A frequency hopping signal processing method, device and equipment

Abstract

The application provides a frequency hopping signal processing method, device and equipment, the method comprising: obtaining spectrum data; performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; determining frequency point values in the target spectrum data and performing first processing on the frequency point values to determine a first frequency point set greater than a threshold value; when the target spectrum data is a first spectrum data, performing second processing on the first frequency point set to obtain a second frequency point set, the second processing comprising clustering processing; when the target spectrum data is an nth spectrum data, n not being 1, performing third processing on the second frequency point set to obtain a third frequency point set, the third processing comprising clustering processing, the clustering processing corresponding to the second processing being different; determining a quantity value of the spectrum data accumulated at present, and when the quantity value of the spectrum data reaches a preset value, counting all the third frequency point sets to obtain a target frequency point set. The method of the application is used for accurately analyzing and identifying frequency modulation signals.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of signal measurement, and particularly relates to a frequency hopping signal processing method, device and equipment. BACKGROUND

[0002] Frequency hopping communication refers to a communication mode in which the carrier of a transmission signal constantly hops under the control of a pseudo-random code. The frequency hopping communication is widely applied due to its unique advantages such as strong anti-interference capability, anti-fading, easy networking, low interception probability, security, etc. The test of the related indexes of the frequency hopping communication is a subject that has been studied by the academic circle. Generally, the test indexes of the frequency hopping communication include the frequency hopping rate, the dwell time and the frequency hopping frequency set.

[0003] At present, there are many algorithms for the test indexes of the frequency hopping communication. However, these algorithms have high complexity, which leads to great difficulty in implementation, or the precision is not enough, which leads to the result of estimating the frequency hopping rate, the dwell time and the frequency hopping frequency set not being accurate enough. SUMMARY

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

[0005] The content of the present application includes providing a frequency hopping signal processing method, comprising:

[0006] obtaining spectrum data;

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

[0008] determining a frequency point value in the target spectrum data, and performing first processing on the frequency point value to determine a first frequency point set greater than a threshold value;

[0009] when the target spectrum data is a first spectrum data, performing second processing on the first frequency point set to obtain a second frequency point set, the second processing including clustering processing;

[0010] when the target spectrum data is an n-th spectrum data, n is not 1, performing third processing on the second frequency point set to obtain a third frequency point set, the third processing including clustering processing, and the clustering processing corresponding to the third processing is different from the clustering processing corresponding to the second processing;

[0011] determining a quantity value of the spectrum data accumulated at present, and when the quantity value of the spectrum data reaches a preset value, counting all the third frequency point sets to obtain a target frequency point set.

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

[0013] obtaining dwell time data corresponding to the spectrum data;

[0014] filtering the residence time data based on the target frequency set, to obtain target residence time.

[0015] In some embodiments, when performing the fast Fourier transform on the spectrum data, the method comprises:

[0016] determining whether the PointNum obtained in the current calculation is the same as the PointNum obtained when performing the fast Fourier transform on the previous spectrum data, and if so, determining the rotation factor in the current calculation based on the rotation factor obtained in the previous calculation.

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

[0018] setting the first acquired spectrum data as initial data, and determining the spectrum value thereof as an initial value;

[0019] when the spectrum value of the target spectrum data is greater than the value of the current maximum maintained spectrum, updating the maximum maintained spectrum based on the current target spectrum data.

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

[0021] determining the frequency point value in the target spectrum data;

[0022] dividing the frequency point value to obtain a plurality of sub-frequency point sets, the number of frequency points in each sub-frequency point set being the same;

[0023] performing mean value processing on each sub-frequency point set to obtain a first average value;

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

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

[0026] filtering the frequency point value in the target spectrum data 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, the method further comprises:

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

[0029] If the difference satisfies a preset condition, it is indicated that the two frequency point values can be clustered.

[0030] An average value of the two frequency point values that can be clustered is calculated, and the average value is used as a reference frequency point.

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

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

[0033] When the target frequency spectrum data is the nth frequency spectrum data, n is not 1, a frequency point error is determined by performing a statistics on the second frequency point set.

[0034] Based on the frequency point error, the second frequency point set is screened, and a frequency point satisfying a preset condition is regarded as a same frequency point.

[0035] An average value of the same frequency point is calculated as a new frequency point value, and the second frequency point set is updated.

[0036] The number of times of appearance of each frequency point in the updated second frequency point set is counted, and an effective frequency point is determined based on the number of times.

[0037] The effective frequency point is counted to form the third frequency point set.

[0038] In some embodiments, the counting of the number of times of appearance of each frequency point in the updated second frequency point set and the determination of the effective frequency point based on the number of times include:

[0039] A threshold range is determined.

[0040] The number of times of appearance of each frequency point in the updated second frequency point set is counted, and a frequency point with a number of times within the threshold range is determined as the effective frequency point.

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

[0042] An acquisition module is configured to acquire frequency spectrum data.

[0043] A transformation module is configured to perform fast Fourier transform processing on the frequency spectrum data to obtain target frequency spectrum data.

[0044] A first processing module is configured to determine frequency point values in the target frequency spectrum data, and perform first processing on the frequency point values to determine a first frequency point set greater than a threshold value.

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

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

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

[0048] Another embodiment of the present application 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 according to any one of the above embodiments.

[0052] The present application has the advantages that accurate information about the frequency hopping signal can be obtained by using the fast Fourier transform processing, the frequency point value processing, the different clustering processing in different scenarios, and the statistical processing of the target frequency point, the process is simple and has low complexity, the memory space and the operation time are saved, the operation efficiency is improved, the accuracy is high, the error is low, and accurate estimation results can be obtained for the test indicators, that is, the frequency hopping point number, the dwell time, and the frequency hopping frequency set can be accurately measured.

[0053] In addition, the fast Fourier transform algorithm is optimized in the present application, the memory space and the operation time are further saved, the frequency point set is processed by the maximum preserving spectrum and the multiple clustering processing, the frequency point values are screened in combination with the preconfigured thresholds, the error is further reduced, and the accuracy is improved.

[0054] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be learned from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0055] The technical solutions of the present application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:

[0057] Figure 1 A flowchart of a frequency hopping signal processing method in an embodiment of the application.

[0058] Figure 2 A flowchart of a frequency hopping signal processing method in an embodiment of the application.

[0059] Figure 3 A flowchart of a frequency hopping signal processing method in another embodiment of the application.

[0060] Figure 4 A block diagram of a frequency hopping signal processing apparatus in an embodiment of the application. DETAILED DESCRIPTION

[0061] In the following, specific embodiments of the application will be described in detail with reference to the accompanying drawings, which are intended to explain the present application rather than to limit the same. The following embodiments are presented by way of non-limiting examples, and the scope of the application is not limited to them.

[0062] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the description should not be construed as limiting, but merely as exemplification of the embodiments. Those skilled in the art will envision other modifications within the scope of the present disclosure.

[0063] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:

[0064] These and other characteristics, features and advantages of the present application will become apparent from the following description, given, by way of non-limiting example only, with reference to the accompanying drawings.

[0065] It is also to be understood that the application is not limited to the details of the above-described embodiments but can be implemented with various other equivalent arrangements. The application is therefore not limited to the details of the above-described embodiments, but can be implemented with various other equivalent arrangements.

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

[0067] Specific embodiments of the present disclosure are described herein with reference to the accompanying drawings. However, it should be understood that the disclosed embodiments are merely examples of implementing the present disclosure and can be implemented in numerous ways. Well-known and / or repetitive functions and structures are not described in detail to avoid obscuring the present disclosure unnecessarily. Therefore, specific structural and functional details disclosed herein are not intended to limit, but merely as a basis for the claims and a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriate detailed structure.

[0068] The specification can use phrases such as "in one embodiment", "in another embodiment", "in yet another embodiment", or "in other embodiments", which can refer to one or more of the same or different embodiments according to the present disclosure.

[0069] In the following, embodiments of the present application are described in detail with reference to the accompanying drawings.

[0070] As shown in Figure 1 and Figure 2 Embodiments of the present application provide a frequency hopping signal processing method, characterized in that comprising:

[0071] S1: obtaining spectrum data;

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

[0073] S3: determining a frequency point value in the target spectrum data, and performing first processing on the frequency point value 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 second processing on the first frequency point set to obtain a second frequency point set, the second processing comprising clustering processing;

[0075] S5: when the target spectrum data is the nth spectrum data, n is not 1, performing third processing on the second frequency point set to obtain a third frequency point set, the third processing comprising clustering processing, the third processing being different from the corresponding clustering processing of the second processing;

[0076] S6: determining a current cumulative spectrum data quantity value, and when the quantity value of the spectrum data reaches a preset value, counting all third frequency point sets to obtain a target frequency point set.

[0077] The beneficial effects of the embodiment are that accurate information about the frequency hopping signal can be obtained by using fast Fourier transform processing, frequency point value processing, different clustering processing in different scenarios, and target frequency point statistical processing. This process not only has a simple flow and low complexity, saves memory space and operation time, and improves operation efficiency, but also has high accuracy and low error, and can obtain accurate estimation results for test indicators, that is, the frequency hopping point number, the dwell time, and the frequency hopping frequency set can be accurately measured.

[0078] For example, the method of the embodiment can be applied to a signal acquisition and identification device, which includes an FPGA, a DSP, and a host computer. The host computer issues parameters such as a signal type (e.g., a frequency hopping signal), a frequency (e.g., 1.2 GHz), and a bandwidth (e.g., 10 Mhz) to the DSP, which then forwards them to the FPGA. The FPGA collects signals through adc and filters them, and then sends data packets containing frequency hopping information to the DSP through srio. After the DSP calculates, it is passed to the host computer, which evaluates the results of different test indicators in combination with the obtained frequency hopping data.

[0079] In the following embodiment, the method is applied to the above-mentioned signal acquisition and identification device as an example. The schemes described in the embodiment are executed by the DSP. When executing step S1, the DSP can select a target time period of 1s. In this period, the FPGA will generate 1000 data packets, each containing a dwell time and 4096 frequency points. A data packet is sent once every 1ms, so the DSP needs to run the frequency hopping algorithm within 1ms.

[0080] Specifically, as shown in Figure 3 The method further includes:

[0081] S7: Obtain dwell time data corresponding to the frequency spectrum data.

[0082] S8: Filter 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. When executing, the dwell time can be filtered according to the number of frequency points in the target frequency point set obtained in the previous step, so that its value remains within a reasonable range, and the corresponding result is retained. The obtained dwell time and the previously obtained target frequency point set are uploaded to the host computer together. After the host computer obtains the uploaded dwell time and target frequency point set, it can calculate the estimation value of the test indicators for frequency hopping communication, and obtain the test conclusion, significantly improving the test efficiency.

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

[0085] S9: judging whether the PointNum obtained in the current calculation is same as the PointNum obtained when performing the fast Fourier transform on the previous spectrum data, if same, determining the rotation factor in the current calculation based on the rotation factor obtained in the previous calculation.

[0086] For example, it is assumed that the number of frequency points is 4096 points, wherein the fft (fast Fourier transform) uses assembly language to process a plurality of frequency points to obtain a new spectrum. In order to improve the processing efficiency, the fft of the DSP is optimized in the embodiment, and the specific optimization items include: (1) integrating the calculation part of the rotation factor into the algorithm implementation part of the DSP; (2) since the time consumed for calculating the rotation factor is relatively long, a judgment is added in the function, and the judgment includes that if the PointNum obtained in the previous calculation is same as the PointNum obtained in the current calculation, the rotation factor obtained in the previous calculation can be directly used to save the rotation factor calculation link, and the previous calculation is the fast Fourier transform performed on the spectrum data by the DSP; (3) the space occupied by the rotation factor is proportional to the PointNum, in order to improve the utilization rate of the memory space, the malloc function is used to dynamically apply the memory for the rotation factor in the embodiment.

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

[0088] S10: setting the spectrum data obtained for the first time as initial data, and determining the spectrum value thereof as an initial value;

[0089] S11: if the spectrum value of the target spectrum data is greater than the value of the current maximum maintained spectrum, updating the maximum maintained spectrum based on the current target spectrum data.

[0090] The method in the embodiment is to update the maximum maintained spectrum, and specifically includes blackening the spectrum value of the 4096 points obtained for the first time, and determining the spectrum value as a target frequency point set, and the target frequency point set is configured as an initial value set. Subsequently, every time a new spectrum data is obtained, two-by-two comparison is performed according to the index, and the larger value is retained and updated into the initial set. That is, every time a new spectrum data is obtained, the larger value is compared with the maximum spectrum value recorded at present, and the larger value is overwritten in the initial set to update the new maximum spectrum.

[0091] Further, the determination of the frequency point value in the target spectrum data and the first processing of the frequency point value to determine the first frequency point set greater than the threshold value includes:

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

[0093] S13: divide the frequency point values to obtain a plurality of sub-frequency point sets, the number of frequency points in each of the sub-frequency point sets being the same;

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

[0095] S15: performing mean value processing on the frequency points in the sub-frequency point set that are less 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: performing screening on the frequency point values in the target frequency spectrum data based on the threshold value to obtain the first frequency point set.

[0098] For example, assuming that the target frequency point set has 4096 frequency points, then the 4096 frequency points are equally divided into 64 parts in the order of time sequence, each part having 64 points, and then the frequency point values are extracted from each part, the extraction method being that the 64 points are first averaged to obtain avg1, and then the frequency points less than avg1 are screened to obtain avg2. The threshold value is avg2+bias (bias can be configured by an upper computer), and the bias can be, but is not limited to, 10, etc. The frequency point set 1, i.e., the first frequency point set, is obtained by extracting all points greater than the threshold value.

[0099] In an embodiment, when the target frequency spectrum data is the first frequency spectrum data, the first frequency point set is subjected to a second processing to obtain a second frequency point set, the second processing including:

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

[0101] S19: if the difference satisfies a preset condition, then the two frequency point values can be clustered;

[0102] S20: calculating an average value of the two frequency point values that can be clustered, and taking the average value as a reference frequency point;

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

[0104] For example, when the obtained spectrum is the first one, it is not clustered with other spectra. At this time, the method adopted by the embodiment is to take the frequency point set 1, i.e., the first frequency point set, as the analysis object, compare the difference between the last point and the previous point, i.e., the difference between two adjacent frequency points satisfying the time sequence, such as comparing the difference between the first frequency point and the 0th frequency point, then comparing the difference between the second frequency point and the first frequency point, and so on. If the difference is less than a 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. When the number of obtained frequency points is greater than the corresponding threshold value, the average value of the n frequency points is taken as a reference frequency point of the second frequency point set. This cycle is repeated until the analysis of all frequency points of the frequency point set 1 is completed, and the frequency point set 2, i.e., 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, the second frequency point set is subjected to a third processing to obtain a third frequency point set, including:

[0106] S22: When the target spectrum data is the nth spectrum data, n is not 1, the second frequency point set is subjected to a statistical processing to determine a frequency point error;

[0107] S23: Based on the frequency point error, the second frequency point set is subjected to a screening processing, and the frequency points satisfying a preset condition are regarded as the same frequency points;

[0108] S24: The average value of the same frequency points is calculated and determined as a new frequency point value, and the second frequency point set is updated;

[0109] S25: The number of times of appearance of each frequency point in the updated second frequency point set is counted, and an effective frequency point is determined based on the number of times;

[0110] S26: The effective frequency points form the third frequency point set.

[0111] The counting of the number of times of appearance of each frequency point in the updated second frequency point set and the determination of the effective frequency point based on the number of times include:

[0112] S27: A threshold range is determined;

[0113] S28: The number of times of appearance of each frequency point in the updated second frequency point set is counted, and the frequency points with the number of times within the threshold range are determined as the effective frequency points.

[0114] For example, when the spectrum is not the first spectrum, the current spectrum and the last spectrum need to be clustered, and the method is specifically that, the N frequency point sets 2 (second frequency point set) output by the N spectra are counted, for example, 4096 frequency point sets 2 output by 1000 spectra are counted, the frequency point error between the frequency point sets is calculated, the error is compared with the corresponding threshold value Thre (the Thre value can be configured by the host computer), and the frequency points with the frequency point error less than the threshold value Thre are regarded as the same frequency points, the average value of the frequency points is taken as a new frequency point value, the number of occurrences of each new frequency point value is counted, and 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, the M1 and M2 form a threshold value, wherein M1 and M2 can be set to 30 and 50. By modifying the threshold value, the effective frequency point number can be continuously approximated to the real value. Further, if the number of occurrences is within the threshold value, the frequency points are regarded as effective frequency points, and the set of all effective frequency points is the final frequency point set, that is, the third frequency point set.

[0115] For step S6, the number of spectra can be set to 1000 by default, and when the number of spectra reaches 1000, the number of frequency point sets is counted. If the number meets the requirements, it is uploaded to the host computer, and if it does not meet the requirements, the foregoing steps are continuously executed until the number of frequency points meets the requirements. For the obtained third frequency point set, the DSP uploads it to the host computer together with the residence time data, and the specific identification result is displayed by the host computer.

[0116] As shown in Figure 4 Another embodiment of the application is a frequency hopping signal processing device 100, which comprises:

[0117] A first acquisition module is configured to acquire spectrum data.

[0118] A transform module is configured to perform fast Fourier transform processing on the spectrum data to obtain target spectrum data.

[0119] A first processing module is configured to determine frequency point values in the target spectrum data, and perform first processing on the frequency point values to determine a first frequency point set greater than a threshold value.

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

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

[0122] a statistics module configured to determine a current accumulated number of spectrum data, and determine all third frequency point sets when the number of spectrum data reaches a preset value, to obtain a target frequency point set.

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

[0124] a second acquisition module configured to acquire residence time data corresponding to the spectrum data;

[0125] a screening module configured to screen the residence time data according to the target frequency point set, to obtain a target residence time.

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

[0127] determining whether the PointNum obtained in the current calculation is the same as the PointNum obtained when performing fast Fourier transform on the previous spectrum data, and if so, determining the rotation factor in the current calculation based on the rotation factor obtained in the previous calculation.

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

[0129] a setting module configured to set the first-acquired spectrum data as initial data, and determine the spectrum value of the initial data as an initial value;

[0130] an updating module configured to update the maximum maintained 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 maintained spectrum.

[0131] In some embodiments, the method of determining the frequency point value in the target spectrum data and performing first processing on the frequency point value to determine a first frequency point set greater than a threshold value comprises:

[0132] determining the frequency point value in the target spectrum data;

[0133] dividing the frequency point value to obtain a plurality of sub-frequency point sets, and the number of frequency points in each sub-frequency point set is the same;

[0134] performing mean value processing on each sub-frequency point set to obtain a first average value;

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

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

[0137] screening the frequency point value in the target spectrum data 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, the second processing on the first frequency point set is performed to obtain a second frequency point set, including:

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

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

[0141] An average value of the two frequency point values that can be clustered is calculated, and the average value is taken as a reference frequency point;

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

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

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

[0145] Based on the frequency point error, the second frequency point set is screened, and a frequency point whose frequency point error satisfies a preset condition is regarded as a same frequency point;

[0146] An average value of the same frequency point is calculated as a new frequency point value, and the second frequency point set is updated;

[0147] The number of times of appearance of each frequency point in the updated second frequency point set is counted, and an effective frequency point is determined based on the number of times;

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

[0149] In some embodiments, the counting of the number of times of appearance of each frequency point in the updated second frequency point set and the determination of the effective frequency point based on the number of times include:

[0150] A threshold range is determined;

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

[0152] Another embodiment of the present application further provides an electronic device, including:

[0153] One or more processors;

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

[0155] The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the frequency hopping signal processing method according to any one of the preceding embodiments.

[0156] Further, an embodiment of the present application also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the frequency hopping signal processing method according to the preceding embodiments. It should be understood that each of the schemes in the present embodiment has the corresponding technical effects in the method embodiments described above, which will not be repeated here.

[0157] Further, an embodiment of the present application also provides a computer program product tangibly stored on a computer readable medium and comprising computer readable instructions, which, when executed, cause at least one processor to perform the frequency hopping signal processing method according to the preceding embodiments.

[0158] It should be noted that the computer storage medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable medium may, for example, but not limited to, be 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 can include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fibers, portable compact disk read only memory (CD-ROM), optical storage media, magnetic storage media, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which 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 can be transmitted by any suitable medium, including but not limited to wireless, wire, cable, RF, etc., or any suitable combination of the above.

[0159] Those skilled in the art will appreciate that the embodiments of the present application can be further implemented in any desired computer system, such as a computer system 200 as shown in FIG. 2. Such computer system 200 includes one or more processors, such as a processor 204. The processor 204 is connected to a communication infrastructure 206, such as a bus or network. The computer system 200 also includes a main memory 208, e.g., random access memory (RAM), and can include a secondary memory 210, e.g., one or more disk drives or tape drives. The computer system 200 typically includes a variety of computer readable media, such as computer storage media 212 and communication media 214. Computer storage media 212 includes RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by the computer system 200. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery

[0160] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of 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, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions of the flowchart and / or the block diagram block or blocks. These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including an instruction system to implement the flowchart and / or the block diagram block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 one or more functions specified by one or more blocks

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions of the flowchart and / or the block diagram block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 one or more functions specified by one or more blocks

[0162] Those skilled in the art will appreciate that the discussion is only by way of example and is not intended to limit the scope of the application. The principles, and uses of the technologies described can be employed in various arrangements and scenarios within the scope of the application. Any and all embodiments of the present application can be implemented or realized in a special purpose computer, a general purpose computer, an embedded computer, a computer network, or an embedded computer network comprising one or more computers. Some embodiments of the present application can be realized as a high level computer language, instructions set, or object oriented programming language that can be executed in a computer or network computing environment to manipulate the basic computer architecture components of a computer or computer network.

Claims

1. A frequency hopping signal processing method, characterized by, The method comprises: obtaining spectrum data; performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; determining frequency point values in the target spectrum data, and performing first processing on the frequency point values to determine a first frequency point set greater than a threshold value; when the target spectrum data is a first spectrum data, performing second processing on the first frequency point set to obtain a second frequency point set, the second processing comprising clustering processing; when the target spectrum data is an n-th spectrum data, n not being 1, performing third processing on the second frequency point set to obtain a third frequency point set, the third processing comprising clustering processing, the third processing being different from the clustering processing corresponding to the second processing; determining a number value of currently accumulated spectrum data, and when the number value of the spectrum data reaches a preset value, counting all third frequency point sets to obtain a target frequency point set; when the target spectrum data is the first spectrum data, the second processing on the first frequency point set to obtain the second frequency point set comprises: when the target spectrum data is the first spectrum data, calculating, in sequence, a difference value between a previous frequency point value and a subsequent frequency point value in the first frequency point set that satisfy a time sequence relationship; if the difference value satisfies a preset condition, it is indicated that the two frequency point values can be clustered; calculating an average value of the two frequency point values that can be clustered, and taking the average value as a reference frequency point; traversing all frequency points in the first frequency point set to obtain a second frequency point set composed of multiple reference frequency points; when the target spectrum data is the n-th spectrum data, n not being 1, the third processing on the second frequency point set to obtain the third frequency point set comprises: when the target spectrum data is the n-th spectrum data, n not being 1, performing statistics on the second frequency point set to determine a frequency point error; based on the frequency point error, screening the second frequency point set, and regarding a frequency point whose frequency point error satisfies a preset condition as a same frequency point; calculating an average value of the same frequency points as a new frequency point value, and updating the second frequency point set; counting the number of times each frequency point appears in the updated second frequency point set, and determining an effective frequency point based on the number of times; counting the effective frequency points to form the third frequency point set.

2. The frequency hopping signal processing method of claim 1, wherein, The method further comprises: obtaining residence time data corresponding to the spectrum data; based on the target frequency point set, screening the residence time data to obtain target residence time.

3. The frequency hopping signal processing method of claim 1, wherein, When performing fast Fourier transform processing on the spectrum data, it comprises: determining whether PointNum obtained in the current calculation is the same as PointNum obtained when performing fast Fourier transform on the previous spectrum data, if the same, determining a rotation factor in the current calculation based on the rotation factor obtained in the previous calculation.

4. The frequency hopping signal processing method of claim 1, wherein, The method further comprises: setting the first obtained spectrum data as initial data, and determining a spectrum value thereof as an initial value; when the spectrum value of the target spectrum data is greater than a value of a current maximum holding spectrum, updating the maximum holding spectrum based on the current target spectrum data.

5. The frequency hopping signal processing method of claim 1, wherein, The determination of the frequency point values in the target spectrum data and the first processing on the frequency point values to determine the first frequency point set greater than the threshold value comprises: Determine the frequency point value in the target spectrum data; Divide the frequency point value to obtain a plurality of sub-frequency point sets, and the number of frequency points in each sub-frequency point set is the same; Perform mean value processing on each sub-frequency point set to obtain a first average value; Perform mean value processing on the frequency points in the sub-frequency point set that are less than the first average value to obtain a second average value; Construct the threshold value based on the second average value; Screen the frequency point value in the target spectrum data based on the threshold value to obtain the first frequency point set.

6. The frequency hopping signal processing method of claim 1, wherein, The number of occurrences of each frequency point in the updated second frequency point set is counted, and the effective frequency point is determined based on the number of occurrences, comprising: Determine a threshold range; Count the number of occurrences of each frequency point in the updated second frequency point set, and determine the frequency point whose number of occurrences is within the threshold range as the effective frequency point.

7. A frequency hopping signal processing apparatus characterized by comprising: Comprise: The first acquisition module is used for acquiring spectrum data; The transformation module is used for performing fast Fourier transform processing on the spectrum data to obtain target spectrum data; The first processing module is used for determining the frequency point value in the target spectrum data, and performing first processing on the frequency point value to determine the first frequency point set greater than the threshold value; The second processing module is used for performing second processing on the first frequency point set when the target spectrum data is the first spectrum data to obtain the second frequency point set, and the second processing comprises clustering processing; The third processing module is used for performing third processing on the second frequency point set when the target spectrum data is the nth spectrum data, n is not 1, to obtain the third frequency point set, and the third processing comprises clustering processing, and the clustering processing corresponding to the third processing is different from the clustering processing corresponding to the second processing; The statistical module is used for determining the number value of the currently accumulated spectrum data, and counting all third frequency point sets to obtain the target frequency point set when the number value of the spectrum data reaches a preset value; When the target spectrum data is the first spectrum data, the difference between the previous frequency point value and the next frequency point value in the first frequency point set that satisfies the time sequence relationship is calculated in sequence; If the difference satisfies a preset condition, it indicates that the two frequency point values can be clustered; The average value of the two frequency point values that can be clustered is calculated, and the average value is taken as a reference frequency point; All frequency points in the first frequency point set are traversed to obtain a second frequency point set composed of a plurality of reference frequency points; When the target spectrum data is the nth spectrum data, n is not 1, the second frequency point set is screened based on the frequency point error, and the frequency point whose frequency point error satisfies a preset condition is regarded as the same frequency point; The average value of the same frequency point is calculated as a new frequency point value, and the second frequency point set is updated; The number of occurrences of each frequency point in the updated second frequency point set is counted, and the effective frequency point is determined based on the number of occurrences; ​ ​ ​ The effective frequency points form the third frequency point set.

8. An electronic device, comprising: The method comprises: 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 realize the frequency hopping signal processing method in any one of claims 1-6.

Images