A chirp rate estimation method for linear frequency modulation jamming

Abstract

The application discloses a method for estimating the sweep period of linear frequency modulation interference, and is mainly used for estimating the sweep period of linear frequency modulation interference in the field of signal processing and the like. The method can preset a transform order and a calculation parameter, generates a coefficient required for fractional order Fourier transform calculation, and does not need to normalize the dimension of input data in advance. According to different transform orders, fractional order Fourier transform calculation is performed on input data, until the average peak value interval is calculated after multiple peak values appear in the transform domain. Finally, the sweep period is estimated according to the current searched transform order and the average peak value interval. The method greatly reduces the calculation complexity of the sweep period estimation, multiplexes the anti-interference hardware architecture, saves the hardware resources, does not need to normalize the dimension of input data in advance, increases the compatibility of the method, and does not need to search the period with a fixed length, thereby reducing the calculation amount and increasing the estimation accuracy.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, and in particular relates to a method for estimating the sweep period of linear frequency modulation interference. Background Technology

[0002] Linear Frequency Modulation (LFM) signals are signals whose frequency changes linearly with time. LFM jamming signals are a typical type of suppression-based broadband jamming signal, belonging to the frequency sweep jamming category. Traditional LFM jamming methods often use fractional Fourier transform (FRFT) for parameter estimation. However, LFM jamming signals are not cooperative signals like radar signals, and anti-jamming receivers often need to estimate parameters such as the period and modulation frequency of the LFM jamming signal. LFM jamming signals in the battlefield often have a certain frequency sweep period, broadcasting jamming in a multi-cycle manner. Traditional FRFT cannot focus multi-cycle LFM signals into a single energy pulse because the kernel function of the FRFT cannot match the periodic LFM signal well, making its detection suboptimal.

[0003] Therefore, in traditional fractional Fourier transform anti-interference methods, the estimation of the period of linear frequency modulated (LFM) interference signals generally adopts a blind search estimation method, which is a two-dimensional search process. This method pre-sets the corresponding search range and search step size. After selecting a period each time, the data is windowed, and then the windowed data is subjected to fractional Fourier transform detection according to different search orders until the parameters are adjusted to obtain an accurate period and transform order. This allows the signal to converge into an energy pulse in the fractional Fourier domain, thereby estimating the relevant period, frequency modulation, and other parameters. The advantage of this method is its ease of implementation, but the disadvantage is that the computational complexity in the fractional Fourier domain is inherently high, so the cost of two-dimensional search increases exponentially, resulting in a large computational load, long processing time, and unsuitability for rapid implementation. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a sweep frequency period estimation method for linear frequency modulation interference. This method aims to solve the problems of low detection success rate and large computational load in existing blind search period algorithms. The method proposed in this invention has a high period detection success rate, while the computational load is relatively small, and it has good robustness.

[0005] To address the aforementioned technical problems, this invention discloses a method for estimating the sweep period of linear frequency modulation interference, comprising:

[0006] Step 1: Determine the three linear frequency modulated signals ChirpA required for the current order of the fractional Fourier transform. α [n]、ChirpBα [n]、ChirpC α [n] and the normalized constant C a ;

[0007] Step 2, based on the determined fractional Fourier transform, the required ChirpA is... α [n]、ChirpB α [n] and ChirpC α [n], perform a fractional Fourier transform on the input linear frequency modulation interference signal to obtain the fractional Fourier transform result at the current transform order;

[0008] Step 3: Perform peak search on the fractional Fourier transform result under the current transform order to determine the number of peaks Num_Peak for the current transform order;

[0009] Step 4: Determine the number of peaks Num_Peak in the current transform order; if Num_Peak > 1, it indicates that the linear frequency modulation interference signal has multiple periods and needs to be segmented. After recording the fractional Fourier transform result under the current transform order, calculate the average value Δu of the interval Δu between each peak. avg Proceed to step 5;

[0010] Step 5: Calculate the estimated modulation frequency of the linear frequency modulation interference signal based on the transformation angle α of the current transformation order.

[0011] Step 6, based on Δu avg and The sweep period of the linear frequency modulated interference signal was calculated.

[0012] Step 7: Repeat steps 1 to 6 to complete the calculation of all transformation orders, obtain all sweep frequency cycles and output them.

[0013] In the above method for estimating the sweep period of linear frequency modulation interference, α=(a×π) / 2; where a represents the transform order in the fractional Fourier transform search process, a∈[0,2].

[0014] In the above method for estimating the sweep period of linear frequency modulation interference, the transformation order of 'a' in each cycle is: 0, Δa, 2Δa, 3Δa, ..., 2; where Δa represents the step size, Δa = 0.01.

[0015] In the above-mentioned method for estimating the sweep period of linear frequency modulation interference, ChirpA α [n]、ChirpB α [n] and ChirpC α [n] represents the following respectively:

[0016]

[0017]

[0018]

[0019]

[0020] Where n represents each discrete signal point of the linear frequency modulation interference signal in the time domain sampling interval, and Δx represents the sampling interval of the linear frequency modulation interference signal in the time domain.

[0021] In the above-mentioned method for estimating the sweep period of linear frequency modulation interference, the ChirpA required based on the determined fractional Fourier transform current transform order is... α [n]、ChirpB α [n]、ChirpC α [n] and C a The input linear frequency modulation interference signal is subjected to a fractional Fourier transform to obtain the fractional Fourier transform result at the current transform order, including:

[0022] The ChirpA corresponding to the current order after doubling the interpolation of the linear frequency modulation interference signal. α Multiplying [n] together yields the first temporary variable value t. a [n]:

[0023]

[0024] in, This represents the time-domain discrete signal after doubling the interpolation of the linear frequency modulated interference signal.

[0025] t a [n] is the ChirpB corresponding to the current order. α Perform a convolution operation on [n] to obtain the value of the second temporary variable T. a [m]:

[0026]

[0027] Where m represents each discrete signal point in the fractional domain, m∈[-N,N], and N represents the total number of points after sampling the original signal;

[0028] T a [m] is the ChirpC corresponding to the current order. α Perform a product operation on [n] to obtain the value of the third temporary variable F. a [m]:

[0029] F a[m] = T a [m]×ChirpC α [n]

[0030] F a [m] is the C corresponding to the current order. a Perform the product operation to obtain the value of the fourth temporary variable F. a [m′]:

[0031] F a [m′]=C a ×F a [m]

[0032] Where m′ represents the sampling points after doubling the extraction rate;

[0033] F a [m′] is the output of the fractional Fourier transform result under the current transform order.

[0034] In the above-mentioned method for estimating the sweep period of linear frequency modulation interference, The solution formula is shown below:

[0035]

[0036] Among them, f s T represents the sampling rate. obs Indicates the local observation time.

[0037] In the above-mentioned method for estimating the sweep period of linear frequency modulation interference, The solution formula is shown below:

[0038]

[0039] Where, N sample N represents the data length of the linear frequency modulation interference signal. sample =f s ×T obs .

[0040] The above-mentioned method for estimating the sweep period of linear frequency modulation interference also includes: pre-storing f s T obs N sample a, ChirpA α [n]、ChirpB α [n]、ChirpC α [n] and C a This is to improve the numerical computation speed of fractional Fourier transform.

[0041] In the above method for estimating the sweep period of linear frequency modulation interference, in step 4, if Num_Peak = 0, then return to step 2 and perform a new round of transformation order calculation.

[0042] In the above method for estimating the sweep period of linear frequency modulation interference, in step 4, if Num_Peak = 1, it means that the linear frequency modulation interference signal has only one sweep period and does not need to be segmented. According to steps 5 to 6, the sweep period of the linear frequency modulation interference signal is calculated and output.

[0043] The present invention has the following advantages:

[0044] (1) This invention discloses a sweep frequency period estimation method for linear frequency modulation interference, which combines fractional Fourier transform and period estimation. It utilizes the property that the linear frequency modulation interference signal exhibits a fixed peak value and is extended with a specific period in the fractional transform domain to quickly estimate the interference period of the linear frequency modulation interference signal. The original two-dimensional search process is transformed into a one-dimensional search process of optimal order search of fractional Fourier transform, which greatly reduces the computational complexity and search time.

[0045] (2) This invention discloses a method for estimating the sweep period of linear frequency modulation interference. It reuses the relevant system architecture of fractional Fourier transform anti-interference, which is easy to implement, reduces the complexity of system design, and saves hardware space.

[0046] (3) This invention discloses a method for estimating the sweep period of linear frequency modulation interference. When using the fractional Fourier transform algorithm, a method is adopted to transform the output data for dimensional normalization. The variables required for the transformation are relatively easy to obtain, and the system has good compatibility with the input data, thus making the system highly robust.

[0047] (4) This invention discloses a sweep frequency period estimation method for linear frequency modulation interference. It does not require prior assumptions about the space and step size of the period search. Instead, it directly calculates the corresponding period length by the interval between peaks in a specific transform domain. Compared with the blind search estimation method, it can obtain higher period estimation accuracy with less resource consumption. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a method for estimating the sweep period of linear frequency modulation interference in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating another method for estimating the sweep period of linear frequency modulation interference in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments disclosed in the present invention will be described in further detail below with reference to the accompanying drawings.

[0051] One of the core ideas of this invention is to address the shortcomings of existing blind search estimation methods by proposing a frequency sweep period estimation method for linear frequency modulated interference based on fractional Fourier transform. This method utilizes the property that after a periodic linear frequency modulated interference signal undergoes a fractional Fourier transform of a specific order, it manifests as multiple energy pulses in the fractional transform domain with fixed spacing. The spacing is related to the modulation frequency and the period. Since the modulation frequency is related to the current specific order, this characteristic can be used to estimate the period parameter of the current linear frequency modulated interference signal.

[0052] In this embodiment, as Figure 1 The method for estimating the sweep period of this linear frequency modulation interference includes:

[0053] Step 1: Determine the three linear frequency modulated signals ChirpA required for the current order of the fractional Fourier transform. α [n]、ChirpB α [n]、ChirpC α [n] and the normalized constant C a .

[0054] In this embodiment, the range of the transformation order 'a' in the fractional Fourier transform search process can be pre-set to a∈[0,2]; the transformation order of 'a' in each loop is sequentially: 0, Δa, 2Δa, 3Δa, ..., 2. Therefore, the transformation order in each loop can be set according to a certain step size Δa, where Δa = 0.01. Based on the transformation order in each loop, the three linear frequency modulation signals ChirpA can be quickly determined. α [n]、ChirpB α [n]、ChirpC α [n] and the normalized constant C a :

[0055]

[0056]

[0057]

[0058]

[0059] Where α represents the transformation angle, α=(a×π) / 2; n represents each discrete signal point of the linear frequency modulation interference signal in the time domain sampling interval, and Δx represents the sampling interval of the linear frequency modulation interference signal in the time domain.

[0060] Step 2, based on the determined fractional Fourier transform, the required ChirpA is... α [n]、ChirpB α [n] and ChirpC α [n] performs a fractional Fourier transform on the input linear frequency modulation interference signal to obtain the fractional Fourier transform result at the current transform order.

[0061] In this embodiment, the specific implementation process of performing a fractional Fourier transform on the input linear frequency modulated interference signal is as follows:

[0062] The ChirpA corresponding to the current order after doubling the interpolation of the linear frequency modulation interference signal. α Multiplying [n] together yields the first temporary variable value t. a [n]:

[0063]

[0064] in, This represents the time-domain discrete signal after doubling the interpolation of the linear frequency modulation interference signal.

[0065] t a [n] is the ChirpB corresponding to the current order. α [n] is used for convolution. Here, two Fast Fourier Transforms plus one Inverse Transform can be used for fast computation to obtain the value of the second temporary variable T. a [m]:

[0066]

[0067] Where m represents each discrete signal point in the fractional domain, m∈[-N,N], and N represents the total number of points after sampling the original signal.

[0068] T a [m] is the ChirpC corresponding to the current order. α Perform a product operation on [n] to obtain the value of the third temporary variable F. a [m]:

[0069] F a [m] = T a [m]×ChirpC α [n]

[0070] F a [m] is the C corresponding to the current order. a Perform the product operation to obtain the value of the fourth temporary variable F. a [m′]:

[0071] F a [m′]=Ca ×F a [m]

[0072] Where m′ represents the sampling points after doubling the extraction rate;

[0073] F a [m′] is the output of the fractional Fourier transform result under the current transform order.

[0074] Step 3: Perform peak search on the fractional Fourier transform result under the current transform order to determine the number of peaks Num_Peak for the current transform order.

[0075] Step 4: Determine the number of peaks Num_Peak for the current transformation order.

[0076] In this embodiment, if Num_Peak > 1, it indicates that the linear frequency modulation interference signal has multiple periods and needs to be segmented. After recording the fractional Fourier transform result at the current transform order, the average value Δu of the interval Δu between each peak is calculated. avg Proceed to step 5. If Num_Peak = 0, return to step 2 and perform a new round of transformation order calculation. If Num_Peak = 1, it indicates that the linear frequency modulation interference signal has only one sweep period and does not need to be segmented. Calculate and output the sweep period of the linear frequency modulation interference signal according to steps 5-6.

[0077] Step 5: Calculate the estimated modulation frequency of the linear frequency modulation interference signal based on the transformation angle α of the current transformation order.

[0078] In this embodiment, The solution formula is shown below:

[0079]

[0080] Among them, f s T represents the sampling rate. obs Indicates the local observation time.

[0081] Step 6, based on Δu avg and The sweep period of the linear frequency modulated interference signal was calculated.

[0082] In this embodiment, The solution formula is shown below:

[0083]

[0084] Where, N sample N represents the data length of the linear frequency modulation interference signal. sample=f s ×T obs .

[0085] Step 7: Repeat steps 1 to 6 to complete the calculation of all transformation orders, obtain all sweep frequency cycles and output them.

[0086] It should be noted that the f used in this embodiment s T obs N sample a, ChirpA α [n]、ChirpB α [n]、ChirpC α [n] and C a Parameters can be pre-stored to improve the numerical calculation speed of fractional Fourier transform.

[0087] In summary, this invention discloses a method for estimating the sweep period of linear frequency modulation (LFM) interference. When estimating interference parameters, the original two-dimensional search process for the sweep period and the optimal order of the fractional Fourier transform is transformed into a one-dimensional search process for the optimal order of the fractional Fourier transform, significantly reducing computational complexity and search time. Secondly, the estimation of the period parameter is consistent with the architecture of the fractional Fourier calculation required for anti-interference, eliminating the need for additional hardware area and saving hardware resources. Furthermore, no data preprocessing is required before performing the fractional Fourier transform; dimensional normalization is performed only after processing, resulting in better system compatibility. Finally, instead of searching for a fixed-length period, estimation is performed using the interval between peaks in a specific transform domain, reducing computational load while increasing estimation accuracy.

[0088] Based on the above embodiments, the following is an explanation using a specific example.

[0089] like Figure 2 The specific implementation process of the frequency sweep period estimation method for linear frequency modulation interference is as follows:

[0090] Step a: Pre-define the range of the transformation order 'a' in the fractional Fourier transform search process: a∈[0,2]; set the step size of 'a' to be Δa=0.01, and 'a' starts from 0 and ends at 2. The transformation order in each loop is called 'a'. i .

[0091] Step b, pre-set the following calculation parameters: local observation time T obs Local sampling interval 1 / 2Δx, sampling rate f s Therefore, the data length N of the input linear frequency modulation interference signal sample For N sample =f s×T obs .

[0092] Step c: Based on the transformation order determined in each loop in step a, quickly generate the ChirpA required for the fractional Fourier transform. α [n]、ChirpB α [n]、ChirpC α [n] and the normalized constant C a :

[0093]

[0094]

[0095]

[0096] Since sgn(sin(α)) = 1 in the range a ∈ (0.5, 1), the normalization constant can be written as:

[0097] To accelerate the calculation process, the relevant constant parameters mentioned above can be stored in advance according to Δa in step a, thereby speeding up the numerical calculation of the fractional Fourier transform. The method of this invention pre-sets a large step size for the search order, thus allowing for the pre-calculation of the constant parameters of each order of fractional Fourier transform, thereby accelerating the calculation process.

[0098] Step d involves using the different transformation orders obtained from the step size Δa in step a and the computational constants stored in step c to perform fractional Fourier transform calculations at each order. This process is divided into four sub-steps:

[0099] Sub-step d1 involves performing a doubling interpolation on the linear frequency modulated interference signal and then matching it with the ChirpA signal corresponding to the current order. α Multiplying [n] together yields the first temporary variable value t. a [n]:

[0100]

[0101] Sub-step d2, t a [n] is the ChirpB corresponding to the current order. α Perform a convolution operation on [n] to obtain the value of the second temporary variable T. a [m]:

[0102]

[0103] Sub-step d3, T a [m] is the ChirpC corresponding to the current order. α Perform a product operation on [n] to obtain the value of the third temporary variable F. a [m]:

[0104] F a [m] = T a [m]×ChirpC α [n]

[0105] Sub-step d4, F a [m] performs a double extraction and is matched with the C corresponding to the current order. a Perform the product operation to obtain the value of the fourth temporary variable F. a [m′]:

[0106] F a [m′]=C a ×F a [m]

[0107] Step e: Perform peak search on the fractional Fourier transform result under the current transform order to determine the number of peaks Num_Peak for the current transform order.

[0108] In this embodiment, because periodic linear frequency modulation interference produces periodically varying peaks in the fractional Fourier domain, and multiple secondary peaks exist in the spectral lines adjacent to the peaks, a multi-peak detection method suitable for this approach is designed as follows: A detection correlation coefficient is set, with the secondary peak detection range SubPeaks_Range = 0.33. A larger value increases the probability of detecting the true peak, but an excessively large value will lead to a reduction in peak values, while a smaller value may result in failure to determine secondary peaks. The noise detection range Noise_Range = 0.1, where this coefficient multiplied by the maximum signal value serves as a threshold for determining whether something is noise. The possible peak determination range Peaks_Range = 0.8, which is the threshold for peak evaluation values ​​calculated in subsequent steps. These selected peaks will be used to determine whether they are secondary peaks, ultimately selecting each peak. The calculation results are iterated once to find the maximum peak Max_Signal. The calculation results are iterated again to calculate the average noise value, Noise_avg: Initially, the average noise value is set to 0. During the iteration, if the current signal value satisfies Signal(i) < Max_Signal × Noise_avg, it is considered noise, and the average noise value, Noise_avg, is updated incrementally. The peak evaluation value, Peak_Evaluation(i), is calculated by evaluating the probability of the non-noise points from the previous step becoming peaks. The evaluation formula is as follows:

[0109] Peak_Evaluation(i)=abs(Signal(i)-Noise_avg) / (Max_Signal-Noise_avg)

[0110] Secondary peak detection is performed: each selected peak is traversed, and it is determined whether the current peak is a primary peak based on the set peak retrieval range. After the traversal is completed, the positions and sizes of all primary peaks are obtained.

[0111] This implementation method offers more degrees of freedom compared to the conventional hard threshold selection method. While maintaining appropriate computational complexity, it can select the positions of each main peak generated by the periodic linear frequency modulated signal in the fractional domain and shield the influence of surrounding secondary peaks. Furthermore, it employs an averaging method to reduce the impact of false detections on subsequent period estimation.

[0112] Step f involves determining the number of peaks, Num_Peak, in the current transform order. If Num_Peak = 0, proceed to step d for a new round of order calculation. If Num_Peak = 1, it indicates that the input linear frequency modulation (LFM) interference signal has only one sweep cycle, requiring no segmentation. Record the current order for subsequent anti-interference processing and proceed to step d for a new round of order calculation. If Num_Peak > 1, it indicates that the current input LFM interference signal has multiple sweep cycles, requiring segmentation. Record the current transform order and calculate the average value of the intervals between each peak, denoted as Δu. avg Proceed to step g.

[0113] Step g: Calculate the estimated modulation frequency of the linear frequency modulated interference signal based on the transformation angle α of the current transformation order.

[0114] Step h, based on N in step b sample The interval Δu between the current transform order in step e and the peak value in step f. avg Estimating the frequency modulation in step g7 Calculate the sweep period

[0115] At this point, the period of one component of the linear frequency modulation interference signal is estimated. If the order search in step d is not completed, the process jumps to step d to search for other components. If the search is completed, all period results are output.

[0116] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

[0117] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method of chirp-scan period estimation of a linear frequency modulation jamming, characterized in that, Comprising: Step 1, determining three kinds of chirp signals ChirpA required by the fractional Fourier transform of the current transform order α [n], ChirpB α [n], ChirpC α [n] and the normalization constant C a ; wherein n represents each discrete signal point of the linear frequency modulation interference signal in the time domain sampling interval, and a represents the transform order in the fractional Fourier transform search process. Step 2, Chirp A required for the fractional Fourier transform of the current transform order based on the determined score α [n], Chirp B α [n] and Chirp C α [n], the fractional Fourier transform of the input linear frequency modulation interference signal is carried out to obtain the fractional Fourier transform result under the current transform order; Step 3, peak value search is carried out on the fractional Fourier transform result under the current transform order to determine the peak value number Num_Peak of the current transform order; Step 4, judging the number of peaks Num_Peak of the current transform order; wherein, if Num_Peak>1, it indicates that there are multiple periods of the linear frequency modulation interference signal, and the signal needs to be segmented, the fractional Fourier transform result under the current transform order is recorded, and the average value Δu of the interval Δu between each peak is calculated avg Step 5 is executed. Step 5, the estimated frequency of the chirp signal is calculated according to the transform angle a of the current transform order Step 6, according to Δu avg and The sweep period of the chirp jamming signal is calculated Step 7, steps 1-6 are repeated to complete the calculation of all transform orders to obtain all sweep periods and output.

2. The chirp rate estimation method of linear frequency modulation interference according to claim 1, characterized in that, Alpha=(a*Pi) / 2, a belongs to [0, 2].

3. The chirp rate estimation method of linear frequency modulation interference according to claim 2, characterized in that, The transform order of a in each cycle is: 0, delta a, 2*delta a, 3*delta a,..., 2; wherein, delta a represents a step, delta a=0.

01.

4. The chirp rate estimation method of linear frequency modulation interference according to claim 2, characterized in that, ChirpA α [n], ChirpB α [n] and ChirpC α [n] are represented as follows: Wherein, delta x represents the sampling interval of the linear frequency modulation interference signal in the time domain.

5. The chirp rate estimation method of linear frequency modulation interference according to claim 4, characterized in that, ChirpA α [n]、ChirpB α [n]、ChirpC α [n] and C a performing fractional Fourier transform on the input linear frequency modulation interference signal to obtain the fractional Fourier transform result under the current transform order, comprising: Chirp A corresponding to the current order after twice interpolation of the linear frequency modulation interference signal α [n] multiplication, get the first temporary variable value t a [n]: wherein represents a time domain discrete signal after twice interpolation of the linear frequency modulation interference signal; t a [n] corresponds to the current order ChirpB α [n] is convoluted to obtain the second temporary variable value T a [m]: Wherein, m represents each discrete signal point in the fractional order domain, m belongs to [-N, N], N represents the total number of points after sampling of the original signal; T a [m] corresponds to the ChirpC α [n] is multiplied to obtain the third temporary variable value F a [m]: F a [m] = T a [m] x ChirpC α [n] F a [m] C a F a [m′] F a [m′] = C a × F a [m] Wherein, m' represents the sampling point after two times of extraction; F a [m'] as a fractional Fourier transform result at the current transform order.

6. The chirp rate estimation method of linear frequency modulation interference according to claim 1, characterized in that, The solution formula is shown below: where f s represents the sampling rate, T obs represents the local observation time.

7. The chirp rate estimation method of linear frequency modulation interference according to claim 6, characterized in that, The solution formula is shown below: wherein N sample represents the data length of the linear frequency modulation interference signal, N sample = f s × T obs .

8. The chirp rate estimation method of linear frequency modulation interference according to claim 7, characterized in that, Further comprising: Pre-stored f s , T obs , N sample , a, ChirpA α [n], ChirpB α [n], ChirpC α [n] and C a to improve the numerical calculation speed of fractional Fourier transform.

9. The chirp rate estimation method of linear frequency modulation interference according to claim 1, characterized in that, In step 4, if Num_Peak=0, return to step 2 to perform a new round of transform order calculation.

10. The method of chirp rate estimation of a linear frequency modulated interference according to claim 1, characterized in that, In step 4, if Num_Peak=1, it indicates that the linear frequency modulation interference signal has only one sweep period, and there is no need to segment, according to steps 5-6, the sweep period of the linear frequency modulation interference signal is calculated and output.

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