is the average amplitude of the right window;

[0079] The current signal is traversed using a sliding window detection method to obtain multiple contrasts, and the maximum value of the contrast sequence is searched, that is, each contrast is judged according to the first condition, and multiple narrowband pulses of the current signal are screened out, so as to complete the narrowband pulse detection based on single-frame spectrum data.

[0080] The first condition can be: reaching the threshold T c = 5dB, this threshold is equivalent to the signal-to-noise ratio threshold; the narrowband pulses obtained by screening are denoted as wherein, i is the frame count, is the frequency of the nth pulse of the current signal, is the amplitude of the nth pulse of the current signal.

[0081] Step 206, obtain the historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; according to the feature distribution of each historical target, calculate the prior probability of the new modality and the prior probability of each historical target; according to the prior probability of the new modality, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target, calculate the posterior probability that each pulse of the current signal belongs to each historical target.

[0082] Specifically:

[0083] Obtain the historical target set, the historical target set includes multiple historical targets, and use the Gaussian model to represent the feature distribution of each historical target; the feature distribution of each historical target includes: historical frequency mean, historical frequency mean square deviation, historical amplitude mean, and historical amplitude mean square deviation;

[0084] When it is judged that the current target signal list is not empty (that is, the historical target set is not empty), that is, when it is not the first judgment (the first observation), calculate the likelihood probability that each pulse of the current signal belongs to each historical target, that is, calculate the likelihood probability of the frequency and amplitude of each pulse of the current signal relative to each feature distribution:

[0085]

[0086] In the formula, The likelihood probability that the nth pulse of the current signal belongs to the mth historical target The likelihood probability that the nth pulse of the current signal belongs to the mth historical target in the frequency dimension The likelihood probability that the nth pulse of the current signal belongs to the mth historical target in the amplitude dimension The frequency of the nth pulse of the current signal The historical frequency mean of the Gaussian distribution of the mth historical target The historical root mean square of the frequency of the Gaussian distribution of the mth historical target The amplitude of the nth pulse of the current signal The historical amplitude mean of the Gaussian distribution of the mth historical target The historical root mean square of the amplitude of the Gaussian distribution of the mth historical target

[0087] Using a uniform distribution to represent the distribution of new target features, calculate the likelihood probability that each pulse of the current signal belongs to the new target:

[0088]

[0089]

[0090]

[0091] In the formula, The likelihood probability that the nth pulse of the current signal belongs to the new target, r f The frequency distribution range of the current signal and the preset parameters, r a The amplitude distribution range of the current signal and the preset parameters and the preset parameters The frequency distribution range of the current signal The amplitude distribution range of the current signal And Are preset parameters, and the specific numerical range is flexibly selected according to empirical values and actual situations, which belongs to the prior art

[0092] It should be noted that the likelihood probabilities that each pulse of the current signal belongs to the new target are the same

[0093] According to the prior probability of the new modality, the prior probabilities of each historical target, the likelihood probabilities that each pulse of the current signal belongs to each historical target, and the likelihood probabilities that each pulse of the current signal belongs to the new target, calculate the posterior probabilities that each pulse of the current signal belongs to each historical target:

[0094]

[0095]

[0096]

[0097] wherein, is the posterior probability that the nth pulse of the current signal belongs to the mth historical target, is the prior probability that the nth pulse belongs to the mth historical target, is the prior probability that the nth pulse belongs to the new mode, c m is the number of pulses of the current signal contained in the mth historical target within a certain time window, and α is the Dirichlet distribution parameter, generally taken as 0.2.

[0098] In this step, first calculate the likelihood probabilities that each pulse of the current signal belongs to each historical target and the likelihood probabilities that each pulse of the current signal belongs to a new target, then calculate the prior probability of the new mode and the prior probabilities of each historical target, and finally calculate the posterior probabilities that each pulse of the current signal belongs to each historical target, thereby completing pulse probability association.

[0099] Step 208, update the feature distribution of each historical target according to the posterior probabilities that each pulse of the current signal belongs to each historical target, and obtain the current feature distribution of each historical target.

[0100] Specifically:

[0101] According to the posterior probabilities that each pulse of the current signal belongs to each historical target and the feature distribution of each historical target (the feature distribution of each historical target includes: historical pulse count, historical frequency mean, historical frequency mean square deviation, historical amplitude mean, historical amplitude root mean square), update the feature distribution of each historical target, and obtain the current feature distribution of each historical target (the current feature distribution of each historical target includes: current frequency mean, current frequency mean square deviation, current amplitude mean, and current amplitude mean square deviation):

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] wherein, $n_m$ is the number of pulses of the current signal included in the $m$-th historical target within a certain time window, $\gamma$ is the forgetting factor, which depends on the desired size of the sliding observation window, and slide_frames is the number of data frames corresponding to the sliding window. $\bar{f}_m$ is the current frequency mean of the $m$-th historical target. $rms_f_m$ is the root mean square of the current frequency of the $m$-th historical target. $\bar{a}_m$ is the current amplitude mean of the $m$-th historical target. $rms_a_m$ is the root mean square of the current amplitude of the $m$-th historical target.

[0109] In this step, the current frequency mean, current frequency variance, current amplitude mean, and current amplitude variance of each historical target are updated, thus completing the update of the target information.

[0110] Step 210: When it is determined that the current feature distribution meets the preset second condition, the current signal is output as a frequency-hopping signal and reported; otherwise, it is other signals, that is, not a frequency-hopping signal.

[0111] Specifically, the second condition can be:

[0112]

[0113]

[0114] In the formula, $n_m$ is the pulse count of each target. $d$ is the duty cycle. $\sigma_f^2$ is the frequency variance, & is the AND operation, $T$ c $T_n$ is the judgment threshold of the pulse count of each target, $T$ d $T_d$ is the judgment threshold of the duty cycle, $T$ σf $T_{\sigma_f}$ is the judgment threshold of the frequency variance. Each judgment threshold ($T$ c 、$T$ d 、$T$ σf ) is flexibly valued according to empirical values and actual situations, which belongs to the prior art.

[0115] This step completes the judgment of the frequency-hopping signal.

[0116] This application is a sequential frequency hopping signal detection method, that is, a frequency hopping signal detection method based on time series clustering. A time series is a set of random variables sorted by time. It is usually the result of observing a certain potential process at equal intervals of time according to a given sampling rate. It refers to a sequence formed by arranging the values of the same statistical indicator in the order of their occurrence time, which hides some past and future relationships and is adapted to the single-frame data specifically used in the present invention; clustering is to divide a data set into different classes or clusters according to a specific criterion (such as distance), so that the difference within the class is minimized and the difference between classes is maximized, which is adapted to the signal classification in the present invention.

[0117] The above sequential frequency hopping signal detection method is a frequency hopping signal detection method based on single-frame stream data with an unknown frequency table. Based on sequence spectrum data, it fully considers prior knowledge such as the frequency spectrum characteristics of frequency hopping pulses and inter-pulse correlation, and comprehensively uses methods such as signal processing and adaptive correlation estimation. Only using single-frame data can effectively capture the frequency hopping characteristics, thereby detecting frequency hopping signals with unknown frequency tables, solving the problem in the prior art that multi-frame stream data and a known frequency table are required to detect frequency hopping signals, and is particularly suitable for anti-drone passive detection. Passive detection mainly judges the frequency modulation signal by receiving the signals emitted by drones.

[0118] It should be understood that although Figure 2 the steps in the flowchart of Figure 2 are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0119] In one embodiment, as Figure 3 shown, it further includes: calculating the posterior probability that each pulse of the current signal belongs to the new target according to the prior probability of the new modality, the prior probabilities of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to the new target:

[0120]

[0121] In the formula, is the posterior probability that the nth pulse of the current signal belongs to the new target;

[0122] When the posterior probability that each pulse of the current signal belongs to the new target satisfies a preset third condition (the third condition may be: ), a new target is established, and the feature distribution of the new target is initialized to obtain an initial feature distribution:

[0123]

[0124] In the formula, and are preset parameters; the preset parameters represent the prior features of the fluctuations of the frequency and amplitude of a certain target. In order to suppress the influence of single-frequency continuous waves on the clustering of frequency-hopping signals, while establishing single-frequency and frequency-hopping signals, different frequency fluctuation parameters are used for single-frequency. is very small for single-frequency, indicating that the frequency frames of the single-frequency target signal are basically the same. For frequency-hopping, is very large, indicating that the frequency frames of the frequency-hopping signal fluctuate greatly; generally, can take 10, can take 2.

[0125] According to the new target and each historical target, the historical target set is updated to obtain the next target set and the feature distributions of each next target; it should be noted that at this time, updating the historical target set includes: combining the new target and each historical target to form each next target; according to the posterior probability that each pulse of the current signal belongs to each historical target, updating the feature distribution of each historical target to obtain the current feature distribution of each historical target; the initial feature distribution of the new target and the current feature distributions of each historical target together form the feature distributions of each next target; each next target and the feature distributions of each next target form the next target set.

[0126] When the posterior probability that each pulse of the current signal belongs to the new target does not satisfy the preset third condition, a new target is not established, and the historical target set is directly updated to obtain the next target set and the corresponding feature distributions of each next target; it should be noted that at this time, updating the historical target set includes: using each historical target as each next target; according to the posterior probability that each pulse of the current signal belongs to each historical target, updating the feature distribution of each historical target to obtain the current feature distribution of each historical target as the feature distribution of each next target; each next target and the feature distributions of each next target form the next target set.

[0127] After obtaining the next target set and the feature distributions of each next target, obtain the single-frame spectrum data of the next signal and obtain multiple pulses of the next signal; according to the next target set and the corresponding feature distributions of each next target, calculate the posterior probability that each pulse of the next signal belongs to each next target; according to the posterior probability that each pulse of the next signal belongs to each next target, update the corresponding feature distributions of each next target, and determine and output whether the next signal is a frequency-hopping signal.

[0128] That is to say, in this embodiment, after the judgment of the previous signal (data of the previous moment) is completed, when judging whether the next signal (data of the next moment) is a frequency-hopping signal, the judgment process of the previous signal will update the historical target set, that is, update each target in the set and the feature distribution of each target. This update process may include the process of establishing a new target and initializing the feature distribution of the new target, or may not include it. By using the method of continuously updating the features, the target feature information can be continuously approximated to the true value.

[0129] It should also be noted that before calculating the likelihood probability that each pulse of the current signal belongs to each historical target, if the current target signal list is empty, then let That is, if the third condition is satisfied, directly establish a new target and initialize the feature distribution of the new target.

[0130] In a specific embodiment, the detection effect of the sequential frequency-hopping signal of the narrowband pulse is detected and the typical measured data is obtained, such as Figure 4 and Figure 5 As shown, it can be seen from the figure that the judgment of the frequency-hopping signal is good. Figure 4 In, the abscissa is the frequency, the unit is Hz, the ordinate is the amplitude, the unit is dB, the solid line represents the current signal, and the dotted line represents the pulse determination. Figure 5 In, the abscissa is the frequency, the unit is Hz, the ordinate is the time, the unit is ms, the box represents the detection of the frequency-hopping pulse train, and the white dot represents the detection of a single frequency-hopping signal.

[0131] The present application also provides a sequential frequency-hopping signal detection device, such as Figure 6 As shown, in one embodiment, it includes: an acquisition module 602, a setting module 604, a calculation module 606, an update module 608, and an output module 610, where:

[0132] The acquisition module 602 is used to acquire and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data;

[0133] A setting module 604 is configured to set a left window, a right window, and a central window, and calculate the contrast of the current signal using a sliding window detection method; screen the contrast according to a preset first condition, and obtain multiple pulses of the current signal based on frequency data and amplitude data;

[0134] A calculation module 606 is configured to obtain a historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; calculate the prior probability of a new modality and the prior probability of each historical target according to the feature distribution of each historical target; calculate the posterior probability that each pulse of the current signal belongs to each historical target according to the prior probability of the new modality, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target;

[0135] An update module 608 is configured to update the feature distribution of each historical target according to the posterior probability that each pulse of the current signal belongs to each historical target, and obtain the current feature distribution of each historical target;

[0136] An output module 610 is configured to determine that when the current feature distribution meets a preset second condition, output that the current signal is a frequency hopping signal.

[0137] For the specific limitations on the sequential frequency hopping signal detection device, reference can be made to the limitations on the sequential frequency hopping signal detection method in the above text, which will not be elaborated here. Each module in the above device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0138] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 7As shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a sequential frequency hopping signal detection method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0139] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0140] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method in the above embodiment.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps of the method in the above embodiment.

[0142] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0143] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0144] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A sequential frequency hopping signal detection method, characterized in that, Including: Obtain and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data; Set a left window, a right window, and a center window, and use the sliding window detection method to calculate the contrast of the current signal; screen the contrast according to a preset first condition, search for the maximum value of the contrast sequence, and obtain multiple pulses of the current signal according to the frequency data and the amplitude data; Obtain the historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target and the likelihood probability that each pulse of the current signal belongs to a new target; according to the feature distribution of each historical target, calculate the prior probability of the new mode and the prior probability of each historical target; according to the prior probability of the new mode, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target, calculate the posterior probability that each pulse of the current signal belongs to each historical target; obtain the historical target set, obtain the feature distribution of each historical target, and calculate the likelihood probability that each pulse of the current signal belongs to each historical target, specifically: The feature distribution of each historical target includes: historical frequency mean, historical frequency mean square deviation, historical amplitude mean, and historical amplitude mean square deviation; Wherein, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target in the frequency dimension, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target in the amplitude dimension, is the frequency of the n th pulse of the current signal, is the historical frequency mean of the m th historical target, is the historical root mean square of the frequency of the m th historical target, is the amplitude of the n th pulse of the current signal, is the historical amplitude mean of the m th historical target, is the historical root mean square of the amplitude of the m th historical target; Calculate the likelihood probability that each pulse of the current signal belongs to a new target, specifically: In the formula, is the likelihood probability that the n -th pulse of the current signal belongs to a new target, is the frequency distribution range of the current signal and the preset parameters, is the amplitude distribution range of the current signal and the preset parameters, is the frequency distribution range of the current signal, is the amplitude distribution range of the current signal, and are preset parameters; According to the prior probability of the new mode, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target, calculate the posterior probability that each pulse of the current signal belongs to each historical target, specifically: Wherein, is the posterior probability that the n th pulse of the current signal belongs to the m th historical target, is the prior probability that the n th pulse belongs to the m th historical target, is the prior probability that the n th pulse belongs to the new mode, is the number of pulses of the current signal contained in the m th historical target within a certain time window, is the dirichlet distribution parameter; Update the feature distribution of each historical target according to the posterior probability that each pulse of the current signal belongs to each historical target to obtain the current feature distribution of each historical target; When it is determined that the current feature distribution meets a preset second condition, output the current signal as a frequency hopping signal; the second condition is: Wherein, is the pulse count of each target, is the duty cycle, is the frequency variance, is the AND operation, is the judgment threshold of the pulse count of each target, is the judgment threshold of the duty cycle, is the judgment threshold of the frequency variance.

2. The sequential frequency hopping signal detection method according to claim 1, wherein Also including: According to the prior probability of the new mode, the prior probability of each historical target, the likelihood probability that each pulse of the current signal belongs to each historical target, and the likelihood probability that each pulse of the current signal belongs to a new target, calculate the posterior probability that each pulse of the current signal belongs to a new target; When it is determined that the posterior probability that each pulse of the current signal belongs to a new target meets a preset third condition, establish a new target; initialize the feature distribution of the new target to obtain an initial feature distribution, and update the historical target set according to the new target and each historical target to obtain the next target set and the corresponding feature distribution of each next target; Obtain the single-frame spectrum data of the next signal and obtain multiple pulses of the next signal; calculate the posterior probability that each pulse of the next signal belongs to each next target according to the next target set and the corresponding feature distribution of each next target; update the corresponding feature distribution of each next target according to the posterior probability that each pulse of the next signal belongs to each next target, and judge and output whether the next signal is a frequency hopping signal.

3. The sequential frequency hopping signal detection method according to claim 2, wherein When the posterior probability that each pulse of the current signal belongs to a new target does not meet the preset third condition, no new target is established, and the historical target set is directly updated to obtain the next target set and the corresponding feature distributions of each next target.

4. The sequential frequency hopping signal detection method according to any one of claims 1 to 3, characterized in that According to the posterior probabilities that each pulse of the current signal belongs to each historical target, update the feature distributions of each historical target to obtain the current feature distributions of each historical target, specifically: The current feature distributions of each historical target include: current frequency mean, current frequency mean square deviation, current amplitude mean, and current amplitude mean square deviation; Wherein, is the number of pulses of the current signal included in the m th historical target within a certain time window, is the forgetting factor, is the number of data frames corresponding to the sliding window, is the current frequency mean of the m th historical target, is the current root mean square of the frequency of the m th historical target, is the current amplitude mean of the m th historical target, is the current root mean square of the amplitude of the m th historical target.

5. A sequential frequency hopping signal detection device, characterized in that, Include: An acquisition module, configured to acquire and process the single-frame spectrum data of the current signal to obtain the frequency data and amplitude data of the single-frame spectrum data; A setting module, configured to set a left window, a right window, and a center window, and calculate the contrast of the current signal by using a sliding window detection method; screen the contrast according to a preset first condition, search for the maximum value of the contrast sequence, and obtain multiple pulses of the current signal according to the frequency data and the amplitude data; A calculation module, configured to obtain a historical target set, obtain the feature distributions of each historical target, and calculate the likelihood probabilities that each pulse of the current signal belongs to each historical target and the likelihood probabilities that each pulse of the current signal belongs to a new target; calculate the prior probability of a new mode and the prior probabilities of each historical target according to the feature distributions of each historical target; calculate the posterior probabilities that each pulse of the current signal belongs to each historical target according to the prior probability of the new mode, the prior probabilities of each historical target, the likelihood probabilities that each pulse of the current signal belongs to each historical target, and the likelihood probabilities that each pulse of the current signal belongs to a new target; obtain a historical target set, obtain the feature distributions of each historical target, and calculate the likelihood probabilities that each pulse of the current signal belongs to each historical target, specifically: The feature distributions of each historical target include: historical frequency mean, historical frequency mean square deviation, historical amplitude mean, and historical amplitude mean square deviation; Wherein, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target in the frequency dimension, is the likelihood probability that the n th pulse of the current signal belongs to the m th historical target in the amplitude dimension, is the frequency of the n th pulse of the current signal, is the historical frequency mean of the m th historical target, is the historical frequency root mean square of the m th historical target, is the amplitude of the n th pulse of the current signal, is the historical amplitude mean of the m th historical target, is the historical amplitude root mean square of the m th historical target; Calculate the likelihood probabilities that each pulse of the current signal belongs to a new target, specifically: Wherein, is the likelihood probability that the n -th pulse of the current signal belongs to a new target, is the frequency distribution range of the current signal and the preset parameters, is the amplitude distribution range of the current signal and the preset parameters, is the frequency distribution range of the current signal, is the amplitude distribution range of the current signal, and are preset parameters; Calculate the posterior probabilities that each pulse of the current signal belongs to each historical target according to the prior probability of the new mode, the prior probabilities of each historical target, the likelihood probabilities that each pulse of the current signal belongs to each historical target, and the likelihood probabilities that each pulse of the current signal belongs to a new target, specifically: Wherein, is the posterior probability that the n -th pulse of the current signal belongs to the m -th historical target, is the prior probability that the n -th pulse belongs to the m -th historical target, is the prior probability that the n -th pulse belongs to the new modality, is the number of pulses of the current signal contained in the m -th historical target within a certain time window, is the dirichlet distribution parameter; An update module, configured to update the feature distributions of each historical target according to the posterior probabilities that each pulse of the current signal belongs to each historical target to obtain the current feature distributions of each historical target; An output module, configured to determine that when the current feature distribution meets a preset second condition, output that the current signal is a frequency-hopping signal; the second condition is: Wherein, is the pulse count of each target, is the duty cycle, is the frequency variance, is the AND operation, is the judgment threshold of the pulse count of each target, is the judgment threshold of the duty cycle, is the judgment threshold of the frequency variance.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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