Frequency hopping signal processing method, device and equipment

Abstract

The invention provides a frequency hopping signal processing method, device and equipment. The method comprises the following steps: acquiring frequency spectrum data; performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; determining frequency point values in the target frequency spectrum data, performing first processing on the frequency point values, and determining a first frequency point set greater than a threshold value; when the target frequency spectrum data is the first frequency spectrum data, second processing is carried out on the first frequency point set to obtain a second frequency point set, and the second processing comprises clustering processing; when the target spectrum data is the nth spectrum data, n is not 1, third processing is carried out on the second frequency point set to obtain a third frequency point set, the third processing comprises clustering processing, and the third processing is different from the clustering processing corresponding to the second processing; and determining a current accumulated frequency spectrum data quantity value, and when the frequency spectrum data quantity value reaches a preset value, counting all the third frequency point sets to obtain a target frequency point set. The method is used for accurately analyzing and identifying the frequency modulation signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal measurement, and in particular relates to a method, device and equipment for processing frequency hopping signals. Background Art

[0002] Frequency hopping communication refers to a communication method in which the carrier of the transmission signal continuously jumps under the control of a pseudo-random code. It is widely used for its unique strong anti-interference ability, anti-fading, easy networking, low interception probability, confidentiality and security. The test of frequency hopping communication related indicators is a topic that the academic community has been studying. Generally speaking, the test indicators of frequency hopping communication include: frequency hopping rate, dwell time and frequency hopping frequency set.

[0003] There are many algorithms for testing frequency hopping communication indicators, but these algorithms are either highly complex, making them difficult to implement, or are not precise enough, resulting in inaccurate estimates of the frequency hopping rate, dwell time, and frequency hopping frequency set. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a frequency hopping signal processing method, device and equipment for accurately analyzing and identifying frequency modulation signals.

[0005] The present invention provides a method for processing a frequency hopping signal, comprising:

[0006] Get spectrum data;

[0007] Performing fast Fourier transform processing on the spectrum data to obtain target spectrum data;

[0008] Determine frequency point values ​​in the target spectrum data, and perform a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value;

[0009] When the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set, wherein the second process includes clustering process;

[0010] When the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set, the third process including a clustering process, and the third process is different from the clustering process corresponding to the second process;

[0011] The quantity value of the currently accumulated spectrum data is determined, and when the quantity value of the spectrum data reaches a preset value, all third frequency point sets are counted to obtain a target frequency point set.

[0012] In some embodiments, the method further comprises:

[0013] Acquire residence time data corresponding to the spectrum data;

[0014] The dwell time data is screened based on the target frequency point set to obtain a target dwell time.

[0015] In some embodiments, when performing fast Fourier transform processing on the spectrum data, it includes:

[0016] It is determined whether the PointNum calculated this time is the same as the PointNum calculated when the spectrum data was last fast Fourier transformed. If they are the same, the rotation factor of this time is determined based on the rotation factor calculated last time.

[0017] In some embodiments, the method further comprises:

[0018] The spectrum data acquired for the first time is set as initial data, and its spectrum value is determined as the initial value;

[0019] When the spectrum value of the target spectrum data is greater than the value of the current maximum held spectrum, the maximum held spectrum is updated based on the current target spectrum data.

[0020] In some embodiments, determining the frequency point values ​​in the target spectrum data and performing a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value includes:

[0021] Determining frequency point values ​​in the target spectrum data;

[0022] Dividing the frequency point value to obtain a plurality of sub-frequency point sets, each of the sub-frequency point sets having the same number of frequency points;

[0023] Performing mean processing on each of the sub-frequency point sets to obtain a first mean value;

[0024] Performing mean processing on the frequency points in the sub-frequency point set that are smaller than the first average value to obtain a second average value;

[0025] constructing the threshold value based on the second average value;

[0026] The frequency point values ​​in the target spectrum data are screened based on the threshold value to obtain the first frequency point set.

[0027] In some embodiments, when the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set includes:

[0028] When the target spectrum data is the first spectrum data, the difference between the previous frequency point value and the next frequency point value that satisfy the time sequence relationship in the first frequency point set is calculated in sequence;

[0029] If the difference satisfies a preset condition, it indicates that the two frequency point values ​​can be clustered;

[0030] Calculate the average value of the two frequency point values ​​that can be clustered, and use the average value as the reference frequency point;

[0031] All frequency points in the first frequency point set are traversed to obtain a second frequency point set consisting of a plurality of the reference frequency points.

[0032] In some embodiments, when the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set includes:

[0033] When the target spectrum data is the nth spectrum data, n is not 1, performing statistics on the second frequency point set to determine a frequency point error;

[0034] Screening the second frequency point set based on the frequency point error, and considering the frequency points whose frequency point errors satisfy a preset condition as the same frequency points;

[0035] Calculate and determine the average value of the same frequency point as a new frequency point value, and update the second frequency point set;

[0036] Counting the number of occurrences of each frequency point in the updated second frequency point set, and determining a valid frequency point based on the number of occurrences;

[0037] The valid frequency points are counted to form the third frequency point set.

[0038] In some embodiments, the counting of the number of occurrences of each frequency point in the updated second frequency point set and determining the valid frequency point based on the number of occurrences includes:

[0039] Determine threshold range;

[0040] The number of occurrences of each frequency point in the updated second frequency point set is counted, and the frequency point whose number is within the threshold range is determined as the valid frequency point.

[0041] Another embodiment of the present invention also provides a frequency hopping signal processing device, including:

[0042] An acquisition module, used for acquiring spectrum data;

[0043] A transformation module, used for performing fast Fourier transform processing on the spectrum data to obtain target spectrum data;

[0044] A first processing module, used for determining frequency point values ​​in the target spectrum data, and performing a first processing on the frequency point values ​​to determine a first frequency point set greater than a threshold value;

[0045] A second processing module, configured to perform a second processing on the first frequency point set to obtain a second frequency point set when the target spectrum data is the first spectrum data, wherein the second processing includes clustering processing;

[0046] a third processing module, configured to, when the target spectrum data is the nth spectrum data and n is not 1, perform a third processing on the second frequency point set to obtain a third frequency point set, wherein the third processing includes a clustering processing, and the third processing is different from the clustering processing corresponding to the second processing;

[0047] The statistical module is used to determine the quantity value of the currently accumulated spectrum data, and when the quantity value of the spectrum data reaches a preset value, all third frequency point sets are counted to obtain a target frequency point set.

[0048] Another embodiment of the present invention further provides an electronic device, comprising:

[0049] one or more processors;

[0050] a memory configured to store one or more programs;

[0051] When the one or more programs are executed by the one or more processors, the one or more processors implement the frequency hopping signal processing method as described in any one of the embodiments above.

[0052] The beneficial effect of the present invention is that accurate information about the frequency hopping signal can be obtained by utilizing fast Fourier transform processing, frequency point value processing, different clustering processing in different scenarios, and statistical processing of target frequency points. This process is not only simple in flow and low in complexity, saving memory space and computing time, and improving computing efficiency, but also has high accuracy and low error, and can obtain accurate estimation results for test indicators, that is, it can accurately measure the number of frequency hopping points, dwell time, and frequency hopping frequency set.

[0053] In addition, the present application further saves memory space and computing time by optimizing the fast Fourier transform algorithm, and further reduces errors and improves accuracy by performing maximum hold spectrum and multiple clustering processing on the frequency point set and combining pre-configured threshold values ​​for frequency point value screening.

[0054] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0055] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 The figure is a flow chart of a frequency hopping signal processing method in an embodiment of the present invention.

[0058] Figure 2 The figure is a schematic diagram of the application flow of the frequency hopping signal processing method in the embodiment of the present invention.

[0059] Figure 3 It is a flowchart of a frequency hopping signal processing method in another embodiment of the present invention.

[0060] Figure 4 4 is a structural block diagram of a frequency hopping signal processing device in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but are not intended to limit the present invention.

[0062] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope of the present disclosure will occur to those skilled in the art.

[0063] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0064] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0065] It should also be understood that, although the invention has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the invention that have the characteristics recited in the claims and are therefore within the scope of protection defined thereby.

[0066] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0067] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0068] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0069] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0070] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a frequency hopping signal processing method, characterized in that it includes:

[0071] S1: Acquire spectrum data;

[0072] S2: Performing fast Fourier transform processing on the spectrum data to obtain target spectrum data;

[0073] S3: determining frequency point values ​​in the target spectrum data, and performing a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value;

[0074] S4: when the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set, wherein the second process includes clustering process;

[0075] S5: when the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set, wherein the third process includes a clustering process, and the third process is different from the clustering process corresponding to the second process;

[0076] S6: Determine the quantity value of the currently accumulated spectrum data, and when the quantity value of the spectrum data reaches a preset value, count all third frequency point sets to obtain a target frequency point set.

[0077] The beneficial effect of this embodiment is that accurate information about the frequency hopping signal can be obtained by utilizing fast Fourier transform processing, frequency point value processing, different clustering processing in different scenarios, and statistical processing of the target frequency point. This process is not only simple in flow and low in complexity, saving memory space and computing time, and improving computing efficiency, but also has high accuracy and low error, and can obtain accurate estimation results for test indicators, that is, it can accurately measure the number of frequency hopping points, dwell time, and frequency hopping frequency set.

[0078] For example, the method of this embodiment can be applied to a signal acquisition and identification device, which includes an FPGA, a DSP, and a host computer. The host computer sends parameters such as the type of signal to be tested (such as a frequency hopping signal), frequency (such as 1.2Ghz), and bandwidth (such as 10Mhz) to the DSP, which then forwards the parameters to the FPGA. After the FPGA acquires and filters the signal through the ADC, the data packet containing the frequency hopping information is sent to the DSP through the SRIO. After the DSP calculates, it is passed to the host computer, which evaluates the results of different test indicators based on the obtained frequency hopping data.

[0079] In the following embodiments, the method is applied to the above-mentioned signal acquisition and identification device as an example for explanation, and the schemes described in this embodiment are all executed by the above-mentioned DSP. When the DSP executes step S1, the target time period can be selected as 1s. During this period, the FPGA will generate 1000 data packets, and one data packet contains a dwell time and 4096 frequency points. One data packet is sent once every 1ms, so the DSP needs to complete the frequency hopping algorithm within 1ms.

[0080] Specifically, Figure 3 As shown, the method also includes:

[0081] S7: Acquire the dwell time data corresponding to the spectrum data;

[0082] S8: Filtering the dwell time data based on the target frequency point set to obtain a target dwell time.

[0083] The dwell time is the time interval between two frequency points. During execution, the dwell time can be screened according to the number of frequency points in the target frequency point set obtained in the previous step, so that its value remains in a reasonable range, and the corresponding results are retained. The obtained dwell time will be uploaded to the host computer together with the previously obtained target frequency point set. After the host computer obtains the uploaded dwell time and target frequency point set, it can calculate the estimated value of the test index for frequency hopping communication and obtain the test conclusion at the same time, which significantly improves the test efficiency.

[0084] In another embodiment, when the DSP performs fast Fourier transform processing on the spectrum data, the DSP includes:

[0085] S9: Determine whether the PointNum calculated this time is the same as the PointNum calculated when the spectrum data was last fast Fourier transformed. If they are the same, determine the rotation factor for this time based on the rotation factor calculated last time.

[0086] For example, it is assumed that the number of frequency points is 4096 points, where the FFT (Fast Fourier Transform) uses assembly language to process multiple frequency points to obtain a new spectrum. In order to improve processing efficiency, the FFT built into the DSP is optimized in this embodiment. The specific optimization items include: (1) integrating the calculation part of the rotation factor into the algorithm implementation part of the DSP; (2) since it takes a long time to calculate the rotation factor, a judgment is added to the function. The judgment includes that if the PointNum calculated last time is the same as the PointNum calculated this time, the rotation factor calculated last time can be directly used to omit the rotation factor calculation link. The last time is the last time that the DSP performed a fast Fourier transform on the spectrum data; (3) The space occupied by the rotation factor is proportional to the PointNum. In order to improve the utilization of memory space, this embodiment uses the malloc function to dynamically apply for memory for the rotation factor.

[0087] In another embodiment, the method further comprises:

[0088] S10: setting the spectrum data acquired for the first time as initial data, and determining its spectrum value as the initial value;

[0089] S11: When the spectrum value of the target spectrum data is greater than the value of the current maximum held spectrum, the maximum held spectrum is updated based on the current target spectrum data.

[0090] The method in this embodiment is to update the maximum hold spectrum, specifically including obtaining the spectrum values ​​of 4096 points for the first time, and determining them as the target frequency point set, which is configured as the initial value set, and then comparing two by two according to the index each time new spectrum data is obtained, retaining the larger value and updating it to the initial set. That is, each time new spectrum data is obtained, it will be compared with the current maximum spectrum value, and the larger value will cover the initial set and update it to the new maximum spectrum.

[0091] Further, the determining of frequency point values ​​in the target spectrum data and performing a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value includes:

[0092] S12: Determine the frequency point value in the target spectrum data;

[0093] S13: Divide the frequency point value to obtain a plurality of sub-frequency point sets, each of which has the same number of frequency points;

[0094] S14: performing mean processing on each of the sub-frequency point sets to obtain a first mean value;

[0095] S15: performing mean processing on the frequency points in the sub-frequency point set that are smaller than the first average value to obtain a second average value;

[0096] S16: constructing the threshold value based on the second average value;

[0097] S17: Filter the frequency point values ​​in the target spectrum data based on the threshold value to obtain the first frequency point set.

[0098] For example, assuming that there are 4096 frequency points in the target frequency point set, then divide them into 64 equal parts in chronological order, each with 64 points, and then extract the frequency point value from each equal part. The extraction method is to first average the 64 points to obtain avg1, and then filter to obtain the frequency points less than avg1, and then average the frequency points to obtain avg2. Take avg2+bias (bias can be configured by the host computer) as the threshold value, and the bias can be, for example, but not limited to, 10. Extract all points greater than the threshold value to obtain frequency point set 1, that is, the first frequency point set.

[0099] In one embodiment, when the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set includes:

[0100] S18: when the target spectrum data is the first spectrum data, sequentially calculating the difference between the previous frequency point value and the next frequency point value that satisfy the time sequence relationship in the first frequency point set;

[0101] S19: If the difference satisfies a preset condition, it indicates that the two frequency point values ​​can be clustered;

[0102] S20: Calculate the average value of the two frequency point values ​​that can be clustered, and use the average value as the reference frequency point;

[0103] S21: Traverse all frequency points in the first frequency point set to obtain a second frequency point set consisting of a plurality of the reference frequency points.

[0104] For example, when the obtained spectrum is the first one, it does not need to be clustered with other spectrums. At this time, the method adopted in this embodiment is to use frequency point set 1, that is, the first frequency point set, as the analysis object, and compare the difference between the frequency points of the last point and the previous point, that is, the difference between two adjacent frequency point values ​​that meet the time sequence, such as first comparing the difference between the first frequency point and the 0th frequency point, and then comparing the difference between the second frequency point and the first frequency point, and so on. If the difference is less than the threshold value BandThre (BandThre can be configured by the upper computer), it is considered that the two points can be clustered. This cycle is repeated to obtain n frequency points until the number of obtained frequency points is greater than the corresponding threshold value, and then the average value of these n frequency points is taken as a reference frequency point of the second frequency point set. This cycle is repeated until all frequency points of frequency point set 1 are analyzed, and frequency point set 2, that is, the second frequency point set, is obtained.

[0105] In another embodiment, when the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set includes:

[0106] S22: when the target spectrum data is the nth spectrum data, and n is not 1, statistics are performed on the second frequency point set to determine a frequency point error;

[0107] S23: screening the second frequency point set based on the frequency point error, and considering the frequency points whose frequency point errors satisfy a preset condition as the same frequency points;

[0108] S24: Calculate and determine the average value of the same frequency point as a new frequency point value, and update the second frequency point set;

[0109] S25: Counting the number of occurrences of each frequency point in the updated second frequency point set, and determining a valid frequency point based on the number of occurrences;

[0110] S26: Counting the valid frequency points to form the third frequency point set.

[0111] The counting of the number of occurrences of each frequency point in the second frequency point set after the update, and determining the valid frequency point based on the number of occurrences includes:

[0112] S27: Determine a threshold range;

[0113] S28: Count the number of occurrences of each frequency point in the updated second frequency point set, and determine the frequency point whose number is within the threshold range as the valid frequency point.

[0114] For example, when the spectrum is not the first spectrum, the current spectrum and the previous spectrum need to be clustered. Specifically, the method is to make statistics on the N frequency point sets 2 (second frequency point sets) output by the N spectrums, such as making statistics on the 4096 frequency point sets 2 output by the 1000 spectrums, and calculate the frequency point errors between the frequency point sets, compare the error with the corresponding threshold value Thre (the Thre value can be configured by the host computer), and regard the frequency points whose frequency point errors are less than the threshold value Thre as the same frequency points, take the average value of the frequency points as the new frequency point value, and count the number of occurrences of each new frequency point value. If the number of occurrences is greater than or equal to M1 (M1 can be configured by the host computer) and less than or equal to M2 (M2 can be configured by the host computer), that is, M1 and M2 form a threshold value, where M1 and M2 can be set to 30 and 50. By modifying the threshold value, the number of valid frequency points can be continuously approached to the true value. Furthermore, if the number of occurrences is within the threshold, it is regarded as a valid frequency point, and the set of all valid frequency points is the final frequency point set, that is, the third frequency point set.

[0115] For step S6, the spectrum quantity value can be set to 1000 by default. When the spectrum quantity reaches 1000, the number of frequency point sets is counted. If the number meets the requirement, it is uploaded to the host computer. If it does not meet the requirement, the above steps will continue to be executed until the number of frequency points meets the requirement. For the third frequency point set obtained, the DSP will upload it and the dwell time data to the host computer through another core, and the specific recognition result will be displayed by the host computer.

[0116] like Figure 4 As shown, another embodiment of the present invention is a frequency hopping signal processing device 100, comprising:

[0117] A first acquisition module, used for acquiring spectrum data;

[0118] A transformation module, used for performing fast Fourier transform processing on the spectrum data to obtain target spectrum data;

[0119] A first processing module, used for determining frequency point values ​​in the target spectrum data, and performing a first processing on the frequency point values ​​to determine a first frequency point set greater than a threshold value;

[0120] A second processing module, configured to perform a second processing on the first frequency point set to obtain a second frequency point set when the target spectrum data is the first spectrum data, wherein the second processing includes clustering processing;

[0121] a third processing module, configured to, when the target spectrum data is the nth spectrum data and n is not 1, perform a third processing on the second frequency point set to obtain a third frequency point set, wherein the third processing includes a clustering processing, and the third processing is different from the clustering processing corresponding to the second processing;

[0122] The statistical module is used to determine the quantity value of the currently accumulated spectrum data, and when the quantity value of the spectrum data reaches a preset value, all third frequency point sets are counted to obtain a target frequency point set.

[0123] In some embodiments, the apparatus further comprises:

[0124] A second acquisition module, used to acquire the dwell time data corresponding to the spectrum data;

[0125] The screening module is used to screen the dwell time data according to the target frequency point set to obtain a target dwell time.

[0126] In some embodiments, when performing fast Fourier transform processing on the spectrum data, it includes:

[0127] It is determined whether the PointNum calculated this time is the same as the PointNum calculated when the spectrum data was last fast Fourier transformed. If they are the same, the rotation factor of this time is determined based on the rotation factor calculated last time.

[0128] In some embodiments, the apparatus further comprises:

[0129] A setting module, used for setting the spectrum data acquired for the first time as initial data, and determining its spectrum value as the initial value;

[0130] The updating module is used to update the maximum hold spectrum based on the current target spectrum data when the spectrum value of the target spectrum data is greater than the value of the current maximum hold spectrum.

[0131] In some embodiments, determining the frequency point values ​​in the target spectrum data and performing a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value includes:

[0132] Determining frequency point values ​​in the target spectrum data;

[0133] Dividing the frequency point value to obtain a plurality of sub-frequency point sets, each of the sub-frequency point sets having the same number of frequency points;

[0134] Performing mean processing on each of the sub-frequency point sets to obtain a first mean value;

[0135] Performing mean processing on the frequency points in the sub-frequency point set that are smaller than the first average value to obtain a second average value;

[0136] constructing the threshold value based on the second average value;

[0137] The frequency point values ​​in the target spectrum data are screened based on the threshold value to obtain the first frequency point set.

[0138] In some embodiments, when the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set includes:

[0139] When the target spectrum data is the first spectrum data, the difference between the previous frequency point value and the next frequency point value that satisfy the time sequence relationship in the first frequency point set is calculated in sequence;

[0140] If the difference satisfies a preset condition, it indicates that the two frequency point values ​​can be clustered;

[0141] Calculate the average value of the two frequency point values ​​that can be clustered, and use the average value as the reference frequency point;

[0142] All frequency points in the first frequency point set are traversed to obtain a second frequency point set consisting of a plurality of the reference frequency points.

[0143] In some embodiments, when the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set includes:

[0144] When the target spectrum data is the nth spectrum data, n is not 1, performing statistics on the second frequency point set to determine a frequency point error;

[0145] Screening the second frequency point set based on the frequency point error, and considering the frequency points whose frequency point errors satisfy a preset condition as the same frequency points;

[0146] Calculate and determine the average value of the same frequency point as a new frequency point value, and update the second frequency point set;

[0147] Counting the number of occurrences of each frequency point in the updated second frequency point set, and determining a valid frequency point based on the number of occurrences;

[0148] The valid frequency points are counted to form the third frequency point set.

[0149] In some embodiments, the counting of the number of occurrences of each frequency point in the updated second frequency point set and determining the valid frequency point based on the number of occurrences includes:

[0150] Determine threshold range;

[0151] The number of occurrences of each frequency point in the updated second frequency point set is counted, and the frequency point whose number is within the threshold range is determined as the valid frequency point.

[0152] Another embodiment of the present invention further provides an electronic device, comprising:

[0153] one or more processors;

[0154] a memory configured to store one or more programs;

[0155] When the one or more programs are executed by the one or more processors, the one or more processors implement the frequency hopping signal processing method as described in any one of the embodiments above.

[0156] Furthermore, an embodiment of the present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the frequency hopping signal processing method described above is implemented. It should be understood that each solution in this embodiment has the corresponding technical effect in the above method embodiment, which will not be repeated here.

[0157] Furthermore, an embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, which, when executed, enable at least one processor to perform a frequency hopping signal processing method such as in the above-described embodiment.

[0158] It should be noted that the computer storage medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate or transmit a program configured to be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0159] In addition, it will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0160] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0161] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

Claims

1. A frequency hopping signal processing method, characterized in that: include: Get spectrum data; Performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; Determine frequency point values ​​in the target spectrum data, and perform a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value; When the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set, wherein the second process includes clustering process; When the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set, the third process including a clustering process, and the third process is different from the clustering process corresponding to the second process; The quantity value of the currently accumulated spectrum data is determined, and when the quantity value of the spectrum data reaches a preset value, all third frequency point sets are counted to obtain a target frequency point set.

2. The frequency hopping signal processing method according to claim 1, characterized in that: The method further comprises: Acquire residence time data corresponding to the spectrum data; The dwell time data is screened based on the target frequency point set to obtain a target dwell time.

3. The frequency hopping signal processing method according to claim 1, characterized in that: When the frequency spectrum data is subjected to fast Fourier transform processing, the process includes: It is determined whether the PointNum calculated this time is the same as the PointNum calculated when the spectrum data was fast Fourier transformed last time. If they are the same, the rotation factor of this time is determined based on the rotation factor calculated last time.

4. The frequency hopping signal processing method according to claim 1, characterized in that: The method further comprises: The spectrum data acquired for the first time is set as initial data, and its spectrum value is determined as the initial value; When the spectrum value of the target spectrum data is greater than the value of the current maximum held spectrum, the maximum held spectrum is updated based on the current target spectrum data.

5. The frequency hopping signal processing method according to claim 1, characterized in that: The determining of frequency point values ​​in the target spectrum data and performing a first process on the frequency point values ​​to determine a first frequency point set greater than a threshold value includes: Determining frequency point values ​​in the target spectrum data; Dividing the frequency point value to obtain a plurality of sub-frequency point sets, each of the sub-frequency point sets having the same number of frequency points; Performing mean processing on each of the sub-frequency point sets to obtain a first mean value; Performing mean processing on the frequency points in the sub-frequency point set that are smaller than the first average value to obtain a second average value; constructing the threshold value based on the second average value; The frequency point values ​​in the target spectrum data are screened based on the threshold value to obtain the first frequency point set.

6. The frequency hopping signal processing method according to claim 1, characterized in that: When the target spectrum data is the first spectrum data, performing a second process on the first frequency point set to obtain a second frequency point set includes: When the target spectrum data is the first spectrum data, the difference between the previous frequency point value and the next frequency point value that satisfy the time sequence relationship in the first frequency point set is calculated in sequence; If the difference satisfies a preset condition, it indicates that the two frequency point values ​​can be clustered; Calculate the average value of the two frequency point values ​​that can be clustered, and use the average value as the reference frequency point; All frequency points in the first frequency point set are traversed to obtain a second frequency point set consisting of a plurality of the reference frequency points.

7. The frequency hopping signal processing method according to claim 1, characterized in that: When the target spectrum data is the nth spectrum data, n is not 1, performing a third process on the second frequency point set to obtain a third frequency point set includes: When the target spectrum data is the nth spectrum data, n is not 1, performing statistics on the second frequency point set to determine a frequency point error; Screening the second frequency point set based on the frequency point error, and considering the frequency points whose frequency point errors satisfy a preset condition as the same frequency points; Calculate and determine the average value of the same frequency point as a new frequency point value, and update the second frequency point set; Counting the number of occurrences of each frequency point in the updated second frequency point set, and determining a valid frequency point based on the number of occurrences; The valid frequency points are counted to form the third frequency point set.

8. The frequency hopping signal processing method according to claim 8, characterized in that: The counting of the number of occurrences of each frequency point in the second frequency point set after the update, and determining the valid frequency point based on the number of occurrences includes: Determine threshold range; The number of occurrences of each frequency point in the updated second frequency point set is counted, and the frequency point whose number is within the threshold range is determined as the valid frequency point.

9. A frequency hopping signal processing device, characterized in that: include: A first acquisition module, used for acquiring spectrum data; A transformation module, used for performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; A first processing module, used for determining frequency point values ​​in the target spectrum data, and performing a first processing on the frequency point values ​​to determine a first frequency point set greater than a threshold value; A second processing module, configured to perform a second processing on the first frequency point set to obtain a second frequency point set when the target spectrum data is the first spectrum data, wherein the second processing includes clustering processing; a third processing module, configured to, when the target spectrum data is the nth spectrum data and n is not 1, perform a third processing on the second frequency point set to obtain a third frequency point set, wherein the third processing includes a clustering processing, and the third processing is different from the clustering processing corresponding to the second processing; The statistical module is used to determine the quantity value of the currently accumulated spectrum data, and when the quantity value of the spectrum data reaches a preset value, all third frequency point sets are counted to obtain a target frequency point set.

10. An electronic device, characterized in that: include: one or more processors; a memory configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the frequency hopping signal processing method according to any one of claims 1 to 8.

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