High-precision linear frequency modulation signal real-time detection method based on chaos theory

Abstract

The invention provides a high-precision chirp signal real-time detection method based on a chaos theory, and the method is characterized in that the method comprises the following steps: S1, reasonably setting oscillator parameters and threshold values according to a target signal needing to be detected; s2, inputting a signal to be detected; s3, phase output of the chaotic system is observed, and data distribution statistics are output; and S4, comparing the statistical magnitude with a threshold value to complete detection. The method is low in algorithm complexity, and can detect the chirp signal in real time. Compared with a two-step method, the method is more reasonable in operation, does not introduce additional errors, and is higher in precision. The detection performance of the weak linear frequency modulation signal is effectively improved, and the detection task can be effectively completed under the condition of an extremely low signal-to-noise ratio.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and more specifically, to a method for real-time detecting a chirp signal with high precision based on chaos theory. Background Art

[0002] With the continuous development of communication technologies, communication devices have become increasingly dense and communication times have become increasingly frequent. At the same time, communication interference has become increasingly large, resulting in a decreasing signal-to-noise ratio of device communication. Traditional detection methods cannot work under conditions of too low signal-to-noise ratio, so new detection methods need to be proposed. A chirp signal is a common communication signal. Currently, most detections of weak chirp signals are based on a two-step method for detection, that is, first finding the relationship between frequency and time, and then detecting the chirp signal through a formula. The two-step method has too high complexity and cannot accurately detect the signal in real time. Moreover, additional errors are introduced, and the detection effect is limited under low signal-to-noise ratio conditions.

[0003] In 2016, Antonio et al. fully considered the continuity of the chirp signal and proposed a detection method for detecting low signal-to-noise ratio chirp signals in combination with a Duffing oscillator, called CTFDM. By performing frequency analysis on the time window and continuously adjusting the detection range of the frequency, the detection range of the next time window is effectively shortened. This method reduces the complexity of the detection method, but still needs to be improved in terms of accuracy.

[0004] In 2021, Chen et al. proposed a method for detecting weak chirp signals based on chaos theory, called CSSVF. By obtaining approximate frequencies of different time windows through an oscillator array and performing linear fitting on these data, the chirp rate of the chirp signal can be obtained.

[0005] In 2022, Li et al. improved the coupling method of the Duffing oscillator and the Van der Pol oscillator, called IDCS, thereby obtaining better detection performance. By dividing the signal into multiple time windows, accurate frequencies of different time periods are obtained, and then the chirp signal is detected through linear regression analysis.

[0006] The chaos theory method detects signals by observing the changes in the system state. This state jump is theoretically independent of noise, so the chaos theory is insensitive to Gaussian white noise. The phase jump of the chaotic system can be used for weak signal detection. By setting the parameters of the chaotic oscillator to make it in a chaotic critical state, it is possible to judge whether a signal exists according to the changes in the oscillator state.

[0007] Disclose a method for real-time detecting linear frequency modulation signals based on chaos theory with high precision. The method includes the following steps: Set the parameters of the chaotic oscillator and the threshold for judging the state of the chaotic system according to application requirements, input the signal into the chaotic system, observe the change of the system phase, calculate the data distribution of the system displacement x, and statistically determine whether the average value of the system displacement is less than the threshold, so as to judge whether the target signal exists. Summary of the Invention

[0008] The present invention provides a method for real-time detecting linear frequency modulation signals based on chaos theory with high precision, which can obtain accurate detection results under low signal-to-noise ratio conditions.

[0009] To solve the above technical problems, the technical solution of the present invention is as follows:

[0010] A method for real-time detecting linear frequency modulation signals based on chaos theory with high precision includes the following steps.

[0011] S1: Reasonably set the oscillator parameters and threshold according to the target signal to be detected.

[0012] S2: Input the signal to be detected.

[0013] S3: Observe the phase output of the chaotic system and output the data distribution statistic.

[0014] S4: Compare the statistic with the threshold to complete the detection.

[0015] Preferably, according to the target linear frequency modulation signal to be detected, the parameters of the chaotic oscillator need to be adjusted. The expression of the chaotic oscillator is as follows:

[0016]

[0017] In the formula, x is the system displacement amount, and are the second derivative and the first derivative of x respectively; a is the damping ratio, bx 3 -cx is the nonlinear restoring force; F(t)cos(ω0t + k0t 2 ) is the internal driving force of the system, where ω0 is the initial angular frequency to be measured of the system, k0 is the frequency modulation rate to be measured of the system; f1 and F2 are two system constants. By setting appropriate parameters, the chaotic system is in a critical state.

[0018] Preferably, the signal to be detected can be expressed as:

[0019] y(t) = A·s(t) + n(t)

[0020] In the formula, s(t) is the target linear frequency modulation signal, A is the amplitude of the target linear frequency modulation signal, n(t) is Gaussian additive white noise. y(t) is the signal received by the chaotic system.

[0021] The expression of the target linear frequency modulation signal to be detected is as follows:

[0022] s(t) = cos(ω s t + k s t 2 )

[0023] where ω s is the initial angular frequency of the linear frequency modulation signal, and k s is the frequency modulation rate of the linear frequency modulation signal. To detect the target signal, it is necessary to set the initial angular frequency to be measured and the frequency modulation rate to be measured in the system to be the same as those of the target signal, ω0 = ω s , k0 = k s .

[0024] Preferably, the signal to be measured y(t) is input into the chaotic oscillator to solve for the system displacement x.

[0025] When there is no target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 1, and its data distribution is biased to the left or right of the zero point. When there is a target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 2, and its data distribution is symmetric about the zero point. The distribution of the system displacement x is statistically analyzed, and the absolute value of its average value is taken as the data distribution statistic:

[0026]

[0027] Preferably, the data distribution statistic M x is compared with the set threshold γ as the detection result. When M x ≥γ, it means that there is no target signal in the detected signal. When M x <γ, it means that there is a target signal in the detected signal. The detection result is output to complete the detection.

[0028] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0029] By improving the chaotic system, the present invention can achieve accurate detection of linear frequency modulation signals. Utilizing the characteristic that the chaotic system is insensitive to Gaussian white noise, it can detect signals under lower signal-to-noise ratio conditions. At the same time, this method greatly simplifies the detection steps of linear frequency modulation signals. Compared with other methods, this method reduces the algorithm complexity and improves the real-time performance of the algorithm. At the same time, the one-step method does not introduce additional errors and has a better detection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic flow chart of the method of the present invention.

[0031] Figure 2Phase diagram and data distribution diagram for cycle state 1 in the embodiment: (a) Phase diagram, (b) Data distribution diagram.

[0032] Figure 3 Phase diagram and data distribution diagram for cycle state 2 in the embodiment: (a) Phase diagram, (b) Data distribution diagram.

[0033] Figure 4 Detection success rates of four comparison algorithms under different signal-to-noise ratios in the embodiment. Specific implementation manner

[0034] The accompanying drawings are only for illustrative purposes and should not be construed as limitations on this patent;

[0035] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] This embodiment provides a high-precision method for real-time detecting linear frequency modulation signals based on chaos theory. As Figure 1 shown, it includes the following steps:

[0037] S1: Reasonably set the oscillator parameters and thresholds according to the target signal to be detected.

[0038] S2: Input the signal to be detected.

[0039] S3: Observe the phase output of the chaotic system and output the data distribution statistic.

[0040] S4: Compare the statistic with the threshold to complete the detection.

[0041] In this embodiment, in step S1, according to the target linear frequency modulation signal to be detected, the chaotic oscillator parameters need to be adjusted. The expression of the chaotic oscillator is as follows:

[0042]

[0043] In the formula, x is the system displacement, and are the second derivative and the first derivative of x respectively; a is the damping ratio, bx 3 -cx is the non-linear restoring force; F(t)cos(ω0t + k0t 2 ) is the internal driving force of the system, where ω0 is the initial angular frequency to be measured of the system, k0 is the frequency modulation rate to be measured of the system; F1 and F2 are two system constants. If the initial angular frequency to be measured of the system ω0 = 2π × 15000 Hz and the frequency modulation rate to be measured of the system k0 = 10000 Hz. By setting appropriate parameters, the chaotic system is in a critical state. The chaotic oscillator parameters can be set as a = 0.5, b = 1, c = 2, F1 = 1.100, F2 = 0.01.

[0044] In this embodiment, the signal to be detected in step S2 can be expressed as:

[0045] y(t) = A·s(t) + n(t)

[0046] In the formula, s(t) is the target linear frequency modulation signal, A is the amplitude of the target linear frequency modulation signal, and n(t) is Gaussian additive white noise. y(t) is the signal received by the chaotic system. Assume that the signal amplitude A = 0.050V. The mean of the Gaussian white noise n(t) is 0 and the variance is 0.01. Since the signal amplitude is extremely weak, the signal-to-noise ratio value of the received signal y(t) is also very small. In this case, the traditional signal detection method is ineffective.

[0047] The expression of the target linear frequency modulation signal to be detected is as follows:

[0048] s(t) = cos(ω s t + k s t 2 )

[0049] In the formula, ω s is the initial angular frequency of the linear frequency modulation signal, and k s is the frequency modulation rate of the linear frequency modulation signal. To detect the target signal, it is necessary to set the initial angular frequency to be measured and the frequency modulation rate to be measured in the system to be the same as those of the target signal, ω0 = ω s , k0 = k s . Therefore, the initial angular frequency ω s of the target signal = 2π×15000Hz, k s = 10000Hz.

[0050] In this embodiment, in step S3, the signal y(t) to be measured is input into the chaotic oscillator, and the system displacement x is solved.

[0051] When there is no target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 1, as Figure 2 shown, and its data distribution is biased to the left or right of the zero point. When there is a target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 2, as Figure 3 shown, and its data distribution is symmetrically distributed with respect to the zero point. The distribution of the system displacement x is statistically analyzed, and the absolute value of its average value is taken as the data distribution statistic:

[0052]

[0053] In this embodiment, through simulation, it is obtained that M x = 1.2586 in the case of no target signal, while M x = 0.0064 in the case of having a target signal.

[0054] In this embodiment, the data distribution statistic M in step S4 x is compared with the set threshold γ as the detection result. When M x ≥γ, it means that there is no target signal in the detection signal. When M x <γ, it means that there is a target signal in the detection signal. Output the detection result to complete the detection.

[0055] According to the analysis of the simulation results, the threshold γ can be set to 0.5. If the data distribution statistic M x =1.2586, at this time M x ≥γ, it means that there is no target signal in the input signal. If the data distribution statistic M x =0.0064, at this time M x <γ, it means that there is a target signal in the input signal. Output the detection result according to the comparison.

[0056] Figure 4 is the detection success rate of four comparison algorithms under different signal-to-noise ratios in the embodiment. It can be seen that the detection success rate of the present invention is better than that of similar methods under different signal-to-noise ratio conditions.

[0057] The present invention improves the chaotic system so that it can achieve accurate detection of linear frequency modulation signals. Utilizing the characteristic that the chaotic system is insensitive to Gaussian white noise, it can achieve signal detection under lower signal-to-noise ratio conditions. At the same time, this method greatly simplifies the detection steps of linear frequency modulation signals. Compared with other methods, this method reduces the algorithm complexity and improves the real-time performance of the algorithm. At the same time, the one-step method does not introduce additional errors and has a better detection effect.

[0058] The drawings are only for illustrative purposes and should not be construed as a limitation of this patent;

[0059] Obviously, the above embodiments of the present invention are examples clearly illustrating the present invention, rather than limitations on the embodiments of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the embodiments here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A high-precision real-time detection method for chirp signals based on chaos theory, characterized in that It includes the following steps: S1: Reasonably set the oscillator parameters and threshold according to the target signal to be detected. S2: Input the signal to be detected. S3: Observe the phase output of the chaotic system and output the data distribution statistic. S4: Compare the statistic with the threshold to complete the detection.

2. The method for real-time detecting a chirp signal with high precision based on chaos theory according to claim 1, wherein In step S1, according to the target chirp signal to be detected, the chaotic oscillator parameters need to be adjusted. The expression of the chaotic oscillator is as follows: Where x is the displacement of the system, and are the second-order derivative and the first-order derivative of x respectively; a is the damping ratio, bx 3 -cx is the non-linear restoring force; F(t)cos(ω0 + k0t 2 ) is the internal driving force of the system, where ω0 is the initial angular frequency to be measured of the system, and k0 is the tuning frequency to be measured of the system; F1 and F2 are two system constants. By setting appropriate parameters, the chaotic system is in a critical state.

3. The method for real-time detecting a chirp signal with high precision based on chaos theory according to claim 2, wherein In step S2, the signal to be detected can be expressed as: y(t) = A·s(t) + n(t) Where s(t) is the target chirp signal, A is the amplitude of the target chirp signal, and n(t) is Gaussian additive white noise. y(t) is the signal received by the chaotic system. The expression of the target chirp signal to be detected is as follows: s(t) = cos(ω s t + k s t 2 ) where ω s is the initial angular frequency of the chirp signal, and k s is the chirp rate of the chirp signal. To detect the target signal, it is necessary to set the initial angular frequency to be measured and the chirp rate to be measured of the system to be the same as those of the target signal, ω0 = ω s , k0 = k s .

4. A method for real-time detecting a chirp signal with high precision based on chaos theory according to claim 3, wherein In step S3, the signal y(t) to be measured is input into the chaotic oscillator to solve for the system displacement x. When there is no target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 1, and its data distribution is biased to the left or right of zero. When there is a target signal in the signal to be measured, the phase diagram of the chaotic system shows a periodic state 2, and its data distribution is symmetric about zero. Statistically analyze the distribution of the system displacement x, and take the absolute value of its average as the data distribution statistic:

5. A method for real-time detecting a chirp signal with high precision based on chaos theory according to claim 4, characterized in that, The data distribution statistic M in step S4 x is compared with the set threshold γ as the detection result. When M x ≥γ, it means that there is no target signal in the detection signal. When M x <γ, it means that there is a target signal in the detection signal. Output the detection result to complete the detection.

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