IFF signal mode recognition method based on sliding window search pulse width-amplitude combination

Abstract

The invention discloses an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination, which comprises the following steps of: firstly, establishing a signal data comparison library by utilizing various known inquiry and response signal formats, comparing a standard reference pulse string sequence in the database with a pulse stream to be recognized in a sliding window range, searching a reference pulse, and comparing the reference pulse string sequence with the pulse stream to be recognized in the sliding window range; determining the positions of a sliding window and each child window; when the similarity between the to-be-identified pulse parameter in each sub-window and the standard reference pulse string sequence reaches a preset threshold, it can be considered that the required pulse string sequence is found; and then continuing to search the next sliding window until all pulses are identified. If the similarity of the pulse parameters in the sub-window does not reach a threshold set value, the reference pulse is abandoned, the next reference pulse is found to establish a new sliding window, and the operation is continued until the pulse string sequence meeting the condition is found or all pulse signal sequences are identified.

Description

Technical Field

[0001] The invention belongs to the technical field of signal recognition and data processing in target detection and positioning, and in particular relates to an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination. Background Art

[0002] Correct and effective identification of IFF signals requires a comprehensive and detailed analysis of the signal's time-frequency domain characteristics. In the theory of signal time-frequency analysis, signal distribution is represented as a joint function of time and frequency. The most direct method is to assume that the signal is approximately stationary in a small time period, and then use Fourier transform to analyze the local power spectrum of the signal in each time period, that is, short-time Fourier transform.

[0003] Later, inspired by similar methods in quantum mechanics, Gabor and Ville proposed the famous Gabor transform and Wigner-Ville distribution, respectively, laying the foundation for the development of time-frequency analysis theory. In China, some scholars, based on the basic characteristics of IFF signals, extracted the signal time domain and frequency domain information through carrier frequency measurement, signal envelope and instantaneous phase detection, and classified and identified the signals to be identified. However, due to the influence of signal-to-noise ratio, the accuracy of identification needs to be improved.

[0004] Considering that the civil aircraft identification interrogation / response signal is in the form of a pulse train sequence rather than a fixed PRI pulse signal, and is a non-periodic signal, the conventional PRI repetition frequency identification method cannot be used. Therefore, it is necessary to adopt the pulse width-amplitude joint parameter identification method, and use the sliding window search technology to gradually slide match the pulses in the pulse train sequence to achieve the best interception of the entire pulse train. Summary of the invention

[0005] The present invention aims to solve the problem raised in the background technology and proposes an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination.

[0006] In order to achieve the purpose of the present invention, the present invention discloses an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination, comprising the following steps:

[0007] Step 1: Use the standard reference pulse train sequence in the database to compare with the pulse stream to be identified within the sliding window range, search for the reference pulse, and determine the position of the sliding window and each sub-window;

[0008] Step 2: determine whether the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold. If so, it is considered that the required pulse train sequence has been found;

[0009] Step 3: If the pulse parameter similarity in the sub-window does not reach the threshold setting value, the reference pulse will be abandoned and the next reference pulse will be found to establish a new sliding window; steps 1 and 2 are repeated until a pulse train sequence that meets the conditions is found or the identification of all pulse signal sequences is completed.

[0010] Furthermore, in step 1, a target interrogation response signal pulse train sequence is determined, and a reference pulse satisfying the interrogation response signal pulse train sequence is searched in the entire pulse stream.

[0011] Furthermore, in step 2, after the desired pulse string sequence is found, a suitable search window is set according to the standard pulse string sequence in the database to quickly search it. After the entire pulse string is searched, the window is slid backward until the search is completed in the entire pulse stream.

[0012] Furthermore, in step 3, the searched pulse string sequence is identified from the pulse stream, and the identification of the next pulse signal sequence continues. During this period, the processing time overhead of the entire signal identification is reduced by parallel search processing of different mode inquiry response signal sequences.

[0013] Furthermore, a signal data comparison library is first established using various known interrogation and response signal sequence formats as prior knowledge; then, the pulse width, amplitude and other features in the known standard signal sequence are selected as typical descriptions, recorded as b(k), k is the standard signal pulse number, and the characteristic parameters such as the actual detected signal pulse width and amplitude are recorded as a(t), t is the actual signal pulse number; in order to search for the reference pulse faster, assuming k = 1, the similarity calculation formula is used to traverse and search for the pulse number t corresponding to the preset threshold, which is recorded as the reference pulse, and the number is reassigned to an initial value of 1:

[0014] d=cos(a(t)-b(1)),t≥1

[0015] d is the similarity calculated between each pulse and the reference pulse. If, during the traversal and matching process, the similarity calculated for any pulse in the time window does not reach the preset threshold, this pulse string can be abandoned and the search can be restarted in the next time window until a reference pulse that meets the preset threshold is found.

[0016] Furthermore, the method further comprises step 4: quantitatively analyzing the accuracy of the matching result and evaluating the matching pulse train in combination with a confidence criterion.

[0017] Furthermore, step 4 is specifically as follows:

[0018] Assume that the characteristic parameter of the kth matching pulse in the actually detected pulse train is a(k), the characteristic parameter of the kth pulse in the standard signal sequence is b(k), the maximum parameter deviation is err, and the parameter deviation obeys the uniform distribution of [0, err]. Then the deviation mean Δv = err / 2, the variance is σ2 = err2 / 12, and the similarity confidence is f(k);

[0019]

[0020] Then use the following formula to calculate the similarity confidence value μ of the entire pulse train;

[0021]

[0022] Where N is the number of pulses in the pulse train.

[0023] In order to achieve the purpose of the present invention, the present invention also discloses an IFF signal pattern recognition system based on sliding window search pulse width-amplitude combination, comprising:

[0024] Search reference pulse module: compare the standard reference pulse train sequence in the database with the pulse stream to be identified within the sliding window range, search for the reference pulse, and determine the position of the sliding window and each sub-window;

[0025] Similarity threshold judgment module: judge whether the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold. If so, it is considered that the required pulse train sequence has been found;

[0026] Backtracking search module: If the pulse parameter similarity in the sub-window does not reach the threshold setting value, the reference pulse will be abandoned and the next reference pulse will be found to establish a new sliding window; steps 1 and 2 will be repeated until a pulse train sequence that meets the conditions is found or the identification of all pulse signal sequences is completed.

[0027] In order to achieve the purpose of the present invention, the present invention also discloses an electronic device, including a memory, a processor and a computer program stored in the memory and run on the processor. When the processor executes the program, an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination is implemented.

[0028] In order to achieve the purpose of the present invention, the present invention also discloses a computer storable medium on which a computer program is stored. When the computer program is executed by a processor, an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination is implemented.

[0029] Compared with the prior art, the significant progress of the present invention lies in that the method is not limited to the recognition and processing of periodic sequence pulse signals, but can also perform effective signal recognition and processing on non-periodic pulse signals. At the same time, during the signal recognition process, there is no need to traverse every pulse in the entire pulse string sequence signal to be identified. It is only necessary to first find the reference pulse, and then search for the corresponding pulse sequence signal in a specific position area according to the standard reference sequence in the database. This can significantly speed up the efficiency of signal recognition.

[0030] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0032] Figure 1 Schematic diagram of the waveform of the pulse sequence signal to be sorted;

[0033] Figure 2 It is the query response signal sorting result figure a;

[0034] Figure 3 It is the query response signal sorting result diagram b;

[0035] Figure 4 The present invention is a flowchart of an IFF signal pattern recognition method based on a sliding window search pulse width-amplitude combination. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] A method for IFF signal pattern recognition based on sliding window search pulse width-amplitude combination, using the prior knowledge of the signal data comparison library, searching for reference pulses, determining the position of the sliding window and each sub-window, when the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold, it can be considered that the required pulse train sequence is found. The specific implementation method is as follows:

[0038] Step 1: Use the standard reference pulse train sequence in the database to compare with the pulse stream to be identified within the sliding window range, search for the reference pulse, and determine the position of the sliding window and each sub-window;

[0039] Step 2: determine whether the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold. If so, it is considered that the required pulse train sequence has been found;

[0040] Step 3: If the pulse parameter similarity in the sub-window does not reach the threshold setting value, the reference pulse will be abandoned and the next reference pulse will be found to establish a new sliding window; steps 1 and 2 are repeated until a pulse train sequence that meets the conditions is found or the identification of all pulse signal sequences is completed.

[0041] Specifically, in step 1, a target interrogation response signal pulse train sequence is determined, and a reference pulse satisfying the interrogation response signal pulse train sequence is searched in the entire pulse stream.

[0042] Specifically, in step 2, after the desired pulse string sequence is found, a suitable search window is set according to the standard pulse string sequence in the database to quickly search it. After the entire pulse string is searched, the window is slid backward until the search is completed in the entire pulse stream.

[0043] Specifically, in step 3, the searched pulse string sequence is identified from the pulse stream, and the identification of the next pulse signal sequence continues. During this period, the processing time overhead of the entire signal identification is reduced by parallel search processing of different mode inquiry response signal sequences.

[0044] Specifically, a signal data comparison library is first established using various known interrogation and response signal sequence formats as prior knowledge; then, the pulse width, amplitude and other features in the known standard signal sequence are selected as typical descriptions, recorded as b(k), k is the standard signal pulse number, and the actual detected signal pulse width, amplitude and other characteristic parameters are recorded as a(t), t is the actual signal pulse number; in order to search for the reference pulse faster, assuming k = 1, the similarity calculation formula is used to traverse and search for the pulse number t corresponding to the preset threshold, recorded as the reference pulse, and the number is reassigned to an initial value of 1:

[0045] d=cos(a(t)-b(1)),t≥1

[0046] d is the similarity calculated between each pulse and the reference pulse. If, during the traversal and matching process, the similarity calculated for any pulse in the time window does not reach the preset threshold, this pulse string can be abandoned and the search can be restarted in the next time window until a reference pulse that meets the preset threshold is found.

[0047] Specifically, the method further includes step 4: quantitatively analyzing the accuracy of the matching result and evaluating the matching pulse train in combination with the confidence criterion, specifically:

[0048] Assume that the characteristic parameter of the kth matching pulse in the actually detected pulse train is a(k), the characteristic parameter of the kth pulse in the standard signal sequence is b(k), the maximum parameter deviation is err, and the parameter deviation obeys the uniform distribution of [0, err]. Then the deviation mean Δv = err / 2, the variance is σ2 = err2 / 12, and the similarity confidence is f(k);

[0049]

[0050] Then use the following formula to calculate the similarity confidence value μ of the entire pulse train;

[0051]

[0052] Where N is the number of pulses in the pulse train.

[0053] Example

[0054] To verify the recognition accuracy of the algorithm, the following simulation was performed: the pulse flow density after preprocessing was about 70,000 pulses / second, and the sampling frequency was 100Msps. The duration of the simulated pulse sequence was 30ms (3 million sampling points), that is, 100 points = 1us. The pulse amplitude was normalized to 1, the pulse amplitude threshold was set to 0.4-1.6, and the signal-to-noise ratio was 12-18dB.

[0055] The pulse train signal stream to be sorted consists of the following signals:

[0056] The inquiry and response pulse train signals include Mark X Mode 1, Mode 2, Mode 3 / A inquiry, Mark X response, Mark XII Mode 4 inquiry and response, and Mark S inquiry and response. Each signal is sent continuously for 5 frames (with a random frame interval value).

[0057] Other pulse signals include 12 repetition rate pulse signals with different pulse intervals and pulse widths, and the specific parameters are shown in Table 1.

[0058] Table 1 Specific parameter information of other repetition frequency pulse signals

[0059] Serial number PRI PW Serial number PRI PW 1 3000 20 7 5000 60 2 3000 30 8 5000 80 3 3000 45 9 10000 25 4 3000 50 10 10000 50 5 5000 30 11 10000 65 6 5000 45 12 10000 80

[0060] Each repetition rate pulse signal has 100 repetition pulses. The window tolerance of the sliding window is 5%. 100 Monte Carlo simulation experiments are carried out under different signal-to-noise ratio conditions, and the pulse signal sequence sorting results are shown in Table 2.

[0061] Table 2 Sorting and recognition rate of IFF signals in various working modes and different signal-to-noise ratios

[0062]

[0063] The accuracy of the signal sorting results obtained by simulation sorting processing under high signal-to-noise ratio conditions is about 85%-95%. The reason for the sorting error is that there is a partial signal waveform overlap area in the signal synchronization pulse sequence.

[0064] The waveform of the pulse sequence signal to be sorted generated by the simulation program is as follows Figure 1 As shown, it is not only composed of the common pulse sequence in Table 1 and the inquiry response pulse sequence in Table 2, but also superimposed with Gaussian white noise, wherein the waveform overlap interval accounts for ≤6%.

[0065] from Figure 1 The query response signal waveforms under each working mode selected from the signal shown are as follows: Figure 2-Figure 3 shown. Figure 2 The recognition results of the Mark X Mode 1, Mode 2, Mode 3 / A inquiry and Mark X response signal waveforms after using the present invention are shown in FIG. Figure 3 The recognition results of the Mark XII Mode 4 interrogation and response and Mark S interrogation and response signal waveforms.

[0066] By adopting the above-mentioned algorithm recognition and processing, it is possible to identify the inquiry response signal pulse train sequence of interest from a high-density pulse stream. When the signal-to-noise ratio (SNR) exceeds 15dB, the signal recognition accuracy can reach more than 70%. Its main shortcoming is that there is still a high error rate in identifying the inquiry response pulse signal stream with overlapping signals. It is necessary to explore a recognition processing algorithm with higher recognition accuracy when there is partial overlap in the pulse sequence signal.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants 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.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for IFF signal pattern recognition based on sliding window search pulse width-amplitude combination, characterized in that: The following steps are involved: Step 1: Use the standard reference pulse train sequence in the database to compare with the pulse stream to be identified within the sliding window range, search for the reference pulse, and determine the position of the sliding window and each sub-window; Step 2: determine whether the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold. If so, it is considered that the required pulse train sequence has been found; Step 3: If the pulse parameter similarity in the sub-window does not reach the threshold setting value, the reference pulse will be abandoned and the next reference pulse will be found to establish a new sliding window; steps 1 and 2 are repeated until a pulse train sequence that meets the conditions is found or the identification of all pulse signal sequences is completed.

2. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 1, characterized in that: In step 1, a target interrogation response signal pulse train sequence is determined, and a reference pulse satisfying the interrogation response signal pulse train sequence is searched in the entire pulse stream.

3. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 2, characterized in that: In step 2, after the desired pulse string sequence is found, a suitable search window is set according to the standard pulse string sequence in the database to quickly search it. After the entire pulse string is searched, the window is slid backward until the search is completed in the entire pulse stream.

4. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 3, characterized in that: In step 3, the searched pulse string sequence is identified from the pulse stream, and the identification of the next pulse signal sequence continues. During this period, the processing time overhead of the entire signal identification is reduced by parallel search processing of different mode inquiry response signal sequences.

5. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 1, characterized in that: First, a signal data comparison library is established as prior knowledge using various known interrogation and response signal sequence formats; then, the pulse width, amplitude and other features in the known standard signal sequence are selected as typical descriptions, denoted as b(k), where k is the standard signal pulse number, and the characteristic parameters such as the actual detected signal pulse width and amplitude are denoted as a(t), where t is the actual signal pulse number; in order to search for the reference pulse faster, assuming k = 1, the similarity calculation formula is used to traverse and search for the pulse number t corresponding to the preset threshold, which is denoted as the reference pulse, and the number is reassigned to an initial value of 1: d=cos(a(t)-b(1)),t≥1 d is the similarity calculated between each pulse and the reference pulse. If, during the traversal and matching process, the similarity calculated for any pulse in the time window does not reach the preset threshold, this pulse string can be abandoned and the search can be restarted in the next time window until a reference pulse that meets the preset threshold is found.

6. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 1, characterized in that: The method further includes step 4: quantitatively analyzing the accuracy of the matching result and evaluating the matching pulse train in combination with a confidence criterion.

7. The IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to claim 6, characterized in that: Step 4 is as follows: Assume that the characteristic parameter of the kth matching pulse in the actually detected pulse train is a(k), the characteristic parameter of the kth pulse in the standard signal sequence is b(k), the maximum parameter deviation is err, and the parameter deviation obeys the uniform distribution of [0, err]. Then the deviation mean Δv = err / 2, the variance is σ2 = err2 / 12, and the similarity confidence is f(k); Then use the following formula to calculate the similarity confidence value μ of the entire pulse train; Where N is the number of pulses in the pulse train.

8. An IFF signal pattern recognition system based on sliding window search pulse width-amplitude combination, the system is based on an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to any one of claims 1-7, characterized in that: include Search reference pulse module: compare the standard reference pulse train sequence in the database with the pulse stream to be identified within the sliding window range, search for the reference pulse, and determine the position of the sliding window and each sub-window; Similarity threshold judgment module: judge whether the similarity between the pulse parameters to be identified in each sub-window and the standard reference pulse train sequence reaches a preset threshold. If so, it is considered that the required pulse train sequence has been found; Backtracking search module: If the pulse parameter similarity in the sub-window does not reach the threshold setting value, the reference pulse will be abandoned and the next reference pulse will be found to establish a new sliding window; steps 1 and 2 will be repeated until a pulse train sequence that meets the conditions is found or the identification of all pulse signal sequences is completed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination as described in any one of claims 1-7 is implemented.

10. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, an IFF signal pattern recognition method based on sliding window search pulse width-amplitude combination according to any one of claims 1 to 7 is implemented.

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