Chaotic frequency hopping pattern generation quantization method, device, equipment and storage medium

Abstract

The application discloses a chaotic frequency hopping pattern generation quantization method, device and equipment and a storage medium. The method collects chaotic sequence samples offline and sorts them, obtains a quantization threshold value based on uniform segmentation calculation, generates chaotic sequences online, compares the chaotic sequences with the quantization threshold value, and obtains a frequency hopping frequency point sequence. The frequency hopping frequency point sequence is input into a wide interval processing unit to generate a final frequency hopping pattern. The randomicity and long-periodicity characteristics of the chaotic sequence can be effectively maintained, potential periodicity distribution is broken, and a system is prevented from staying in an easily disturbed frequency point for a long time. Finally, a frequency hopping pattern with high uniformity, strong randomicity and excellent anti-interference capability is generated, comprehensive requirements of a high-speed frequency hopping system on real-time performance, uniform distribution and anti-blocking interference are met, the uniformity and long periodicity of the frequency hopping pattern after quantization are ensured on the premise that the randomicity of the chaotic sequence is not damaged, and the speed and efficiency of chaotic frequency hopping pattern generation quantization are improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus, device, and storage medium for generating and quantizing chaotic frequency hopping patterns. Background Technology

[0002] High-speed frequency hopping technology is one of the important means of anti-interference in current data link systems. The frequency hopping pattern, which is a sequence of frequency points for each hop, determines the operating frequency and variation pattern of the frequency-hopping signal. On the one hand, to ensure good anti-interference and low detectability of the frequency-hopping system, the frequency hopping pattern needs to have strong randomness and uniformity while also having a long period. On the other hand, for frequency division multiple access (FDMA) frequency hopping systems, to ensure that multiple nodes within the system or different systems do not interfere with each other, different frequency hopping patterns must be orthogonal (or quasi-orthogonal) pairwise. Therefore, frequency hopping pattern generation technology is one of the core technologies of frequency hopping systems and is of great significance to the performance of the frequency hopping system.

[0003] Due to the high randomness and susceptibility to initial values ​​of chaotic systems, frequency hopping patterns generated based on chaotic mapping simultaneously possess strong randomness, long periods, a large number of usable frequency hopping patterns, and quasi-orthogonality between each pair of patterns. Therefore, the frequency hopping pattern generation scheme based on chaotic mapping is currently the most advanced technical solution in the industry.

[0004] The flowchart of the frequency hopping pattern generation scheme based on chaotic mapping is as follows: Figure 1 As shown, Figure 1 For a flowchart of the existing frequency hopping pattern generation process based on chaotic mapping, see [link / reference]. Figure 1 The quantization module maps the chaotic sequence to each frequency hopping point. Therefore, the design of the quantization module directly determines the distribution characteristics of the frequency hopping pattern. Generally, without destroying the randomness of the sequence, the higher the uniformity of the frequency hopping pattern distribution, the better the anti-interference performance (anti-blocking interference, anti-scanning interference) of the frequency hopping system.

[0005] Quantization schemes can be divided into uniform quantization and non-uniform quantization. Uniform quantization and related improved schemes have the advantages of simple calculation and convenient modification of the number of mapped frequency points, but the uniformity of the frequency hopping pattern generated is poor. Therefore, non-uniform quantization schemes are currently more commonly used. Commonly used non-uniform quantization schemes include the queue sorting method and the cosine mapping method. Although the queue sorting method produces excellent uniformity of the frequency hopping pattern, it has high computational complexity, slow generation speed, and destroys the randomness of long-period patterns, so its practicality is low. Although the frequency hopping pattern generated by the cosine mapping method has strong uniformity and long periodicity, it is more suitable for logistic chaotic mapping systems. When used in other chaotic mapping systems, the uniformity of the frequency hopping pattern generated will decrease to some extent. Summary of the Invention

[0006] The main objective of this invention is to provide a chaotic frequency hopping pattern generation quantization method, apparatus, device, and storage medium, aiming to solve the technical problems in the prior art where existing frequency hopping pattern quantization schemes suffer from poor uniformity due to uniform quantization, queue sorting methods destroy randomness and have high computational complexity, and cosine mapping methods are only applicable to specific chaotic mapping sequences, failing to simultaneously meet the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution of frequency hopping patterns, and anti-interference capabilities.

[0007] Offline collection and sorting of chaotic sequence samples, and calculation of quantization threshold values ​​based on uniform segmentation;

[0008] A chaotic sequence is generated online, and the chaotic sequence is compared with the quantization threshold value to obtain a frequency hopping sequence;

[0009] The frequency hopping sequence is input into the wide-interval processing unit to generate the final frequency hopping pattern.

[0010] Optionally, the offline collection and sorting of chaotic sequence samples, and the calculation of the quantization threshold based on uniform segmentation, includes:

[0011] Offline collection of chaotic sequence sample data and numerical sorting to generate ordered sequences;

[0012] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the divided sequence segment is calculated.

[0013] Optionally, the offline acquisition of chaotic sequence sample data and numerical sorting to generate an ordered sequence includes:

[0014] The chaotic sequence generated based on the input system parameters and the initial seed, with an offline screenshot length of L, is sorted from smallest to largest to generate an ordered sequence.

[0015] Optionally, the step of uniformly dividing the ordered sequence and calculating the quantization threshold value corresponding to the divided equally segmented sequence includes:

[0016] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the equally divided sequence segment is calculated using the following formula:

[0017]

[0018]

[0019]

[0020] in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The ordered sequence obtained after sorting To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number .

[0021] Optionally, the step of generating a chaotic sequence online and comparing the chaotic sequence with the quantization threshold to obtain a frequency hopping sequence includes:

[0022] Chaotic sequences are generated online in real time using the chaos mapping module;

[0023] Each element in the chaotic sequence is compared with the quantization threshold value element by element to obtain the frequency hopping sequence.

[0024] Optionally, the step of comparing each element in the chaotic sequence with the quantization threshold value element by element to obtain the frequency hopping sequence includes:

[0025] Each element in the chaotic sequence is compared element-by-element with the quantization threshold value, and the frequency value corresponding to each element is determined by the following formula based on the frequency intervals divided by the quantization threshold value:

[0026]

[0027] in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value;

[0028] The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

[0029] Optionally, the step of inputting the frequency hopping sequence into the wide-spacing processing unit to generate the final frequency hopping pattern includes:

[0030] The frequency hopping sequence is input to the wide-interval processing unit, which detects the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping sequence in real time, dynamically inserts additional frequency hopping intervals, and generates the final frequency hopping pattern.

[0031] Secondly, to achieve the above objectives, the present invention also proposes a chaotic frequency hopping pattern generation and quantization device, the chaotic frequency hopping pattern generation and quantization device comprising:

[0032] The threshold calculation module is used to collect and sort chaotic sequence samples offline, and calculate the quantized threshold value based on uniform segmentation.

[0033] The frequency point sequence acquisition module is used to generate a chaotic sequence online and compare the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence.

[0034] The frequency hopping pattern generation module is used to input the frequency hopping frequency sequence into the wide-interval processing unit to generate the final frequency hopping pattern.

[0035] Thirdly, to achieve the above objectives, the present invention also proposes a chaotic frequency hopping pattern generation and quantization device, the chaotic frequency hopping pattern generation and quantization device comprising: a memory, a processor, and a chaotic frequency hopping pattern generation and quantization program stored in the memory and executable on the processor, the chaotic frequency hopping pattern generation and quantization program being configured to implement the steps of the chaotic frequency hopping pattern generation and quantization method as described above.

[0036] Fourthly, to achieve the above objectives, the present invention also proposes a storage medium storing a chaotic frequency hopping pattern generation and quantization program, wherein when the chaotic frequency hopping pattern generation and quantization program is executed by a processor, it implements the steps of the chaotic frequency hopping pattern generation and quantization method described above.

[0037] The chaotic frequency hopping pattern generation and quantization method proposed in this invention involves offline acquisition and sorting of chaotic sequence samples, and obtaining a quantization threshold value based on uniform segmentation calculation; online generation of chaotic sequences, and comparison of the chaotic sequences with the quantization threshold value to obtain a frequency hopping point sequence; inputting the frequency hopping point sequence into a wide-interval processing unit to generate the final frequency hopping pattern. This method effectively maintains the randomness and long-period characteristics of the chaotic sequences, breaks the potential periodic distribution, avoids the system from lingering on interference-prone frequencies for extended periods, and ultimately generates a frequency hopping pattern with high uniformity, strong randomness, and excellent anti-interference capability. This meets the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution, and anti-blocking interference, and improves the speed and efficiency of chaotic frequency hopping pattern generation and quantization. Attached Figure Description

[0038] Figure 1 A flowchart for generating existing frequency hopping patterns based on chaotic mapping is provided.

[0039] Figure 2 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0040] Figure 3This is a flowchart illustrating the first embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention.

[0041] Figure 4 This is a flowchart illustrating the second embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention;

[0042] Figure 5 This is a flowchart illustrating the third embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention.

[0043] Figure 6 This is a flowchart illustrating the fourth embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention.

[0044] Figure 7 This is a block diagram of the frequency hopping pattern generator in the chaotic frequency hopping pattern generation and quantization method of the present invention;

[0045] Figure 8 This is a schematic diagram of the simulation curve of the balance parameter in the chaotic frequency hopping pattern generation quantization method of the present invention;

[0046] Figure 9 This is a functional block diagram of the first embodiment of the chaotic frequency hopping pattern generation and quantization device of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] The solution of this invention mainly involves: offline acquisition and sorting of chaotic sequence samples, and calculation of quantization threshold values ​​based on uniform segmentation; online generation of chaotic sequences, and comparison of the chaotic sequences with the quantization threshold values ​​to obtain frequency hopping sequence; inputting the frequency hopping sequence into a wide-interval processing unit to generate the final frequency hopping pattern. This effectively maintains the randomness and long-period characteristics of the chaotic sequences, breaks the potential periodic distribution, avoids the system from staying at frequency points susceptible to interference for a long time, and finally generates a frequency hopping pattern with high uniformity, strong randomness, and excellent anti-interference capability. This meets the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution, and anti-blocking interference, improves the speed and efficiency of chaotic frequency hopping pattern generation and quantization, and solves the technical problems in the prior art where existing frequency hopping pattern quantization schemes result in poor uniformity due to uniform quantization, queue sorting methods destroy randomness and have high computational complexity, and cosine mapping methods are only applicable to specific chaotic mapping sequences, failing to simultaneously meet the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution of frequency hopping patterns, and anti-interference capability.

[0050] Reference Figure 2 , Figure 2This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0051] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0052] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0053] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating device, a network communication module, a user interface module, and a chaotic frequency hopping pattern generation and quantization program.

[0054] The device of the present invention uses processor 1001 to call the chaotic frequency hopping pattern stored in memory 1005 to generate a quantization program, and performs the following operations:

[0055] Offline collection and sorting of chaotic sequence samples, and calculation of quantization threshold values ​​based on uniform segmentation;

[0056] A chaotic sequence is generated online, and the chaotic sequence is compared with the quantization threshold value to obtain a frequency hopping sequence;

[0057] The frequency hopping sequence is input into the wide-interval processing unit to generate the final frequency hopping pattern.

[0058] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0059] Offline collection of chaotic sequence sample data and numerical sorting to generate ordered sequences;

[0060] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the divided sequence segment is calculated.

[0061] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0062] The chaotic sequence generated based on the input system parameters and the initial seed, with an offline screenshot length of L, is sorted from smallest to largest to generate an ordered sequence.

[0063] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0064] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the equally divided sequence segment is calculated using the following formula:

[0065]

[0066]

[0067]

[0068] in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The ordered sequence obtained after sorting To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number .

[0069] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0070] Chaotic sequences are generated online in real time using the chaos mapping module;

[0071] Each element in the chaotic sequence is compared with the quantization threshold value element by element to obtain the frequency hopping sequence.

[0072] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0073] Each element in the chaotic sequence is compared element-by-element with the quantization threshold value, and the frequency value corresponding to each element is determined by the following formula based on the frequency intervals divided by the quantization threshold value:

[0074]

[0075] in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value;

[0076] The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

[0077] The device of the present invention generates a quantization program by calling the chaotic frequency hopping pattern stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0078] The frequency hopping sequence is input to the wide-interval processing unit, which detects the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping sequence in real time, dynamically inserts additional frequency hopping intervals, and generates the final frequency hopping pattern.

[0079] This embodiment, through the above-described scheme, collects and sorts chaotic sequence samples offline, calculates a quantization threshold value based on uniform segmentation, generates a chaotic sequence online, compares the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence, and inputs the frequency hopping sequence into a wide-interval processing unit to generate the final frequency hopping pattern. This effectively maintains the randomness and long-period characteristics of the chaotic sequence, breaks the potential periodic distribution, avoids the system from staying on easily interfered frequencies for a long time, and ultimately generates a frequency hopping pattern with high uniformity, strong randomness, and excellent anti-interference capability. This meets the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution, and anti-blocking interference, and improves the speed and efficiency of chaotic frequency hopping pattern generation and quantization.

[0080] Based on the above hardware structure, an embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention is proposed.

[0081] Reference Figure 3 , Figure 3 This is a flowchart illustrating the first embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention.

[0082] In the first embodiment, the chaotic frequency hopping pattern generation quantization method includes the following steps:

[0083] Step S10: Collect chaotic sequence samples offline and sort them, and calculate the quantization threshold value based on uniform segmentation.

[0084] It should be noted that during the offline processing stage, chaotic sequence sample data is collected and numerically sorted. Then, based on the principle of uniform segmentation, a quantization threshold value is obtained, which can provide a basic quantization basis for subsequent online frequency hopping pattern generation.

[0085] Step S20: Generate a chaotic sequence online and compare the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence.

[0086] It should be understood that during the online processing phase, a chaotic sequence can be dynamically generated in real time through the chaotic mapping module. The chaotic sequence is then compared with the quantization threshold value to obtain the frequency hopping sequence, thereby realizing the real-time conversion from random chaotic values ​​to ordered frequency hopping patterns.

[0087] Step S30: Input the frequency hopping sequence into the wide-interval processing unit to generate the final frequency hopping pattern.

[0088] Understandably, after the frequency hopping sequence generated by quantization is input into the wide-interval processing unit, the unit dynamically inserts additional frequency hopping intervals (e.g., forcibly skipping specified frequency points) by analyzing the distribution pattern of continuous frequency points in the sequence in real time (e.g., detecting continuous identical or adjacent frequency points). This breaks the inherent potential periodic distribution of the quantized sequence and prevents the system from residing on frequency points susceptible to jamming interference for extended periods. The final output frequency hopping pattern achieves sparsity and uniformity of frequency point distribution while strictly maintaining the original randomness and long-period characteristics of the chaotic sequence, significantly improving the anti-interference capability and communication reliability of the high-speed frequency hopping system.

[0089] This embodiment, through the above-described scheme, collects and sorts chaotic sequence samples offline, calculates a quantization threshold value based on uniform segmentation, generates a chaotic sequence online, compares the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence, and inputs the frequency hopping sequence into a wide-interval processing unit to generate the final frequency hopping pattern. This effectively maintains the randomness and long-period characteristics of the chaotic sequence, breaks the potential periodic distribution, avoids the system from staying on easily interfered frequencies for a long time, and ultimately generates a frequency hopping pattern with high uniformity, strong randomness, and excellent anti-interference capability. This meets the comprehensive requirements of high-speed frequency hopping systems for real-time performance, uniform distribution, and anti-blocking interference. Without destroying the randomness of the chaotic sequence, it ensures the uniformity and long periodicity of the quantized frequency hopping pattern, improving the speed and efficiency of chaotic frequency hopping pattern generation and quantization.

[0090] Furthermore, Figure 4 This is a flowchart illustrating the second embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention, as shown below. Figure 4As shown, based on the first embodiment, a second embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention is proposed. In this embodiment, step S10 specifically includes the following steps:

[0091] Step S11: Collect sample data of chaotic sequence samples offline and sort the values ​​to generate an ordered sequence.

[0092] It should be noted that during the offline processing stage, sample data of chaotic sequences can be collected, and then these sample data can be numerically sorted to generate an ordered sequence. This ordered sequence serves as the input basis for quantization threshold calculation, ensuring that subsequent uniform segmentation can accurately delineate the frequency hopping point boundaries.

[0093] Furthermore, step S11 specifically includes the following steps:

[0094] The chaotic sequence generated based on the input system parameters and the initial seed, with an offline screenshot length of L, is sorted from smallest to largest to generate an ordered sequence.

[0095] Understandably, during the offline processing stage, a chaotic sequence sample of length L can be generated based on the input system parameters (such as the control parameters of the chaotic mapping) and the initial seed (initial value). Then, the sample sequence is sorted in ascending order to form a strictly ordered sequence (i.e., the sorted chaotic sequence), which provides an accurate distribution basis for subsequent calculation of the quantization threshold value based on uniform partitioning.

[0096] Step S12: Divide the ordered sequence into equal segments and calculate the quantization threshold value corresponding to the divided sequence segments.

[0097] It should be understood that by uniformly dividing the ordered sequence and calculating the quantization threshold values ​​of the equally divided sequence segments, these threshold values ​​accurately define the boundary intervals of the frequency hopping points, ensuring the uniformity and randomness of the frequency point distribution when generating the frequency hopping pattern online.

[0098] Furthermore, step S12 specifically includes the following steps:

[0099] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the equally divided sequence segment is calculated using the following formula:

[0100]

[0101]

[0102]

[0103] in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The ordered sequence obtained after sorting To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number .

[0104] In the specific implementation, the frequency hopping pattern generator first needs to enter offline mode. The specific steps are as follows:

[0105] 1. Extract a chaotic sequence of length L generated based on the input system parameters and the initial seed. Then, sort the data to obtain an ascending sequence:

[0106] 2. Based on the number of mapped frequency points N, obtain the quantization threshold values ​​for each segment. ; The calculation formula is shown above, where .

[0107] 3. Generate quantization threshold values ​​for each segment and store them locally as system parameters for the post-frequency hopping pattern generator.

[0108] This embodiment, through the above-described scheme, generates an ordered sequence by offline acquisition of sample data from chaotic sequence samples and numerical sorting; the ordered sequence is then uniformly divided, and the quantization threshold value corresponding to the divided sequence segments is calculated, effectively avoiding the defect of traditional methods that make it difficult to balance uniformity and randomness. This processing strictly maintains the original randomness and long-period characteristics of the chaotic sequence, while providing a highly versatile and computationally efficient quantization foundation for online frequency hopping generation. It resolves the fundamental contradiction between uniformity, randomness, and versatility in existing schemes, laying a reliable technical foundation for the real-time application of high-speed frequency hopping systems.

[0109] Furthermore, Figure 5 This is a flowchart illustrating the third embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention, as shown below. Figure 5 As shown, based on the first embodiment, a third embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention is proposed. In this embodiment, step S20 specifically includes the following steps:

[0110] Step S21: Generate chaotic sequences online in real time using the chaotic mapping module.

[0111] It should be noted that during the online processing phase, the system can dynamically generate chaotic sequences based on real-time system parameters and initial seeds through the chaotic mapping module. These sequences possess high randomness, long periods, and unpredictability, providing real-time input data for subsequent frequency hopping mapping and ensuring the frequency hopping pattern operates reliably in high-speed real-time scenarios (e.g., frequency hopping rate ≥ 10). 4 Maintaining randomness and anti-interference capability (jumps / second) avoids the limitations of offline pre-generation.

[0112] Step S22: Compare each element in the chaotic sequence with the quantization threshold value element by element to obtain the frequency hopping sequence.

[0113] Understandably, during the online processing phase, the system can compare each element in the real-time generated chaotic sequence with the offline calculated quantization threshold value one by one. By determining the quantization interval into which the element value falls, the system determines its corresponding frequency index, thereby dynamically converting the original chaotic sequence into a continuous and uniformly distributed frequency hopping sequence, achieving efficient real-time mapping from chaotic randomness to ordered frequency hopping patterns.

[0114] Furthermore, step S22 specifically includes the following steps:

[0115] Each element in the chaotic sequence is compared element-by-element with the quantization threshold value, and the frequency value corresponding to each element is determined by the following formula based on the frequency intervals divided by the quantization threshold value:

[0116]

[0117] in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value;

[0118] The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

[0119] In the specific implementation, after obtaining the quantization threshold values ​​for each segment, the frequency hopping pattern generator can be used to generate frequency hopping patterns in real time for the frequency hopping system; that is, the frequency hopping pattern generator needs to enter online mode. The online mode processing steps are as follows:

[0120] 1. The chaotic mapping module generates a chaotic sequence based on the input system parameters and the initial seed, and maps each element in the sequence... By comparing it with the quantization threshold sequence, the frequency point value it maps to is determined using the above formula. .

[0121] 2. The quantized frequency hopping pattern sequence is sent to the wide-interval processing unit to generate the final frequency hopping pattern.

[0122] This embodiment uses the above-described scheme to generate a chaotic sequence online in real time through a chaotic mapping module. Each element in the chaotic sequence is compared with the quantization threshold value element by element to obtain a frequency hopping sequence. This avoids the drawback of traditional methods where uniformity and randomness are difficult to balance. The computational complexity of this process is extremely low, fully meeting the real-time requirements of high-speed frequency hopping systems and providing reliable technical support for the real-time generation of frequency hopping patterns.

[0123] Furthermore, Figure 6 This is a flowchart illustrating the fourth embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention, as shown below. Figure 6 As shown, based on the first embodiment, a fourth embodiment of the chaotic frequency hopping pattern generation and quantization method of the present invention is proposed. In this embodiment, step S30 specifically includes the following steps:

[0124] Step S31: The frequency hopping sequence is input to the wide-interval processing unit. The wide-interval processing unit detects the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping sequence in real time, dynamically inserts additional frequency hopping intervals, and generates the final frequency hopping pattern.

[0125] It should be noted that after the frequency hopping sequence generated by quantization is input into the wide-interval processing unit, the unit detects the distribution pattern of consecutive identical or adjacent frequency points in the sequence in real time (such as identifying intervals that continuously reside at the same or adjacent frequency points), and dynamically inserts additional frequency hopping intervals (such as forcibly skipping specified frequency points between consecutive frequency points), thereby effectively breaking the inherent potential periodic distribution of the quantized sequence. The final frequency hopping pattern, while strictly maintaining the original randomness and long-period characteristics of the chaotic sequence, achieves sparsity and uniformity of frequency point distribution, significantly avoids the system residing at frequency points that are susceptible to blocking interference for a long time, and greatly enhances the anti-interference capability and communication reliability of the high-speed frequency hopping system.

[0126] Understandably, the idea behind this scheme is derived from the queue sorting method. By sorting and uniformly dividing the chaotic sequence (essentially statistically analyzing the distribution intervals of the chaotic sequence), the uniformity of the frequency hopping pattern after quantization is ensured. At the same time, by utilizing the "convergence" of the chaotic sequence distribution, this scheme ensures that the frequency hopping pattern after quantization has good uniformity without destroying the randomness and long periodicity of the sequence.

[0127] In a specific implementation, the flowchart of the frequency hopping pattern generator based on the chaotic frequency hopping pattern quantization scheme of distribution statistics proposed in this invention is as follows: Figure 7 As shown, Figure 7 This is a block diagram of the frequency hopping pattern generator in the chaotic frequency hopping pattern generation and quantization method of the present invention. See [link / reference]. Figure 7 First, in offline mode, the chaotic sequence is captured, sorted, and a quantization threshold is generated. In online mode, chaotic mapping is performed based on system parameters and generation seed to generate a chaotic sequence. After quantization, wide-interval processing is performed to generate a frequency hopping pattern.

[0128] See Figure 8 , Figure 8 This is a schematic diagram of the simulation curve of the balance parameter in the chaotic frequency hopping pattern generation quantization method of the present invention, as shown in the figure. Figure 8 As shown, the simulation curves of the balance parameters of frequency hopping patterns generated by different quantization schemes are presented. The balance parameters are used to measure the uniformity of the frequency hopping pattern. The smaller the balance parameters, the better the uniformity. The simulation results show that the quantization scheme proposed in this paper has obvious performance advantages.

[0129] It should be understood that this scheme utilizes the "convergence" of chaotic sequence distribution to obtain the quantization threshold value corresponding to each frequency point through the distribution statistics and uniform division of sample data. Without destroying the randomness of the chaotic sequence, it ensures the uniformity and long periodicity of the frequency hopping pattern after quantization. In addition, the scheme has low computational cost and is applicable to all chaotic mapping sequences, so it is suitable for real-time chaotic frequency hopping devices in high-speed frequency hopping systems.

[0130] This embodiment, through the above-described scheme, inputs the frequency hopping sequence to a wide-interval processing unit. The wide-interval processing unit then detects the distribution pattern of consecutive identical or adjacent frequencies in the frequency hopping sequence in real time, dynamically inserting additional frequency hopping intervals to generate the final frequency hopping pattern. This effectively breaks the inherent potential periodic distribution of the quantization sequence, preventing the system from lingering on frequencies susceptible to blocking interference for extended periods. The final generated frequency hopping pattern, while strictly maintaining the original randomness and long-period characteristics of the chaotic sequence, achieves sparsity and uniformity in the frequency distribution, significantly improving the anti-interference capability and communication reliability of the high-speed frequency hopping system, and meeting the requirement of a frequency hopping rate ≥ 10. 4 Real-time requirement of jumps per second.

[0131] Accordingly, the present invention further provides a chaotic frequency hopping pattern generation quantization device.

[0132] Reference Figure 9 , Figure 9 This is a functional block diagram of the first embodiment of the chaotic frequency hopping pattern generation and quantization device of the present invention.

[0133] In a first embodiment of the chaotic frequency hopping pattern generation and quantization device of the present invention, the chaotic frequency hopping pattern generation and quantization device includes:

[0134] The threshold calculation module 10 is used to collect and sort chaotic sequence samples offline, and calculate the quantization threshold value based on uniform segmentation.

[0135] The frequency point sequence acquisition module 20 is used to generate a chaotic sequence online and compare the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence.

[0136] The frequency hopping pattern generation module 30 is used to input the frequency hopping frequency sequence into the wide-interval processing unit to generate the final frequency hopping pattern.

[0137] The threshold calculation module 10 is also used to collect sample data of chaotic sequence samples offline and sort them numerically to generate an ordered sequence; to uniformly divide the ordered sequence and calculate the quantization threshold value corresponding to the divided sequence segments.

[0138] The threshold calculation module 10 is also used to sort the chaotic sequence generated from the input system parameters and the initial seed in ascending order, based on the offline screenshot of length L, to generate an ordered sequence.

[0139] The threshold calculation module 10 is also used to uniformly divide the ordered sequence and calculate the quantization threshold value corresponding to the equally divided sequence segment using the following formula:

[0140]

[0141]

[0142]

[0143] in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The ordered sequence obtained after sorting To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number .

[0144] The frequency point sequence acquisition module 20 is also used to generate a chaotic sequence online in real time through the chaotic mapping module; and to compare each element in the chaotic sequence with the quantization threshold value element by element to obtain the frequency hopping sequence.

[0145] The frequency sequence acquisition module 20 is further configured to compare each element in the chaotic sequence with the quantization threshold value element by element, and determine the frequency value corresponding to each element according to the frequency interval divided by the quantization threshold value using the following formula:

[0146]

[0147] in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value;

[0148] The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

[0149] The frequency hopping pattern generation module 30 is also used to input the frequency hopping frequency point sequence to the wide interval processing unit, and to detect the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping frequency point sequence in real time through the wide interval processing unit, dynamically insert additional frequency hopping intervals, and generate the final frequency hopping pattern.

[0150] The steps for implementing each functional module of the chaotic frequency hopping pattern generation and quantization device can be referred to in the various embodiments of the chaotic frequency hopping pattern generation and quantization method of the present invention, and will not be repeated here.

[0151] Furthermore, this embodiment of the invention also proposes a storage medium storing a chaotic frequency hopping pattern generation and quantization program, which, when executed by a processor, performs the following operations:

[0152] Offline collection and sorting of chaotic sequence samples, and calculation of quantization threshold values ​​based on uniform segmentation;

[0153] A chaotic sequence is generated online, and the chaotic sequence is compared with the quantization threshold value to obtain a frequency hopping sequence;

[0154] The frequency hopping sequence is input into the wide-interval processing unit to generate the final frequency hopping pattern.

[0155] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0156] Offline collection of chaotic sequence sample data and numerical sorting to generate ordered sequences;

[0157] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the divided sequence segment is calculated.

[0158] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0159] The chaotic sequence generated based on the input system parameters and the initial seed, with an offline screenshot length of L, is sorted from smallest to largest to generate an ordered sequence.

[0160] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0161] The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the equally divided sequence segment is calculated using the following formula:

[0162]

[0163]

[0164]

[0165] in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The ordered sequence obtained after sorting To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number .

[0166] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0167] Chaotic sequences are generated online in real time using the chaos mapping module;

[0168] Each element in the chaotic sequence is compared with the quantization threshold value element by element to obtain the frequency hopping sequence.

[0169] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0170] Each element in the chaotic sequence is compared element-by-element with the quantization threshold value, and the frequency value corresponding to each element is determined by the following formula based on the frequency intervals divided by the quantization threshold value:

[0171]

[0172] in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value;

[0173] The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

[0174] Furthermore, when the chaotic frequency hopping pattern generation quantization program is executed by the processor, it also performs the following operations:

[0175] The frequency hopping sequence is input to the wide-interval processing unit, which detects the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping sequence in real time, dynamically inserts additional frequency hopping intervals, and generates the final frequency hopping pattern.

[0176] Those skilled in the art will understand that all or part of the steps in the methods described above can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium is a computer-readable storage medium, including: a USB flash drive, a portable hard drive, and a read-only memory (ROM). Various media that can store program code, such as only memory, random access memory (RAM), magnetic disks or optical disks.

[0177] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0178] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0179] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for generating and quantizing chaotic frequency-hopping patterns, characterized in that, The chaotic frequency hopping pattern generation quantization method includes: Offline collection and sorting of chaotic sequence samples, and calculation of quantization threshold values ​​based on uniform segmentation; A chaotic sequence is generated online, and the chaotic sequence is compared with the quantization threshold value to obtain a frequency hopping sequence; The frequency hopping sequence is input into the wide-interval processing unit to generate the final frequency hopping pattern. The step of generating a chaotic sequence online and comparing the chaotic sequence with the quantization threshold to obtain a frequency hopping sequence includes: Chaotic sequences are generated online in real time using the chaos mapping module; Each element in the chaotic sequence is compared with the quantization threshold value element by element to obtain the frequency hopping sequence. The step of comparing each element in the chaotic sequence with the quantization threshold value element by element to obtain the frequency hopping sequence includes: Each element in the chaotic sequence is compared element-by-element with the quantization threshold value, and the frequency value corresponding to each element is determined by the following formula based on the frequency intervals divided by the quantization threshold value: in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number, Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value; The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

2. The chaotic frequency hopping pattern generation and quantization method as described in claim 1, characterized in that, The offline collection and sorting of chaotic sequence samples, and the calculation of quantization threshold values ​​based on uniform segmentation, include: Offline collection of chaotic sequence sample data and numerical sorting to generate ordered sequences; The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the divided sequence segment is calculated.

3. The chaotic frequency hopping pattern generation and quantization method as described in claim 2, characterized in that, The offline acquisition of chaotic sequence sample data and numerical sorting to generate an ordered sequence includes: The chaotic sequence generated based on the input system parameters and the initial seed, with an offline screenshot length of L, is sorted from smallest to largest to generate an ordered sequence.

4. The chaotic frequency hopping pattern generation and quantization method as described in claim 2, characterized in that, The step of uniformly dividing the ordered sequence and calculating the quantization threshold value corresponding to the equally divided sequence segments includes: The ordered sequence is uniformly divided, and the quantization threshold value corresponding to the equally divided sequence segment is calculated using the following formula: in, Let i be the quantization threshold value. For sorted ordered sequences The value of the element with index 'index' in the middle. For the offline stage of the original chaotic sequence The sorted sequence is obtained after sorting. To demonstrate the original chaotic sequence samples collected offline, Integer index, For sequence length, For the frequency hopping points, Frequency point number, .

5. The chaotic frequency hopping pattern generation and quantization method as described in claim 1, characterized in that, The step of inputting the frequency hopping sequence into the wide-interval processing unit to generate the final frequency hopping pattern includes: The frequency hopping sequence is input to the wide-interval processing unit, which detects the distribution pattern of consecutive identical or adjacent frequency points in the frequency hopping sequence in real time, dynamically inserts additional frequency hopping intervals, and generates the final frequency hopping pattern.

6. A chaotic frequency-hopping pattern generation quantization device, characterized in that, The chaotic frequency hopping pattern generation and quantization device includes: The threshold calculation module is used to collect and sort chaotic sequence samples offline, and calculate the quantization threshold based on uniform segmentation. The frequency point sequence acquisition module is used to generate a chaotic sequence online and compare the chaotic sequence with the quantization threshold value to obtain a frequency hopping sequence. The frequency hopping pattern generation module is used to input the frequency hopping frequency sequence into the wide-interval processing unit to generate the final frequency hopping pattern; The frequency point sequence acquisition module is also used to generate a chaotic sequence online in real time through the chaotic mapping module; and to compare each element in the chaotic sequence with the quantization threshold value element by element to obtain the frequency hopping sequence. The frequency sequence acquisition module is further configured to compare each element in the chaotic sequence with the quantization threshold value element by element, and determine the frequency value corresponding to each element according to the frequency interval divided by the quantization threshold value using the following formula: in, Let be the frequency value at the j-th position in the frequency hopping sequence. Frequency point number, Let i be the quantization threshold value. Let i be the i-th element in the chaotic sequence. This is the (i+1)th quantization threshold value; The chaotic sequence is dynamically mapped into a continuous frequency hopping sequence based on the frequency point value.

7. A chaotic frequency-hopping pattern generation and quantization device, characterized in that, The chaotic frequency hopping pattern generation and quantization device includes: a memory, a processor, and a chaotic frequency hopping pattern generation and quantization program stored in the memory and executable on the processor, wherein the chaotic frequency hopping pattern generation and quantization program is configured to implement the steps of the chaotic frequency hopping pattern generation and quantization method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a chaotic frequency hopping pattern generation and quantization program, which, when executed by a processor, implements the steps of the chaotic frequency hopping pattern generation and quantization method as described in any one of claims 1 to 5.

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