IFF signal identification and classification method for anti-tacon interference based on feature signal matching

Abstract

The application discloses an IFF signal identification and classification method based on feature signal matching anti-Tacon interference, and is used for solving the problems of low IFF signal identification and classification accuracy and difficulty in identifying IFF signals interfered by Tacon signals. The implementation steps of the application comprise the following steps: using the prior information of IFF signals, pre-extracting the feature signals of various types of IFF signals, using the correlation degree between the signals to be identified and the IFF feature signals for identification and classification, in addition, using the difference between the rising and falling edge duration of the Tacon signal pulse and the IFF signal pulse, a method for recovering the original IFF signal of the interfered IFF signal through first-order difference is proposed. The application overcomes the problems of few identifiable types of IFF signals, poor identification effect of low signal-to-noise ratio and difficulty in parameter fusion discrimination in the prior art, improves the identification accuracy and the robustness of the method, and has the ability of identifying and classifying IFF signals under the condition of Tacon interference, thereby expanding the application range of the method.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of communication, and further relates to an IFF signal identification and classification method based on feature signal matching anti-tacon interference. BACKGROUND

[0002] Low-orbit satellites have the ability to capture a large number of IFF signals due to their wide reconnaissance range, but also introduce signal interference problems. The main source of interference is the Tacan signal, which is a set of air navigation system signals. The Tacan signal has a working bandwidth of 1 MHz, and the channels are distributed from 1000 MHz to 1200 MHz, which conflicts with the two frequency bands of 1030 MHz and 1090 MHz where the IFF signal is located. Since the ground signals have similar degrees of attenuation when reaching the satellite, once there is interference, there is a high probability that the signal-to-interference ratio of the received signal will be low, and the IFF signal will be severely disturbed, making it difficult to identify and classify.

[0003] Hunan Aikonever Technology Co., Ltd. in its applied patent document "IFF pulse signal classification extraction method" (application number: CN 202011624785.1, application date: 2020.12.30, application publication number: CN 112766064A) discloses an IFF pulse signal classification extraction method. The method is mainly based on the idea of signal energy detection. The method calculates the quarter mean value of the received signal amplitude, sets it as a pulse coarse detection threshold, detects data greater than the threshold as a pulse, and extracts a fine detection threshold by clustering the instantaneous power of each pulse. The received IFF signal is detected again using the pulse fine detection threshold to measure the pulse center position, pulse width, pulse amplitude and other pulse modulation parameters for classification and extraction of IFF signals. However, the method still has many shortcomings: first, the method measures too few parameters, only realizes the identification and classification of the IFF signal system level, and is not sufficient to support the identification of multiple IFF signal types; second, the method is based on the traditional energy detection idea, and the signal is detected by the signal instantaneous power, amplitude, etc. When the instantaneous amplitude of the low signal-to-noise ratio IFF signal fluctuates greatly, the fixed detection threshold set by the amplitude mean value and the instantaneous power clustering method is not suitable for low signal-to-noise ratio conditions, so the parameter measurement accuracy is low in low signal-to-noise ratio conditions; third, the method identifies and classifies the IFF signal by matching multiple parameters. If each parameter is independently judged, the accuracy of the parameter measurement method is extremely high. If the parameters are fused, the design difficulty of the fusion judgment rule is great, and the more the number of parameters, the more complex the rule.

[0004] Chengdu Yungao Technology Co., Ltd. disclosed an IFF signal identification method based on a cross algorithm in its applied patent document "IFF signal identification method, device and medium based on cross algorithm" (application number: CN 202310450840.7, application date: April 25, 2023, application publication number: CN116166934A). The method processes the output signal through Gaussian filtering to obtain the to-be-identified Gaussian pulse, and obtains two Gaussian pulses through sampling and delay processing, respectively. According to the intersection of the two Gaussian pulses, the to-be-identified Gaussian pulse is marked, and the marked to-be-identified Gaussian pulse is identified according to the pulse framework search of different signal modes. However, this method still has shortcomings: in the presence of interference, the interference signal pulse and the IFF signal pulse are mixed in the time-frequency domain. Since the IFF signal pulse has a very short duration, the IFF signal is easily hidden in the interference signal, and this method cannot extract the available to-be-identified Gaussian pulse. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide an IFF signal identification and classification method based on feature signal matching anti-Tacon interference, which solves the problem of IFF signal identification difficulty caused by Tacon signal interference and the problem of low IFF signal identification and classification accuracy under low signal-to-noise ratio reception conditions.

[0006] The basic idea to achieve the purpose of the present application is to fully utilize the prior information of IFF signal, and to extract the feature signal of different types of IFF signal in advance. Since the feature signals of different types of IFF signal have weak correlation, the correlation degree of the to-be-identified signal and the feature signal can directly reflect the relationship between the to-be-identified signal and multiple IFF signal types, overcoming the problem of few identifiable types of IFF signal in the prior art. Since the method only uses the correlation degree with each type of IFF feature signal as the identification parameter, the parameter types are single and independent, and the parameters can be independently judged, avoiding the difficulty of parameter fusion judgment. By using the feature that the cross-correlation calculation of homologous signals has a certain gain, the problem of low IFF signal identification and classification accuracy under low signal-to-noise ratio conditions is overcome. The present application takes advantage of the huge difference in rising edge duration and falling edge duration between Tacon signal pulse and IFF signal pulse, which is manifested in time domain as the rising edge and falling edge of Tacon signal pulse being more gentle than IFF signal pulse. Since the first-order difference has the ability to describe the degree of function fluctuation, the received signal can be regarded as a function of time, and the time domain information of the IFF signal pulse disturbed by the Tacon signal pulse is extracted by the first-order difference method, so as to restore the disturbed IFF signal pulse, solving the problem of IFF signal identification and classification under Tacon interference.

[0007] The specific steps to achieve the purpose of the present application are as follows:

[0008] Step 1, the to-be-identified signal is processed by frequency conversion and low-pass filtering, and then the to-be-identified signal instantaneous amplitude sequence is calculated, and the feature signals of each type of IFF signal are extracted;

[0009] Step 2, the to-be-identified signal instantaneous amplitude sequence without tower jamming is processed by the extreme value smoothing method, and the to-be-identified signal instantaneous amplitude sequence interfered by the tower is processed by the difference recovery signal envelope method, to obtain the to-be-identified signal envelope;

[0010] Step 3, the normalized cross-correlation function of the to-be-identified signal envelope and each type of IFF feature signal is calculated, and the maximum value of the normalized cross-correlation function corresponding to each type of IFF feature signal is obtained;

[0011] Step 4, each type of IFF feature signal is used to identify and classify the to-be-identified signal.

[0012] Compared with the prior art, the present application has the following advantages:

[0013] First, the present application makes full use of the prior information of the IFF signal, and extracts the feature signals of each type of IFF signal in advance, and calculates the correlation degree of the to-be-identified signal and the feature signal by the cross-correlation algorithm, and uses the correlation degree as the identification parameter, which overcomes the problems of few identifiable types of IFF signals and poor recognition effect under low signal-to-noise ratio in the prior art, and avoids the difficulty of parameter fusion discrimination, so that the present application improves the accuracy of IFF signal recognition and has the advantage of strong robustness.

[0014] Second, the present application uses the great difference between the rising edge duration and the falling edge duration of the tower signal pulse and the IFF signal pulse, extracts the IFF signal time domain information interfered by the tower signal by the first-order difference method, and recovers the IFF signal, solves the identification and classification problem of the IFF signal under the tower interference, and makes the present application have the ability to identify and classify the IFF signal under the tower interference, and expands the application range of the method. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The flowchart of the present application;

[0016] Figure 2 IFF first type signal structure diagram;

[0017] Figure 3 IFF second type signal structure diagram;

[0018] Figure 4 IFF third type signal structure diagram;

[0019] Figure 5 IFF signal recovery diagram;

[0020] Figure 6 The normalized cross-correlation function diagram of different kinds of IFF signals and their corresponding IFF type characteristic signals;

[0021] Figure 7 The recognition accuracy comparison diagram of the present application and the parameter matching recognition method for the non-interference IFF signal;

[0022] Figure 8 The recognition accuracy comparison diagram of the present application and the parameter matching recognition method for the interference IFF signal. DETAILED DESCRIPTION

[0023] The present application will be further described below in combination with the drawings and embodiments.

[0024] Reference Figure 1 The implementation steps of the embodiments of the present application will be further described.

[0025] Step 1, calculate the instantaneous amplitude sequence of the to-be-recognized signal after processing the to-be-recognized signal through frequency down-conversion and low-pass filtering, and extract the characteristic signal of each type of IFF signal, which specifically includes six IFF interrogation signals of IFF MarkX system mode 1, mode 2, mode 3 / A, mode B, mode C, and mode D, and two signals of IFF MarkXII system mode 4 and mode S.

[0026] Reference Figure 2 The first type of structure refers to the six IFF interrogation signals of IFF MarkX system mode 1, mode 2, mode 3 / A, mode B, mode C, and mode D, which are all composed of P1, P2, and P3 three pulses, with a pulse width of 0.8 microseconds, a P1 and P2 interval of 2 microseconds, and a P1 and P3 interval of 3, 5, 8, 17, 21, and 25 microseconds, corresponding to IFF MarkX system mode 1, 2, 3 / A, B, C, and D respectively. The characteristic signal of this type of signal can be realized by keeping the time relationship of the three pulses unchanged, setting the amplitudes of P1 and P3 to 1, and setting the amplitude of P2 to 0.5.

[0027] Reference Figure 3 The second type of structure refers to the IFF MarkXII system mode 4 signal, which is composed of four synchronization pulses P1, P2, P3, P4, a control pulse P5, and 32 encryption information pulses, with a pulse width of 0.5 microseconds and a pulse interval of 2 microseconds. The characteristic signal of this type of signal is composed of P1, P2, P3, P4, and P5, and the amplitudes of P1, P2, P3, and P4 are set to 1, and the amplitude of P5 is set to 0.5.

[0028] Reference Figure 4The third type of structure refers to the IFF Mark XII system mode S signal, which is composed of two start pulses P1 and P2, a synchronous phase inversion pulse P3, a control pulse P5, and an information pulse P6, wherein the information pulse P6 is modulated by differential phase shift keying, the characteristic signal of this type of signal is composed of P1 and P2, the pulse width is set to 0.8 microseconds, the pulse interval is set to 2 microseconds, and the amplitudes of P1 and P2 are set to 1.

[0029] Step 2, the extreme value smoothing method is used to process the instantaneous amplitude sequence of the to-be-identified signal without TACAN interference, and the difference recovery signal envelope method is used to process the instantaneous amplitude sequence of the to-be-identified signal interfered by TACAN, to obtain the signal envelope of the to-be-identified signal.

[0030] The smoothing window length used in the extreme value smoothing method for the instantaneous amplitude sequence of the to-be-identified signal without TACAN interference is set to 0.1 microseconds.

[0031] Reference Figure 5 Further description is made to the difference recovery signal envelope method for the instantaneous amplitude sequence of the to-be-identified signal interfered by TACAN.

[0032] The rising edge duration of the IFF signal pulse is 0.05 microseconds to 0.1 microseconds, and the falling edge duration is 0.05 microseconds to 0.2 microseconds, the rising edge duration of the TACAN signal pulse is 2 microseconds, and the falling edge duration is 2.5 microseconds, so it can be concluded that the IFF signal has a steeper rising edge and falling edge than the TACAN signal, therefore, the first-order difference step can be set to 0.1 microseconds, and the first-order difference sequence of the IFF signal interfered by TACAN calculated by this method has obvious peaks at the rising and falling moments of the IFF signal pulse.

[0033] Step 3, calculate the normalized cross-correlation function of the to-be-identified signal envelope and each type of IFF characteristic signal, and obtain the maximum value of the normalized cross-correlation function corresponding to each type of IFF characteristic signal.

[0034] Further description is made to the calculation of the normalized cross-correlation function of the to-be-identified signal envelope and each type of IFF characteristic signal.

[0035] Since data in computer devices exists in discrete sampling form, the sampling sequence of the IFF signal characteristic signal can be denoted as S0, and the number of sampling points of the IFF signal characteristic signal sampling sequence can be denoted as N0. For any j∈N∩j∈[1,N0], S0(j) represents the j-th sampling point of S0. The sampling sequence of the envelope of the signal to be identified can be denoted as S1, and the number of sampling points of the envelope of the signal to be identified can be denoted as N1. We have N1≥N0. For any k∈N∩k∈[1,N0], S0(k) represents the k-th sampling point of S0. Assuming that the sampling rates of the two are the same, and that S0 and S1 both satisfy the zero-mean condition, then the normalized cross-correlation function of S0 and S1 can be obtained by the following formula:

[0036]

[0037] in, Let S0(l) represent the value of the nth point of the normalized cross-correlation function of S0 and S1(l+n) represent the value of the (l+n)th point of the normalized cross-correlation function of S0 and S1(l+n). Let S0(l) and S1(l+n) represent the sample mean of their product. This represents the sample variance of S0(l). Let S1(l+n) represent the sample variance, where l is an integer and l∈[1,N0], and n is an arbitrary integer. When n exceeds the index of sequence S1, zero-padding is required in S1. However, when... At that time, the normalized cross-correlation function This is not of reference value, therefore we can limit n∈[-(N0-1),N1].

[0038] Step 4: Use the normalized cross-correlation function corresponding to each type of IFF feature signal to identify and classify the signal to be identified.

[0039] Let there be m types of IFF feature signals. Then the normalized cross-correlation function corresponding to the h-th type of IFF feature signal can be denoted as: Where the integer h∈[1,m], The maximum value is denoted as P. h Assuming the decision threshold for the identification parameters is t, determine whether there exists an integer u∈[1,m] such that P u =max{P1,P2,...,P m-1 ,P m And P u If the value is greater than t, then the signal to be identified is identified as the signal type corresponding to the u-th IFF feature signal; otherwise, the signal to be identified is identified as a non-IFF signal type.

[0040] The technical effects of the present invention will be further explained below with reference to simulation experiments.

[0041] 1. Simulation experiment conditions:

[0042] The hardware platform of the simulation experiment of the application is: the processor is Intel(R) Core(TM) i7-10750H CPU, the main frequency is 2.60 GHz, and the memory is 64 GB.

[0043] The software platform of the simulation experiment of the application is: Windows 11 operating system and Matlab 2022b software.

[0044] 2. Simulation content and result analysis:

[0045] The simulation experiment of the application has three.

[0046] The simulation experiment 1 is to simulate the normalized cross-correlation function of different types of non-interference IFF signals by using the method of the application.

[0047] The signal types used in the simulation experiment 1 of the application are IFF MarkX system mode 1, mode 2, mode 3 / A, mode C four signals and IFF MarkXII system mode 4, mode S two signals, a total of six signals. The carrier frequency is 1030 MHz, the sampling rate is set to 56 MHz, the sampling length is 1288 sampling points, and the received signal-to-noise ratio is 10 dB.

[0048] The simulation experiment 1 of the application is to obtain the normalized cross-correlation function of six IFF signals and corresponding type IFF characteristic signals respectively by using the method of the application, and draw the normalized cross-correlation function into six simulation graphs as shown in Figure 6 .

[0049] The effect of the application will be further described below in combination with the simulation graphs. Figure 6 .

[0050] Figure 6 The horizontal coordinate in the simulation graph represents time, the unit is 0.0179 microseconds, and the vertical coordinate represents the normalized amplitude of the cross-correlation function, the value range is-1 to 1. The red dotted line represents the threshold 0.85, Figure 6 (a) The black curve in the simulation graph is the normalized cross-correlation function curve of IFF type 1 and type 1 characteristic signal, Figure 6 (b) The black curve in the simulation graph is the normalized cross-correlation function curve of IFF type 2 and type 2 characteristic signal, Figure 6 (c) The black curve in the simulation graph is the normalized cross-correlation function curve of IFF type 3 and type 3 characteristic signal, Figure 6 (d) The black curve in the simulation graph is the normalized cross-correlation function curve of IFF type 4 and type 4 characteristic signal, Figure 6 (e) The black curve in the simulation graph is the normalized cross-correlation function curve of IFF type C and type C characteristic signal,Figure 6 (f) The middle black curve is the normalized cross-correlation function curve of IFF type S and type S characteristic signal.

[0051] From Figure 6 It can be seen that the normalized cross-correlation function of a type of IFF signal and its corresponding type characteristic signal has a clear peak value. When the peak value exceeds the threshold, the to-be-identified signal and the characteristic signal have strong correlation, which indicates that the to-be-identified signal may be the IFF signal type corresponding to the characteristic signal, thereby verifying the theory of the method.

[0052] In simulation experiment 2 of the present application, the average recognition accuracy of different types of interference-free IFF signals under 8 signal-to-noise ratios is obtained by using the method of the present application and one prior art respectively, and then the relationship between the obtained recognition accuracy and the signal-to-noise ratio is plotted as two curves in Figure 7 .

[0053] In simulation experiment 2, the one prior art used is:

[0054] Hunan Aikoenov Technology Co., Ltd. discloses an IFF pulse signal classification extraction method in the patent literature "IFF pulse signal classification extraction method" (application number: CN 202011624785.1, application date: December 30, 2020, application publication number: CN 112766064A).

[0055] The signal types used in simulation experiment 2 of the present application are IFF MarkX system mode 1, mode 2, mode 3 / A, mode C four signals and IFF MarkXII system mode 4, mode S two signals, a total of six signals. The carrier frequency is 1030MHz, the sampling rate is set to 56MHz, the sampling length is 1288 sampling points, the received signal-to-noise ratio range is-6dB to 15dB, and the signal-to-noise ratio step is 3dB.

[0056] The effects of the present application will be further described below in combination with the simulation diagram. Figure 7 .

[0057] Figure 7 The horizontal coordinate in the simulation diagram represents the received signal-to-noise ratio, and the unit is dB. The vertical coordinate represents the recognition accuracy, and the value range is 0 to 1. Among them, the red curve marked with a circle represents the relationship curve between the recognition accuracy and the signal-to-noise ratio obtained by simulation using the method of the present application. The black curve marked with an asterisk represents the relationship curve between the recognition accuracy and the signal-to-noise ratio obtained by simulation using the prior art 1.

[0058] From Figure 7As can be seen from the table, the recognition accuracy of the method of the present application is generally significantly higher than that of prior art 1. When the signal-to-noise ratio is lower than 0 dB, the recognition effect of the method of the present application is close to that of prior art 1, but when the signal-to-noise ratio is higher than 0 dB, the recognition accuracy of the method of the present application increases at a rate significantly greater than that of prior art 1. When the signal-to-noise ratio is 4 dB, the present application can achieve a recognition accuracy of 76%, while prior art 1 can only achieve a recognition accuracy of 31%. When the signal-to-noise ratio is 8 dB, the present application can achieve a recognition classification accuracy of 97%, while prior art 1 can only achieve a recognition classification accuracy of 67%. This shows that the method of the present application can adapt to a lower signal-to-noise ratio condition than prior art 1.

[0059] In simulation experiment 3, the method of the present application and one prior art are used to obtain the average recognition accuracy of different types of IFF signals under 9 different signal-to-interference ratios of Tacan interference, and the relationship between the obtained recognition accuracy and the signal-to-interference ratio is plotted as two curves in Figure 8

[0060] In simulation experiment 3, the one prior art used is the same as that in simulation experiment 2.

[0061] The signal types used in simulation experiment 3 of the present application are IFF Mark X system mode 1, mode 2, mode 3 / A, mode C four signals and IFF Mark X II system mode 4, mode S two signals, a total of six signals. The carrier frequency is 1030 MHz, the sampling rate is set to 56 MHz, the sampling length is 1288 sampling points, the received signal-to-noise ratio is 15 dB, and the signal-to-interference ratio ranges from -12 dB to 12 dB with a step of 3 dB.

[0062] The effects of the present application will be further described below in combination with the simulation graph of Figure 8 .

[0063] Figure 8 The horizontal axis in the graph represents the signal-to-interference ratio in dB, and the vertical axis represents the recognition accuracy, which ranges from 0 to 1. The red curve marked with a circle represents the relationship between the recognition accuracy and the signal-to-interference ratio obtained by simulation using the method of the present application, and the black curve marked with an asterisk represents the relationship between the recognition accuracy and the signal-to-interference ratio obtained by simulation using prior art 1.

[0064] From Figure 8 ​As can be seen, the recognition accuracy of the method of the present application is generally significantly higher than that of prior art 1. When the signal-to-interference ratio is lower than -6 dB, the recognition effect of the method of the present application is close to that of prior art 1. However, when the signal-to-interference ratio is higher than -6 dB, the recognition accuracy of the method of the present application increases at a rate significantly greater than that of prior art 1. When the signal-to-interference ratio is 0 dB, the present application can achieve a recognition accuracy of 79%, while prior art 1 can only achieve a recognition accuracy of 22%. When the signal-to-interference ratio is 6 dB, the present application can achieve a recognition classification accuracy of 92%, while prior art 1 can only achieve a recognition classification accuracy of 49% under this condition. This shows that the method of the present application is more excellent than prior art 1 in the recognition and classification ability of IFF signals under the condition of Tacan interference.

Claims

1. A method for IFF signal identification and classification based on feature signal matching to resist TACAN interference, characterized in that, The IFF signal pulse is recovered using the differential method, and the cross-correlation function between the envelope of the signal to be identified and the IFF feature signal is calculated. The steps of this IFF signal identification and classification method include the following: Step 1: After processing the signal to be identified by down-conversion and low-pass filtering, calculate the instantaneous amplitude sequence of the signal to be identified and extract the feature signals of each type of IFF signal; Step 2: The instantaneous amplitude sequence of the signal to be identified without TACAN interference is processed using the extreme value smoothing method, and the instantaneous amplitude sequence of the signal to be identified affected by TACAN interference is processed using the differential recovery signal envelope method to obtain the envelope of the signal to be identified. Step 3: Calculate the normalized cross-correlation function between the envelope of the signal to be identified and the feature signal of each type of IFF, and obtain the maximum value of the normalized cross-correlation function corresponding to each type of IFF feature signal; Step 4: Use the normalized cross-correlation function corresponding to each type of IFF feature signal to identify and classify the signal to be identified.

2. The IFF signal identification and classification method based on feature signal matching to resist TACAN interference as described in claim 1, characterized in that, The specific steps for extracting the feature signals for each type of IFF signal in step 1 are as follows: The first step is to acquire the characteristic signal of the IFF MarkX system, which consists of three pulses, P1, P2, and P3, with a pulse width of 0.8 microseconds. The pulse interval between P1 and P2 is 2 microseconds. The interval between P1 and P3 has different time intervals according to the system specifications. The amplitudes of P1 and P3 are equal, while the amplitude of P2 is lower than that of P1 and P3. The second step is to acquire the first type of characteristic signal of the IFF MarkXⅡ system, which consists of four synchronization pulses P1, P2, P3, P4 and control pulse P5. The pulse width is 0.5 microseconds and the pulse interval is 2 microseconds. The amplitudes of P1, P2, P3 and P4 are equal, while the amplitude of P5 is relatively low. The third step is to acquire the second type of characteristic signal of the IFF MarkXⅡ system, which consists of two starting pulses P1 and P2, with a pulse width of 0.8 microseconds and a pulse interval of 2 microseconds. The amplitudes of P1 and P2 are equal.

3. The IFF signal identification and classification method based on feature signal matching to resist TACAN interference as described in claim 1, characterized in that, The specific steps of the extreme value smoothing method described in step 2 are as follows: The first step is to set the smooth window length; The second step is to use the maximum instantaneous amplitude of the signal to be identified within the window as the envelope of the signal to be identified, according to the following formula. ; in, This indicates that the smoothed envelope of the signal to be identified is in The value at time, This indicates the operation to find the maximum value. This represents the instantaneous amplitude of the signal to be identified at time t. , Indicates the length of the smooth window.

4. The IFF signal identification and classification method based on feature signal matching to resist TACAN interference as described in claim 1, characterized in that, The steps of the differential signal envelope recovery method described in step 2 are as follows: The first step is to perform a first-order difference operation on the instantaneous amplitude sequence of the TACAN-interferenced signal to obtain the first-order difference sequence of the instantaneous amplitude of the signal. The second step is to perform peak detection on the obtained instantaneous amplitude first-order difference sequence of the signal to obtain the relative positions of the rising and falling edges of the pulse; The third step is to recover the original pulse envelope based on the relative positions of the rising and falling edges of the pulse.

5. The IFF signal identification and classification method based on feature signal matching to resist TACAN interference according to claim 1, characterized in that, The normalized cross-correlation function mentioned in step 3 is as follows: ; in, Indicates signal and signal The normalized cross-correlation function, Indicates signal After time delay The delayed signal after that, Let be any real number, express The mathematical expectation, Indicates signal variance Indicates signal The variance.

6. The IFF signal identification and classification method based on feature signal matching to resist TACAN interference according to claim 1, characterized in that, The specific steps for classifying the signal to be identified using the normalized cross-correlation function corresponding to each type of IFF feature signal in step 4 are as follows: The first step is to generate a set of identification parameters, where each element is the maximum value of the normalized cross-correlation function corresponding to each type of IFF feature signal. The second step is to determine whether the largest element in the set of identification parameters is greater than or equal to the threshold. If so, the signal is identified as the IFF signal type corresponding to the largest element in the set of identification parameters; otherwise, the signal is identified as a non-IFF signal.

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