Low-signal-to-noise-ratio Chirp signal high-precision detection method and computer equipment

Abstract

The invention discloses a low-signal-to-noise-ratio Chirp signal high-precision detection method and computer equipment, and the method comprises the steps: setting the range of a system driving force of a Duffing oscillator, and setting the system driving force when a Lyapunov index is changed from greater than 0 to less than 0 as a system driving force threshold value; constructing a Duffing oscillator of a system frequency corresponding to a system driving force threshold value, and determining a range of an unknown signal frequency value by using a wide frequency characteristic of a critical state of the Duffing oscillator; constructing a Duffing oscillator array comprising more than three oscillators, respectively searching oscillator frequency values of which the Lyapunov index is less than 0, and determining the oscillator frequency values as frequency values in an unknown Chirp signal frequency range; and constructing a plurality of oscillator arrays by taking the determined frequency value as a center frequency, obtaining a plurality of frequencies of the oscillator arrays of which the Lyapunov index is less than 0, and obtaining a frequency range and a frequency modulation slope of the Chirp signal according to the frequencies of the oscillator arrays. According to the invention, high-precision detection of the frequency parameter of the Chirp signal with variable frequency can be realized under the condition of low signal-to-noise ratio.

Description

Technical Field

[0001] The present invention belongs to the technical field of weak signal detection, and particularly relates to a high-precision detection method for low signal-to-noise ratio Chirp signals and a computer device. Background Art

[0002] In today's information network era, in the face of hundreds of millions of free space signals, how to accurately extract and detect the parameters of target weak signals in a complex and changeable electromagnetic space is a key fundamental problem that urgently needs to be solved. As one of the typical signal detection technologies, weak signal detection aims to effectively detect the parameters of target weak signals in a background environment of low signal-to-noise ratio and various strong interferences. Its detection ability determines the success or failure of information transmission and is an important research content for the performance of the entire network system.

[0003] Currently, the weak signal detection method based on chaos theory can perform high-precision detection of the frequency of weak signals according to the output state of the system under low signal-to-noise ratio conditions due to the immunity of the chaos system to noise and the sensitivity to signals of specific frequencies. This method is different from the reduction of target signals by filtering in traditional linear detection methods and can extract the parameters of target signals according to the output state, with high detection accuracy and good effects. However, its prerequisite is that the transmission environment of weak signals is relatively simple, and the system only judges the presence or absence of target periodic weak signals.

[0004] The linear frequency modulation signal is a typical non-periodic signal whose frequency changes with time, also known as a Chirp signal. Many scholars have made effective explorations on the detection of Chirp signals. For example, the high-precision maximum likelihood estimation algorithm, but due to its two-dimensional search, it has a slow speed and is only applicable to higher signal-to-noise ratio situations. The Randon-Wigner transform method, the Randon-ambiguity transform method, and the Randon-ambiguity successive filtering algorithm are all time-frequency analysis algorithms and can generally effectively detect Chirp signals when the signal-to-noise ratio is higher than -5dB. The above algorithms cannot work properly under lower signal-to-noise ratios (such as -15dB), and such lower or extremely low signal-to-noise ratio situations widely exist in communication environments such as tsunamis and artificial strong interferences. Summary of the Invention

[0005] The purpose of the present invention is to provide a high-precision detection method for low signal-to-noise ratio Chirp signals, a computer device, a computer-readable storage medium, and a computer program product, which can achieve high-precision detection of the frequency parameters of Chirp signals with frequency changes under low signal-to-noise ratio conditions.

[0006] To achieve the above purpose, one aspect of the present invention provides a high-precision detection method for low signal-to-noise ratio Chirp signals, including: Step S1, set the range of the system driving force of the Duffing oscillator, calculate the Lyapunov exponents of different system driving forces, and set the system driving force when the Lyapunov exponent changes from greater than 0 to less than 0 as the system driving force threshold; Step S2, construct a Duffing oscillator with a system frequency corresponding to the system driving force threshold as , and input the signal to be measured with an unknown frequency value of into the Duffing oscillator in the critical state. Utilizing the wide-frequency characteristic of the critical state of the Duffing oscillator, the frequency value can be determined to be within the range of ; Step S3, construct a Duffing oscillator array including more than 3 oscillators, respectively search for the oscillator frequency values with Lyapunov exponents less than 0, and determine the oscillator frequency values as the frequency values within the unknown Chirp signal frequency range; Step S4, with the determined frequency values within the Chirp signal frequency range as the center frequencies, construct multiple oscillator arrays, obtain the frequencies of several oscillator arrays with Lyapunov exponents less than 0, and based on the multiple frequency values and corresponding time points within the obtained Chirp signal frequency range, obtain the frequency range and frequency modulation slope of the Chirp signal.

[0007] Preferably, step S1 includes: Step S11: Set the range of the system driving force as the minimum value , the maximum value ; Step S12: Set the system driving force , calculate the Lyapunov exponent value of the system driving force . If is greater than 0, then set , otherwise set , and repeat this step until , where N1 is an integer between 8 and 20; Step S13: Set , . If , then set , where the value range of N2 is 0.001 to 0.005, calculate the Lyapunov exponent value of . If >0, then repeat this step. If , then set the system driving force threshold as .

[0008] Preferably, step S2 includes: Step S21: Construct a Duffing oscillator with a system frequency corresponding to the system driving force threshold as , and input a signal to be measured with a frequency value of into the Duffing oscillator in a critical state; Step S22: Calculate N Lyapunov exponent values, where N > 5. If each Lyapunov exponent value is less than , then determine that the output is in a periodic state; otherwise, determine that the output is in a chaotic state; Step S23: When it is determined that the output is in a periodic state, it indicates that the frequency value of the signal to be measured is within ; when it is determined that the output is in a chaotic state, it indicates that the frequency value of the signal is within .

[0009] Preferably, step S3 includes: Step S31: Set the frequency range of the Chirp signal including the frequency range as , and set , where N4 is an integer between 10 and 50; Step S32: Construct an array of Duffing oscillators with a system frequency of , the oscillator frequencies are respectively , , , the number of oscillators is 3, and calculate the Lyapunov exponent values of , , respectively as , , ; Step S33: If , then set as the frequency value within the frequency range of the Chirp signal; otherwise, execute step S34; Step S34: Take the intermediate value of two adjacent frequency values of , , as the new oscillator frequency value, and continue to judge the Lyapunov exponent value of the new oscillator. If the Lyapunov exponent values are all greater than 0, then continue to increase the oscillator with the frequency value in the middle of two adjacent frequency values until it is determined that there is a certain frequency value whose Lyapunov exponent value is less than 0, then set this frequency value as the frequency value within the frequency range of the Chirp signal.

[0010] Preferably, step S4 includes: Step S41: Set the frequency interval , floor denotes rounding up, construct an oscillator array, where the frequency value of the oscillator is , where k is a positive integer; Step S42: If the Lyapunov exponent value of the constructed oscillator array is less than 0, obtain the oscillator array frequency and time point at this time; Step S43: Obtain multiple sets of oscillator array frequency and time point data, obtain the frequency range to be measured of the Chirp signal according to the multiple oscillator array frequencies, and use the slope of the fitting line of the multiple sets of oscillator array frequency and time point data as the frequency modulation slope of the Chirp signal to be measured.

[0011] Another aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the above method.

[0012] Another aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0013] Another aspect of the present invention provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0014] According to the low signal-to-noise ratio Chirp signal high-precision detection method, computer device, computer-readable storage medium, and computer program product of the above aspects of the present invention, it is possible to realize high-precision detection of the characteristic parameters of the Chirp signal under low signal-to-noise ratio conditions based on the wide-frequency detection characteristics of the critical state of the chaotic theory Duffing oscillator. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings: Figure 1 is a flowchart of a low signal-to-noise ratio Chirp signal high-precision detection method according to an embodiment of the present invention; Figure 2 is a flowchart of using the critical state characteristics of the Duffing oscillator to determine the frequency value range of an unknown Chirp signal according to an embodiment of the present invention; Figure 3Schematic diagram for searching and determining frequency values within the target Chirp signal range in an embodiment of the present invention; Figure 4 It is a structural diagram of a computer device in an embodiment of the present invention. Detailed implementation manners

[0016] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] An embodiment of the present invention provides a method for high-precision detection of Chirp signals with low signal-to-noise ratio. By constructing a Duffing oscillator array based on chaos theory and utilizing the wide frequency characteristics in the critical state, aiming at the wide frequency band characteristics of Chirp signals, it realizes the rapid and efficient detection of the frequency range and frequency modulation slope of Chirp signals under low signal-to-noise ratio.

[0018] Generally, the mathematical model of a Duffing oscillator is as follows:

[0019] In the formula: represents time, represents the signal amplitude varying with time, and are respectively the second derivative and the first derivative of, is the nonlinear restoring force of the system, and are coefficients, is the damping ratio, and are respectively the amplitude and angular frequency of the system periodic driving force . When , and are fixed, as the value increases from small to large, the output state of the system first is in the homoclinic orbit state and the period bifurcation state, and then successively enters three significantly different states, namely the chaotic state, the critical state and the large-scale periodic state.

[0020] When the system is in a chaotic state, the phase diagram trajectory output by the Duffing oscillator is disordered, and the corresponding time-domain waveform is aperiodic; when the system is in a large-scale periodic state, its phase diagram trajectory is ordered and the corresponding time-domain waveform is periodically changing; when the system is in a critical state, both chaotic and large-scale periodic states exist in its phase diagram trajectory, and the two also appear alternately in its corresponding time-domain waveform.

[0021] In particular, when k = 0.5, as the amplitude increases from small to large, the phase diagram of the oscillator will experience three significantly different system states: chaotic state (F = 0.81), critical state (F = 0.826), and large-scale periodic state (F = 0.83).

[0022] The weak signal detection method based on the Duffing oscillator is as follows: Set the system parameters so that the system is in a critical state, and input the weak signal to be measured into the system. If the frequency of the weak signal to be measured is the same as the frequency of the system driving force, the amplitude of the total system driving force will increase and be higher than the critical value, and the output state of the system will enter the large-scale periodic state; otherwise, if the amplitude of the total system driving force remains unchanged, the output state of the system will remain unchanged.

[0023] The output state can be accurately judged by the Lyapunov exponent discrimination method. The Lyapunov exponent calculated at different trajectory points is defined as:

[0024] where represents time, represents the signal amplitude changing with time represents the error changing with time represents the initial signal amplitude at time = 0, represents the Lyapunov exponent at the initial signal amplitude represents time = 0 and the initial signal amplitude is represents time and the initial signal amplitude is That is, when That is, when

[0025] Based on the above principle, as Figure 1 ​​​​​As shown in the figure, the high-precision detection method for low signal-to-noise ratio Chirp signals in the embodiments of the present invention includes steps S1 to S4.

[0026] Step S1: Obtain the system excitation force threshold of the Duffing oscillator . Step S1 may further include steps S11 to S13.

[0027] Step S11: Set the system excitation force range to increase from the minimum value to the maximum value so that the output state of the system first enters the homoclinic orbit state and the period bifurcation state, and then successively enters three significantly different states, namely the chaotic state, the critical state, and the large-scale periodic state.

[0028] Step S12: Take the intermediate value of and as the system excitation force , and calculate the Lyapunov exponent value of . If is greater than 0, set , otherwise, set . Repeat this step. When , the loop is terminated. Here, N1 can take an integer between 8 and 20. Preferably, N1 = 10.

[0029] Step S13: Set , . If , then set . The value range of N2 is 0.001 to 0.005. For example, N2 = 0.001. Calculate the Lyapunov exponent value of . If >0, repeat this step. If , then set the system excitation force threshold to be .

[0030] Step S2: Determine the range of the unknown signal frequency value by using the critical state characteristics of the Duffing oscillator. As shown in the flowchart of Figure 2 , step S2 may further include steps S21 to S23.

[0031] Step S21: Construct a Duffing oscillator with a system frequency corresponding to the system excitation force threshold as , and input the unknown frequency signal into the Duffing oscillator in the critical state.

[0032] When the target signal to be measured When input into the Duffing oscillator, then

[0033] and are the amplitude and angular frequency of the target signal, is the frequency of the target signal, satisfying , is the phase difference between the target signal and the system driving force, is the background noise (Gaussian noise).

[0034] The total periodic driving force of the system becomes:

[0035] wherein, , .

[0036] The input of the target signal can periodically cause the output state of the system to be in a chaotic state or a large-scale periodic state.

[0037] Step S22: Calculate the Lyapunov exponent value and determine whether this value is near zero and remains for a period of time. The wolf method can be used to calculate the maximum Lyapunov exponent , determine whether it satisfies , and calculate N Lyapunov exponent values (N>5), and each value satisfies , then it is determined that the output is in a periodic state, otherwise it is determined that the output is in a chaotic state; Step S23: When the output is in a periodic state, it indicates that the frequency value of the signal is within range. When it is determined that the output is in a chaotic state, it indicates that the frequency value of the signal is within range, where N3 is the judgment threshold, and a value within the range of 0.03 to 0.04 can be taken. In this embodiment, let N3 = 0.03. From the above analysis, it can be seen that in the critical state, the frequency values of the weak signals within the frequency range ( is the target frequency value) will make the critical state in a partial periodic state or a partial chaotic state, rather than an obvious periodic or chaotic state, thus causing the periodic uncertainty of the critical state and making the system unable to effectively detect the weak signals in this frequency region. This is an inherent characteristic of the critical state, and the macroscopic manifestation is the detection blind area. This characteristic will affect the detection accuracy in the detection of fixed-frequency signals. Existing technologies all focus on how to eliminate the influence of the detection blind area, but the present invention innovatively utilizes this characteristic to realize the frequency detection of wide-band frequency-variable signals. According to the wide-frequency detection characteristic of the critical state, the Duffing oscillator constructed in step S2 can achieve the detection of Frequency detection within a range.

[0038] Step S3: Quickly search and determine the frequency values within the range of the target Chirp signal. Refer to Figure 3 As shown, step S3 may further include steps S31 to S34.

[0039] Step S31: Set the frequency range according to the band where the Chirp signal is located , which includes the target Chirp signal with a frequency range of . To ensure the rapidity of the system detection performance, assume , where N4 can take an integer between 10 and 50. Preferably, N4 = 20.

[0040] Step S32: Construct a Duffing oscillator array with a system frequency of . The oscillator frequencies are respectively , , . The number of oscillators is 3. Calculate the Lyapunov exponent values , , .

[0041] Step S33: If there exists , then set as the frequency value within the frequency range of the Chirp signal. Otherwise, execute step S34 to add a new oscillator.

[0042] Step S34: For the existing (3) oscillator frequency values, take the middle value of two adjacent frequency values as the frequency value of the newly added oscillator. Continue to judge the Lyapunov exponent value of the newly added oscillator. If the Lyapunov exponent values are all greater than 0, , then continue to add oscillators with the frequency values in the middle of two adjacent frequencies, judge the Lyapunov exponent values, and repeat until it is judged that there exists a certain frequency value of , then set this frequency value as the frequency value within the frequency range of the Chirp signal.

[0043] Step S4: Implement the detection of the frequency range and frequency modulation slope of the Chirp signal. Step S4 may further include steps S41 to S43.

[0044] Step S41: Set , floor represents rounding up. Construct an oscillator array with frequency values of , k = 1, 2, 3...; Step S42: Let the number of oscillators be v and the time be t, where v = 1, 2, 3... If the Lyapunov exponent value of the constructed array is less than 0, the oscillator array frequency at this time is obtained. and the time point ; Step S43: Obtain v data , and the frequency range formed by the v oscillator array frequencies is the frequency range of the target Chirp signal; with as the x-axis and as the y-axis, determine v points in the two-dimensional graph. By linearly fitting the v data, obtain the fitting line and the slope of this line, which is the frequency modulation slope of the target Chirp signal.

[0045] The following further illustrates the high-precision detection method for low signal-to-noise ratio Chirp signals in the embodiments of the present invention through a specific example.

[0046] Let the frequency range of the target Chirp signal be , the signal duration t = 10 -2 s. From this, the frequency modulation slope of the Chirp signal can be obtained. The range of the signal to be measured is , and oscillators with frequencies , , are constructed, where is calculated from . If the calculated Lyapunov exponents are all greater than zero, new oscillators are added to obtain oscillators with frequencies , , which are calculated from , respectively. The oscillator with frequency obtains a Lyapunov exponent less than zero, and it can be determined that the frequency value is within the frequency range of the Chirp signal.

[0047] With the frequency value as the center frequency, construct 2 oscillators symmetrically on both sides with an interval of around it. In total, 5 oscillators are constructed, with frequencies respectively.

[0048] Input the target Chirp signal with a frequency range of to oscillators with frequency values , , , , Among the 5 oscillators, the Lyapunov exponent values of the 5 oscillators are calculated and are all less than zero. The number of oscillator arrays can be increased, and then it is confirmed that the frequency range of the Chirp signal is .

[0049] Calculate the frequency modulation slope, .

[0050] Thus, the detection of the frequency range and frequency modulation slope of the Chirp signal is completed.

[0051] To sum up, the high-precision detection method for low signal-to-noise ratio Chirp signals in the embodiments of the present invention aims at the problem of low detection accuracy of target weak signal parameters in complex environments. Based on the sensitivity of the target and the immunity to noise of chaos theory, the characteristics of the critical state of the Duffing oscillator are deeply analyzed. The output state of the oscillator is quickly judged by the Lyapunov exponent discrimination method. The detection ability of the oscillator for fixed-frequency signals is extended to the detection of signals within the frequency range, and an oscillator array and a detection algorithm are innovatively constructed, which can quickly determine the frequency value of the target Chirp signal, and then realize the detection of the frequency range and frequency modulation slope of the Chirp signal.

[0052] The high-precision detection method for low signal-to-noise ratio Chirp signals in the embodiments of the present invention has the following beneficial effects: 1. The traditional weak signal detection method based on chaos theory is extended from the detection of single-frequency signals to the detection of signals with frequency varying over time, further expanding the application scope of chaos theory in the field of weak signal detection technology. It can be applied to the parameter detection of target signals in relative motion, thereby extending the weak signal detection ability of the Duffing oscillator in chaos theory to the detection, recognition, and perception of characteristic parameters of moving targets, and has wide applications in fields such as far-field complex detection and global environment perception; 2. The traditional weak signal detection method based on chaos theory discriminates the presence or absence of a target single-frequency signal by whether the state of the system output is a periodic state or a chaotic state. This type of method tries to reduce the existence time of the transition state (critical state) between the two states. The present invention innovatively uses the characteristics of the critical state to quickly determine and detect the wide frequency coverage range of the frequency-varying signal itself, greatly improving the signal parameter detection speed and accuracy at low signal-to-noise ratio; 3. It can quickly, efficiently, and accurately detect the frequency parameters such as the frequency range and frequency modulation slope of linear frequency modulation signals (Chirp signals) at low signal-to-noise ratio (-15dB).

[0053] The embodiments of the present invention also provide a computer device, which can be a server, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the operation parameter data of each framework. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements the steps of the method of the embodiment of the present invention.

[0054] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0055] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method of the embodiment of the present invention.

[0056] An embodiment of the present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method of the embodiment of the present invention.

[0057] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A high-precision detection method for low signal-to-noise ratio Chirp signals, characterized in that, Including: Step S1: Set the range of the system driving force of the Duffing oscillator, calculate the Lyapunov exponents of different system driving forces, and set the system driving force when the Lyapunov exponent changes from greater than 0 to less than 0 as the system driving force threshold; Step S2: Construct a Duffing oscillator with a system frequency corresponding to the system driving force threshold. Input the signal to be measured with an unknown frequency value of into the Duffing oscillator in a critical state. By utilizing the wide-frequency characteristic of the critical state of the Duffing oscillator, the frequency value can be determined to be within the range of . Step S3: Construct a Duffing oscillator array including more than 3 oscillators, respectively search for the oscillator frequency values with Lyapunov exponents less than 0, and determine the oscillator frequency values as the frequency values within the unknown Chirp signal frequency range; Step S4: With the determined frequency values within the Chirp signal frequency range as the center frequencies, construct multiple oscillator arrays, obtain the frequencies of several oscillator arrays with Lyapunov exponents less than 0, and obtain the frequency range and frequency modulation slope of the Chirp signal according to the multiple frequency values within the obtained Chirp signal frequency range and the corresponding time points.

2. The method according to claim 1, characterized in that Step S1 includes: Step S11: Set the system driving force range to the minimum value , the maximum value ; Step S12: Setting system driving force , computing system driving force The Lyapunov exponent value of ,like If it is greater than 0, then set , otherwise set Repeat this step until , where N1 is an integer between 8 and 20; Step S13: Set , If , then set , where the value range of N2 is 0.001 to 0.005, calculate the Lyapunov exponent value of If >0, repeat this step. If , then set the system driving force threshold to .​ 3. The method according to claim 2, wherein Step S2 includes: Step S21: Construct a Duffing oscillator corresponding to the system driving force threshold with a system frequency of , and input a signal to be measured with a frequency value of into the Duffing oscillator in a critical state; Step S22: Calculate N Lyapunov exponent values, where N > 5. If each Lyapunov exponent value is less than , then it is determined that the output is in a periodic state; otherwise, it is determined that the output is in a chaotic state. Step S23: When it is determined that the output is in a periodic state, it indicates that the frequency value of the signal to be measured is in the range; when it is determined that the output is in a chaotic state, it indicates that the frequency value of the signal is in the range.

4. The method according to any one of claims 1-3, characterized in that, Step S3 includes: Step S31: Set the frequency range to be measured of the Chirp signal including the frequency range as , and set , where N4 takes an integer between 10 and 50; Step S32: Construct a Duffing oscillator array with a system frequency of , and the oscillator frequencies are respectively , , . The number of oscillators is 3. Calculate the Lyapunov exponent values of , , respectively, which are , , ; Step S33: If , then set to a frequency value within the frequency range of the Chirp signal; otherwise, execute Step S34. Step S34: Take , , the intermediate value of two adjacent frequency values as the newly added oscillator frequency value, and continue to judge the Lyapunov exponent value of the newly added oscillator. If the Lyapunov exponent values are all greater than 0, continue to increase the oscillator with the frequency value in the middle of two adjacent frequency values until it is judged that there is a certain frequency value whose Lyapunov exponent value is less than 0, then set this frequency value as the frequency value within the frequency range of the Chirp signal.

5. The method according to claim 4, wherein Step S4 includes: Step S41: Set the frequency interval , floor denotes rounding up, construct an oscillator array, where the frequency value of the oscillator is , where k is a positive integer; Step S42: If the Lyapunov exponent value of the constructed oscillator array is less than 0, obtain the oscillator array frequency and time point at this time; Step S43: Obtain the data of multiple oscillator array frequencies and time points, obtain the frequency range to be measured of the Chirp signal according to the multiple oscillator array frequencies, and use the slope of the fitting line of the multiple oscillator array frequencies and time point data as the frequency modulation slope of the Chirp signal to be measured.

6. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-5.

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