An adaptive constant false alarm detection method for linear frequency modulation signals based on delayed autocorrelation

Abstract

The invention discloses an adaptive constant false alarm detection method for linear frequency modulation signals based on delayed autocorrelation, comprising: multiplying a received signal with a signal after delaying the received signal, converting the chirp signal into a single tone signal, performing a fast Fourier transform, converting the chirp signal into a frequency domain signal, and taking a modulus to obtain frequency domain data; taking each sampling point of the frequency domain data as a unit to be detected, selecting a front and a rear group of reference units for each unit to be detected, and splicing to obtain a reference sequence; judging the stationarity of the reference sequence, adaptively selecting to use a minimum constant false alarm detection algorithm or a maximum constant false alarm detection algorithm, and obtaining a decision threshold for each unit to be detected; judging whether the frequency domain data of each unit to be detected is greater than the decision threshold, if so, there is an LFM signal in the received signal; otherwise, there is no LFM signal. The invention improves the detection rate under low signal-to-noise ratio conditions while reducing the false alarm rate, and the algorithm complexity is low.

Description

Technical Field

[0001] The invention relates to the field of signal detection in non-cooperative underwater acoustic communication, and in particular to an adaptive constant false alarm detection method for linear frequency modulation signals based on delayed autocorrelation. Background Art

[0002] Non-cooperative underwater acoustic communication is an important research field in modern underwater communication technology and is widely used in many fields such as ocean exploration and submarine communication. In non-cooperative underwater acoustic communication, signal detection is particularly complicated due to the lack of prior information and synchronization mechanism. Effective signal detection technology can extract useful signals from complex noise backgrounds. Especially under low signal-to-noise ratio conditions, how to accurately detect weak signals and suppress noise interference has become the key to ensuring communication quality. Linear frequency modulation (LFM) signals have strong anti-interference capabilities due to their good autocorrelation characteristics and wide Doppler tolerance. They can provide precise time synchronization and are an important signal form in underwater communications. Therefore, LFM signal detection has become one of the core technologies in underwater acoustic signal detection. Among the existing LFM signal detection methods, although the fractional-order Fourier transform can improve the time-frequency resolution of the signal under certain conditions, its computational complexity is relatively high; the double sliding window algorithm has poor noise resistance and an extremely low detection rate in a low signal-to-noise ratio environment; although the existing constant false alarm rate detector (CFAR) algorithm has solved the problem of setting a fixed decision threshold to a certain extent, it still has a high false alarm probability in a complex ocean environment, resulting in a decrease in detection performance. Summary of the invention

[0003] After analyzing the shortcomings of the prior art, it is necessary to carry out research on blind detection technology of LFM signals in complex marine environments. The present invention proposes an adaptive constant false alarm detection method for linear frequency modulation signals based on delayed autocorrelation. The method can effectively suppress noise interference, maintain a low false alarm probability under low signal-to-noise ratio conditions, and improve the accuracy and robustness of LFM signal detection, and the algorithm complexity is low.

[0004] The specific technical solutions are as follows:

[0005] A linear frequency modulation signal adaptive constant false alarm detection method based on delayed autocorrelation, comprising the following steps:

[0006] S1. Perform delayed autocorrelation processing on the received signal: multiply the received signal by the signal after the received signal is delayed, so as to convert the chirp signal into a single tone signal;

[0007] S2: Perform fast Fourier transform on the received signal after delayed autocorrelation processing, convert it into a frequency domain signal and realize energy accumulation; modulus the frequency domain signal to obtain frequency domain data;

[0008] S3: Take each sampling point of the frequency domain data as a unit to be detected, select the front and rear groups of reference units for each unit to be detected; splice the two reference unit groups to obtain a reference sequence, judge its stability according to the variance of the reference sequence, adaptively select to use the minimum constant false alarm detection algorithm or the maximum constant false alarm detection algorithm, and obtain the decision threshold of each unit to be detected;

[0009] S4: Determine whether the frequency domain data of each unit to be detected obtained in S2 is greater than the decision threshold. If so, there is an LFM signal in the received signal; otherwise, there is no LFM signal in the received signal.

[0010] Further, in S1, when the target LFM signal exists in the received signal, the expression of the received signal is:

[0011]

[0012] Where r(t) represents the received signal, s(t) represents the target LFM signal, and w(t) represents Gaussian white noise.

[0013] Furthermore, in S1, the frequency of the target LFM signal changes linearly with time, which is expressed as:

[0014]

[0015]

[0016] Where A is the signal amplitude, f0 is the initial frequency of the signal, f1 is the end frequency of the signal, T is the duration of the signal, and μ is the frequency modulation slope.

[0017] Furthermore, in S1, the received signal is subjected to delayed autocorrelation processing, and the chirp signal is converted into a single tone signal by the following expression:

[0018]

[0019] In the formula, τ represents the time delay; let

[0020]

[0021] The above formula is simplified to .

[0022] Furthermore, in S2, the received signal after delayed autocorrelation processing is subjected to fast Fourier transform and converted into a frequency domain signal, and the expression is as follows:

[0023]

[0024]

[0025]

[0026] In the formula, To obtain the frequency domain representation of the received signal after delayed autocorrelation and fast Fourier transform, for The frequency domain representation obtained by fast Fourier transform is When the energy accumulates, there are two impact peaks; for The frequency domain representation obtained by fast Fourier transform has a Gaussian shape in the spectrum; represents the impulse function;

[0027] The peak frequency of the frequency domain signal is .

[0028] Furthermore, S3 is specifically implemented through the following sub-steps:

[0029] (3.1) For each unit to be detected, a preceding reference unit group and a succeeding reference unit group are obtained, the total number of reference units is 2N, the preceding and succeeding units adjacent to the unit to be detected are protection units, the preceding N units of the preceding protection unit are used as the preceding reference unit group, and the succeeding N units of the succeeding protection unit are used as the succeeding reference unit group; the mean of the preceding reference unit group is calculated to be X, and the mean of the succeeding reference unit group is calculated to be Y;

[0030] (3.2) splicing the preceding reference unit group and the succeeding reference unit group into a reference sequence, and calculating the variance Var of the reference sequence;

[0031] (3.3) Compare the variance Var with the artificially set threshold T var The size of the reference sequence is used to determine the stationarity of the reference sequence; if Var>T var , it means that the signal is not stable, and the minimum constant false alarm detection algorithm is selected to obtain the background noise power Z=min(X,Y) of the unit to be detected; otherwise, it means that the signal is stable, and the maximum constant false alarm detection algorithm is selected to obtain the background noise power Z=max(X,Y) of the unit to be detected;

[0032] (3.4) The decision threshold of the unit to be detected is calculated according to the background noise power Z, and the expression is as follows:

[0033]

[0034] In the formula, α is the threshold factor.

[0035] A linear frequency modulation signal adaptive constant false alarm detection device based on time-delay autocorrelation comprises a memory and one or more processors, wherein the memory stores executable codes, and when the one or more processors execute the executable codes, they are used to implement the linear frequency modulation signal adaptive constant false alarm detection method based on time-delay autocorrelation.

[0036] A computer-readable storage medium stores a program, which, when executed by a processor, implements the linear frequency modulation signal adaptive constant false alarm detection method based on delayed autocorrelation.

[0037] The beneficial effects of the present invention are:

[0038] (1) The method of the present invention effectively reduces the interference of ocean environmental noise through the proposed delayed autocorrelation processing method, which can provide a reference for subsequent research in the field of signal detection.

[0039] (2) The method of the present invention improves the traditional constant false alarm detection algorithm by combining the advantages of minimum constant false alarm detection and maximum constant false alarm detection, thereby improving the detection rate and reducing the false alarm rate under low signal-to-noise ratio conditions.

[0040] (3) Compared with the traditional fractional Fourier transform method and double sliding window energy detection method, the method of the present invention improves the detection rate of LFM signals under low signal-to-noise ratio conditions. At the same time, the algorithm complexity of the method of the present invention is low and the burden on the system is small. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention is a flowchart of a method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation in an embodiment of the present invention.

[0042] Figure 2 4 is a flow chart of an adaptive constant false alarm algorithm (SOGO-CFAR) in an embodiment of the present invention.

[0043] Figure 3 It is a fractional domain diagram obtained after delayed autocorrelation processing and Fourier transform of a received signal containing an LFM signal when the signal-to-noise ratio is -16 dB in an embodiment of the present invention.

[0044] Figure 4 1 is a schematic diagram comparing the detection effects of the SO-CFAR, GO-CFAR, and SOGO-CFAR algorithms on the LFM signal in the embodiments of the present invention, wherein (a) is a schematic diagram showing the detection effect of the SO-CFAR algorithm on the LFM signal, (b) is a schematic diagram showing the detection effect of the GO-CFAR algorithm on the LFM signal, and (c) is a schematic diagram showing the detection effect of the SOGO-CFAR algorithm on the LFM signal.

[0045] Figure 5 is a time-frequency diagram of a received signal including noise and an LFM signal in an embodiment of the present invention.

[0046] Figure 6 It is a comparison chart of LFM detection rates of the method of the present invention, the fractional-order Fourier transform detection method, and the double sliding window energy detection method under different signal-to-noise ratios in the embodiments of the present invention.

[0047] Figure 7 It is a comparison diagram of false alarm rates of LFM detection using the method of the present invention, the fractional-order Fourier transform detection method, and the double sliding window energy detection method under different signal-to-noise ratios in the embodiments of the present invention.

[0048] Figure 8 It is a structural schematic diagram of a linear frequency modulation signal adaptive constant false alarm detection device based on delayed autocorrelation in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0050] In the following description, reference is made to “one specific embodiment”, which describes a subset of all possible embodiments, but it can be understood that “one specific embodiment” describes the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0051] Unless otherwise defined, all technical science and technology used herein have the same meaning as commonly understood by those skilled in the art of the present invention. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0052] like Figure 1 As shown, a linear frequency modulation signal adaptive constant false alarm detection method based on delayed autocorrelation includes the following steps:

[0053] S1: Delay autocorrelation processing is performed on the received signal, that is, the chirp signal is converted into a single tone signal by multiplying the received signal with the signal after the received signal is delayed, so as to improve the responsiveness to the signal and suppress the interference of noise.

[0054] Specifically, when the target LFM signal exists in the received signal, the expression of the received signal is:

[0055]

[0056] Where r(t) represents the received signal, s(t) represents the target LFM signal, and w(t) represents Gaussian white noise.

[0057] The frequency of the target LFM signal varies linearly with time, expressed as:

[0058]

[0059]

[0060] Where A is the signal amplitude, f0 is the initial frequency of the signal, f1 is the end frequency of the signal, T is the duration of the signal, and μ is the frequency modulation slope.

[0061] The received signal is subjected to delayed autocorrelation processing, and the chirp signal is converted into a single tone signal. The expression is as follows:

[0062]

[0063] In the formula, τ represents the time delay.

[0064] Let the two terms to be summed be , , then the above formula can be simplified to ,and:

[0065]

[0066] S2: Perform fast Fourier transform on the received signal after delayed autocorrelation processing, convert it into a frequency domain signal, and realize energy accumulation; modulus the frequency domain signal to obtain frequency domain data.

[0067] Specifically, the received signal after delayed autocorrelation processing is converted into the frequency domain by fast Fourier transform. The frequency domain signal expression is as follows:

[0068]

[0069]

[0070]

[0071] In the formula, To obtain the frequency domain representation of the received signal after delayed autocorrelation and fast Fourier transform, for The frequency domain representation obtained by fast Fourier transform will be Two impact peaks appear (i.e., energy accumulation occurs); for The frequency domain representation obtained by fast Fourier transform has a Gaussian shape in the spectrum; represents the impulse function.

[0072] The frequency domain data D(f) is obtained by taking the modulus of the frequency domain signal. The frequency domain data can be used for further analysis. If there is an LFM signal in the received signal, the transformed spectrum Two peaks will appear, thus achieving efficient signal detection. The frequency positions of the peaks are:

[0073]

[0074] In the formula, Indicates the peak frequency.

[0075] S3: Figure 2 As shown in FIG. 1 , each sampling point of the frequency domain data is taken as a unit to be detected, and two groups of reference units are selected for each unit to be detected. Specifically, the total number of reference units is 2N, and the two units adjacent to the unit to be detected are taken as protection units. Then the first N units of the first protection unit are taken as the first reference unit group. , the N subsequent units of the subsequent protection unit are used as the subsequent reference unit group , concatenate the preceding reference unit group and the succeeding reference unit group to obtain the reference sequence . The decision threshold of each unit to be detected is further obtained by adaptively selecting the Smallest Of Constant False Alarm Rate detector (hereinafter referred to as SO-CFAR) algorithm or the Greatest Of Constant False Alarm Rate detector (hereinafter referred to as GO-CFAR) algorithm through an adaptive constant false alarm algorithm (hereinafter referred to as SOGO-CFAR algorithm). This step dynamically adjusts the decision threshold for each unit to be detected in the frequency domain data to ensure constant false alarm detection under different signal-to-noise ratio conditions, especially to improve the detection performance under low signal-to-noise ratio conditions while reducing the false alarm rate.

[0076] This step is specifically implemented through the following sub-steps:

[0077] (3.1) For each unit to be detected, a preceding reference unit group and a succeeding reference unit group are obtained, and the mean value of the preceding reference unit group is calculated to be X, and the mean value of the succeeding reference unit group is calculated to be Y.

[0078] (3.2) Concatenate the preceding reference unit group and the succeeding reference unit group into a reference sequence, and calculate the variance Var of the reference sequence.

[0079] (3.3) Compare the variance Var with the artificially set threshold T varThe size of , judge the stationarity of the reference sequence. var When , it means that the signal is not stable, select the SO-CFAR algorithm, and get the background noise power Z=min(X,Y) of the unit to be detected; otherwise, it means that the signal is stable, select the GO-CFAR algorithm, and get the background noise power Z=max(X,Y) of the unit to be detected.

[0080] (3.4) The decision threshold of the unit to be detected is calculated according to the background noise power Z, and the expression is as follows:

[0081]

[0082] In the formula, α is the threshold factor.

[0083] S4: Compare the frequency domain data D of each unit to be detected obtained in S2 with the decision threshold T Z Compare and determine whether there is an LFM signal in the received signal. If D>T Z , then the LFM signal exists, otherwise it does not exist.

[0084] The present invention is described in detail below by applying the method of the present invention to the detection of LFM signals with different parameters under different signal-to-noise ratio conditions.

[0085] Example 1

[0086] Taking the LFM signal with a signal-to-noise ratio of -16 dB, a frequency modulation slope μ of 500 Hz / s, and an initial frequency f0 of 1 kHz as an example, the delay τ is set to 0.5 s, the sampling rate is set to 20 kHz, and the received signal containing the LFM signal set in this embodiment is subjected to delayed autocorrelation processing and Fourier transform to obtain the fractional domain diagram as shown in FIG. Figure 3 The existing SO-CFAR, GO-CFAR and SOGO-CFAR algorithms proposed in the present invention are respectively used to process the delayed autocorrelation and Fourier transformed received signal containing LFM signal (i.e. Figure 3 The detection effect of the received signal shown in Figure 4 As shown, it can be seen that the SO-CFAR algorithm is not at the actual peak frequency There are multiple false alarms at the GO-CFAR algorithm and the LFM signal cannot be detected. However, the SOGO-CFAR algorithm proposed in the present invention detects the LFM signal without generating false alarms, which indicates that the method of the present invention can improve the detection rate and reduce the false alarm rate under low signal-to-noise ratio conditions.

[0087] Example 2

[0088] In this embodiment, the frequency modulation slope of the LFM signal is randomly set to 500Hz / s, 800Hz / s, 1000Hz / s, and 1500Hz / s, the duration of the LFM signal is 2s, 3s, and 4s, and the initial frequency of the LFM signal is randomly set to 1kHz, 2kHz, 3kHz, 4kHz, and 5kHz. The sampling rate is 96000Hz, and all signals are composed of a 1s pure noise signal and a section of LFM signal with a superimposed noise signal; the signal-to-noise ratio is -17dB~10dB, the step size is 1dB, and the number of Monte Carlo times at each signal-to-noise ratio is 500. An example of a simulated signal time-frequency diagram is shown below. Figure 5 As shown, at this time, the signal-to-noise ratio is 0 dB, the frequency modulation slope of the LFM signal is 1500 Hz / s, the duration of the LFM signal is 4 s, and the initial frequency of the signal is 1 kHz.

[0089] for Figure 5 The LFM signal shown in the figure is detected by using three methods, namely, the Fractional Fourier Transform (FrFT) detection method, the double sliding window energy detection method, and the adaptive constant false alarm detection method for linear frequency modulation signals based on delayed autocorrelation proposed in the present invention, for each Monte Carlo experiment under each signal-to-noise ratio. The Fractional Fourier Transform detection method sets two different decision thresholds, and the double sliding window energy detection method sets three different decision thresholds. Finally, the detection rates of the LFM signals under different signal-to-noise ratios by the three methods are shown in the figure below: Figure 6 As shown, the detection rate is the ratio of the number of successful detections of the LFM signal to the total number of simulations in which the LFM signal exists. It can be seen that when the signal-to-noise ratio is -16dB~10dB, the detection rate of the LFM signal by the method of the present invention is higher than that of the fractional Fourier transform detection method, and the computational complexity of the method of the present invention is much lower than that of the fractional Fourier transform detection method; when the signal-to-noise ratio is -17dB~0dB, the detection rate of the LFM signal by the method of the present invention is higher than that of the double sliding window energy detection method, which further shows that the detection rate can be improved under low signal-to-noise ratio conditions.

[0090] The false alarm rate is defined as the ratio of the number of times a signal is detected at the non-detection signal target peak frequency or at the non-signal appearance time to the total number of sampling points when a signal exists (i.e., the probability of misjudging noise or other interference signals as threat radiation source signals). Finally, the false alarm rates of the three methods, namely, the FrFT detection method, the double sliding window energy detection method and the method of the present invention, for detecting LFM signals under different signal-to-noise ratio conditions are as follows: Figure 7 It can be seen that the false alarm rate of the method of the present invention is kept at 10 when the signal-to-noise ratio is -17dB~10dB. -7 ~10 -6The false alarm rate of the double sliding window energy detection method is 0 when the decision threshold is set to 1.5 and 1.8, and the false alarm rate is 10 when the decision threshold is set to 1.2. -7 However, it cannot detect LFM signals under low signal-to-noise ratio conditions. In summary, it can be concluded that the method of the present invention has better anti-noise performance and detection performance than the traditional method under low signal-to-noise ratio conditions.

[0091] On the basis of the above-mentioned method for adaptive constant false alarm detection of linear frequency modulation signal based on time-delay autocorrelation, the present invention further provides an adaptive constant false alarm detection device for linear frequency modulation signal based on time-delay autocorrelation.

[0092] like Figure 8 As shown, an embodiment of the present invention provides a linear frequency modulation signal adaptive constant false alarm detection device based on delay autocorrelation, including a memory and one or more processors, wherein executable codes are stored in the memory, and when the one or more processors execute the executable codes, they are used to implement the linear frequency modulation signal adaptive constant false alarm detection method based on delay autocorrelation in the above embodiment.

[0093] The present invention provides a linear frequency modulation signal adaptive constant false alarm detection device based on delayed autocorrelation, which can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer. The linear frequency modulation signal adaptive constant false alarm detection device based on delayed autocorrelation can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located, reading the corresponding computer program instructions in the non-volatile memory into the internal memory and running them. From the hardware level, if Figure 8 As shown in the figure, it is a hardware structure diagram of a device with data processing capability in which a linear frequency modulation signal adaptive constant false alarm detection device based on delayed autocorrelation of the present invention is located. Figure 8 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus of the present invention is located in the embodiments may also include other hardware, which will not be described in detail herein, based on the actual functions of the device with data processing capabilities.

[0094] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0095] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only exemplary, and the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0096] An embodiment of the present invention further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation in the above embodiment is implemented.

[0097] The computer-readable storage medium may be an internal storage unit of any device with data processing capability in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store computer programs and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0098] The method of the present invention utilizes delayed autocorrelation to convert chirp signals into single-tone signals, utilizes fast Fourier transform to realize energy accumulation, and dynamically adjusts the decision threshold through an adaptive constant false alarm detection algorithm, thereby realizing high-accuracy LFM signal detection under low signal-to-noise ratio conditions. The method of the present invention is suitable for detecting linear frequency modulation signals in marine environments, especially in the presence of complex noise interference, and can effectively improve the performance of signal detection.

[0099] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0100] The above description is only the preferred embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations, and the various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A linear frequency modulation signal adaptive constant false alarm detection method based on delayed autocorrelation, characterized in that: The following steps are involved: S1. Perform delayed autocorrelation processing on the received signal: multiply the received signal by the signal after the received signal is delayed, so as to convert the chirp signal into a single tone signal; S2: Perform fast Fourier transform on the received signal after delayed autocorrelation processing, convert it into a frequency domain signal and realize energy accumulation; modulus the frequency domain signal to obtain frequency domain data; S3: Take each sampling point of the frequency domain data as a unit to be detected, select the front and rear groups of reference units for each unit to be detected; splice the two reference unit groups to obtain a reference sequence, judge its stability according to the variance of the reference sequence, adaptively select to use the minimum constant false alarm detection algorithm or the maximum constant false alarm detection algorithm, and obtain the decision threshold of each unit to be detected; S4: Determine whether the frequency domain data of each unit to be detected obtained in S2 is greater than the decision threshold. If so, there is an LFM signal in the received signal; otherwise, there is no LFM signal in the received signal; The S3 is specifically implemented through the following sub-steps: (3.1) For each unit to be detected, a preceding reference unit group and a succeeding reference unit group are obtained, the total number of reference units is 2N, the preceding and succeeding units adjacent to the unit to be detected are protection units, the preceding N units of the preceding protection unit are used as the preceding reference unit group, and the succeeding N units of the succeeding protection unit are used as the succeeding reference unit group; Calculate the mean value of the preceding reference unit group as X, and the mean value of the succeeding reference unit group as Y; (3.2) concatenating the preceding reference unit group and the succeeding reference unit group into a reference sequence, and calculating the variance Var of the reference sequence; (3.3) Compare the variance Var with the artificially set threshold T var The size of the reference sequence is used to determine the stationarity of the reference sequence; if Var>T var , it means that the signal is not stable, and the minimum constant false alarm detection algorithm is selected to obtain the background noise power Z=min(X,Y) of the unit to be detected; otherwise, it means that the signal is stable, and the maximum constant false alarm detection algorithm is selected to obtain the background noise power Z=max(X,Y) of the unit to be detected; (3.4) The decision threshold of the unit to be detected is calculated according to the background noise power Z, and the expression is as follows: T Z =Zα In the formula, α is the threshold factor.

2. The method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to claim 1, characterized in that: In S1, when the target LFM signal exists in the received signal, the expression of the received signal is: r(t)=s(t)+w(t) Where r(t) represents the received signal, s(t) represents the target LFM signal, and w(t) represents Gaussian white noise.

3. The method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to claim 2, characterized in that: In S1, the frequency of the target LFM signal changes linearly with time, which is expressed as: s(t)=Acos(2πf0t+πμt 2 ),0≤t≤T Where A is the signal amplitude, f0 is the initial frequency of the signal, f1 is the end frequency of the signal, T is the duration of the signal, and μ is the frequency modulation slope.

4. The method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to claim 3, characterized in that: In S1, the received signal is subjected to delayed autocorrelation processing, and the chirp signal is converted into a single tone signal. The expression is as follows: In the formula, τ represents the delay; make The above formula is simplified to y(t)=y1(t)+y2(t).

5. The method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to claim 4, characterized in that: In S2, the received signal after delayed autocorrelation processing is subjected to fast Fourier transform and converted into a frequency domain signal, which is expressed as follows: S(f)=S1(f)+S2(f) Where, S(f) is the frequency domain representation obtained by performing delayed autocorrelation and fast Fourier transform on the received signal, S1(f) is the frequency domain representation obtained by fast Fourier transform on y1(t), which has energy accumulation when f=±μτ and has two impulse peaks; S2(f) is the frequency domain representation obtained by fast Fourier transform on y2(t), whose spectrum has a Gaussian shape; δ(f) represents the impulse function; The peak frequency of the frequency domain signal is f peak =±μτ.

6. A linear frequency modulation signal adaptive constant false alarm detection device based on delayed autocorrelation, characterized in that: The invention comprises a memory and one or more processors, wherein the memory stores executable codes, and when the one or more processors execute the executable codes, the method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the method for adaptive constant false alarm detection of linear frequency modulation signals based on delayed autocorrelation according to any one of claims 1 to 5 is implemented.

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