Interference direction finding and identification method for energy detection and wavelet transform in electronic countermeasure

Abstract

The invention belongs to the technical field of interference identification, and particularly relates to an interference direction finding and identification method for energy detection and wavelet transform in electronic countermeasure. The method comprises the following steps: dividing a detection range into azimuth sectors according to azimuth beam width, judging whether interference exists or not for each azimuth sector by using an energy detection method and azimuth sector signal echo power, and giving an interference direction and interference intensity; secondly, extracting interference characteristics by using wavelet transform, and realizing identification of noise suppression interference and dense false target interference; according to the invention, the interference sensing capability of the equipment can be effectively improved, and the equipment is guided to take optimal anti-interference measures, so that the survivability and combat effectiveness of the equipment in a complex electromagnetic environment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of interference identification, and more specifically, relates to an interference direction finding and identification method based on energy detection and wavelet transform in electronic countermeasures. Background Art

[0002] In recent years, with the increasing importance of electronic warfare, the struggle for electromagnetic dominance on the electronic battlefield has intensified. This has directly led to a serious threat of electromagnetic interference to radars. In particular, the widespread use of jamming technology based on digital radio frequency memory (DRFM) allows jammers to be modulated and forwarded based on the currently intercepted signal in a very short time. However, this type of jammer has strong coherence characteristics, making it difficult for radar anti-jamming tools to effectively identify and suppress it.

[0003] Although current radars have anti-interference capabilities, they lack the ability to automatically detect interference types and, consequently, select the most appropriate interference suppression method based on changes in the battlefield interference environment. Currently, most radar anti-interference processing relies solely on the radar operator's observation of the radar display interface, selecting specific anti-interference measures only when interference is detected. However, modern radar systems place higher demands on automation and intelligent performance, and anti-interference methods that rely solely on human effort and subjective experience are lagging behind the pace of modern radar development.

[0004] Radar's inability to automatically detect and identify interference has become a major factor hindering the development of radar anti-jamming technology. Therefore, improving active jamming identification capabilities is key to overcoming bottlenecks in active jamming technology development and is also a crucial prerequisite for radars to implement effective and targeted anti-jamming measures. Given the increasing role of radar countermeasures in electronic warfare and the continuous emergence of new types of jamming, the need for radar jamming identification is becoming increasingly urgent.

[0005] Therefore, conducting research on radar interference identification technology can guide radar to take the best anti-interference measures and suppress interference more effectively, which is of great practical significance for improving the survivability and combat effectiveness of radar in complex electromagnetic environments. Summary of the Invention

[0006] In view of the current technical difficulties in automatically sensing interference types, relying more on manual judgment and setting, and being highly ambiguous and subjective, which seriously restrict the development of equipment anti-interference technology, the present invention proposes an interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures. This innovative combination of energy detection and packet transform feature extraction can solve the technical problems of the existing technology, such as reliance on rich manual experience and slow recognition speed. It has the advantages of low computational complexity, fast recognition speed, high accuracy, simple process, and convenient engineering implementation. It has been successfully verified in application to a certain type of radar.

[0007] The technical solution of the present invention is: an interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures, characterized in that it includes the following steps:

[0008] Step 1: Calculate interference direction and intensity based on energy detection method

[0009] Step 1.1: Adjust the radar detection airspace from 0° to 360° according to the azimuth beam width , the 360° azimuth sector is divided into segments part;

[0010] Step 1.2: The radar echo signal without interference and without transmission is , by counting the signal energy and averaging it to get the signal power when there is no interference ,

[0011] , For sampling points The corresponding echo signal, , is the number of sampling points of the received signal during the statistical time period;

[0012] Step 1.3: When there is interference, the radar echo signal is: , where It is an interference signal. is the target echo signal, is the input system noise signal,

[0013] The signal echo power is: , where , is the total number of sampling points of the received signal during the detection period;

[0014] Step 1.4: Determine whether there is interference using a constant false alarm strategy and set the threshold , is the control coefficient,

[0015] Step 1.5: If , then it is determined that there is interference in the current sector segment, and the interference flag and interference intensity of the current sector are output. ;

[0016] Step 2: Identify interference types based on wavelet transform feature extraction

[0017] Step 2.1: Echo signal Energy normalization is performed to keep the energy of all interference signals at the same level, that is, .

[0018] Step 2.2: Echo Signal The discrete wavelet transform of is defined as:

[0019]

[0020] in, is the wavelet packet coefficient, is the discrete wavelet basis function, 、 is a positive integer, represents the scale factor, represents the translation factor;

[0021] Step 2.3: The original signal can be reconstructed using wavelet packet coefficients and discrete wavelet basis functions.

[0022]

[0023] Step 2.4: Set different and Value, reconstruct the time domain signal, and get , No. layer wavelet packet coefficients, ; Only after 3 layers of wavelet packet decomposition, the wavelet domain signal component of the interference signal has good classification performance, so Take 0 to 3.

[0024] Step 2.5: Calculate the echo power of the reconstructed time domain signal, , is the length of the reconstructed signal. The wavelet domain eigenvector of the signal is obtained, that is,

[0025]

[0026] Step 2.6: Analyze the wavelet domain signal components based on the wavelet domain eigenvectors. The signal components of the dense false target interference signal are regularly distributed in a sawtooth shape, while the signal components of the noise suppression interference are irregularly distributed.

[0027] According to the interference direction finding and identification method of energy detection and wavelet transform in electronic countermeasures, it is characterized in that: the control coefficient The value range is 6dB to 15dB.

[0028] According to the interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures as described above, it is characterized in that: The value range is 0 to 3.

[0029] The interference direction finding and identification method based on energy detection and wavelet transform in electronic countermeasures is characterized by being applied to electronic countermeasure systems.

[0030] According to the interference direction finding and identification method based on energy detection and wavelet transform in electronic countermeasures as described above, it is characterized by a radar interference identification system.

[0031] According to the interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures as described above, it is characterized in that: is 120.

[0032] The beneficial effect of step 2 is that the interference signal is decomposed and reconstructed using wavelet transform, breaking it down into wavelet coefficients in different frequency bands. Wavelet packet decomposition reveals the interference signal's signal components in different frequency bands. Due to the different generation mechanisms and modulation methods of different radar interference signals, the energy distribution of the interference signal varies across the frequency bands. Therefore, the energy distribution vector can be extracted as a characteristic parameter for interference signal classification.

[0033] The beneficial effects of the present invention are: 1. Using the echo power in the absence of interference and non-transmission conditions as the judgment threshold, it is possible to accurately judge whether interference exists in the current sector; 2. By controlling the coefficient, it can be adjusted in real time according to actual conditions to control interference false alarms; 3. Using three-layer wavelet packet decomposition, the amount of calculation is controlled, facilitating engineering implementation; 4. According to the wavelet domain signal components, noise suppression interference and dense false target interference can be accurately distributed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of an interference direction finding and identification method based on energy detection and wavelet transform in electronic countermeasures.

[0035] Figure 2 Schematic diagram of the energy detection method implementation process.

[0036] Figure 3Three-layer wavelet packet tree decomposition diagram.

[0037] Figure 4 Single pulse dense false target interference signal.

[0038] Figure 5 Single impulse noise suppression interference signal.

[0039] Figure 6 The first four signal components of the third layer of wavelet packet decomposition of dense false target interference signals.

[0040] Figure 7 The first four signal components of the third layer are decomposed by wavelet packet of noise suppression interference signal.

[0041] Figure 8 The proportion of the total energy of each signal component under different pulses. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0043] The embodiment of the present invention provides an interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures, which can be applied to the fields of electronic countermeasure systems and radar interference identification technology.

[0044] See also Figure 1 A method for interference direction finding and identification using energy detection and wavelet transform in electronic countermeasures comprises the following steps:

[0045] Step 1: Calculate interference direction and intensity based on energy detection method

[0046] Step 1.1: Adjust the radar detection airspace from 0° to 360° according to the azimuth beam width , the 360° azimuth sector is divided into segments part, ,For example =3°, then =120.

[0047] Step 1.2: Segment azimuth sector, count all pulse repetition period (PRI) echoes in each azimuth sector, assuming no interference and no transmission, the radar echo signal is ,at this time , is the input system noise signal. Since the transmission is not turned on, the radar echo signal only contains system noise.

[0048] The signal power without interference is obtained by counting the signal energy within a certain period of time and averaging it. , , For sampling points The corresponding echo signal, , The number of sampling points of the received signal in the statistical time period is used as the basis for judging whether there is interference.

[0049] Step 1.3: When there is interference, the radar echo signal is: , where It is an interference signal. is the target echo signal.

[0050] The signal echo power is: , where , is the total number of sampling points of the received signal during the detection period, and It is determined by the number of PRIs in the azimuth sector and the number of sampling points corresponding to each PRI.

[0051] Step 1.4: If Figure 2 As shown, the constant false alarm strategy is used to determine whether there is interference, that is, setting the threshold , is the control coefficient, if If the value of is too large, the detection threshold will be high, and the direct result is that the probability of missed alarm will increase; if If the value of is too small, the detection threshold will be too low, and the direct result is that the probability of false alarm will increase. Generally, it is 6dB to 15dB. In actual application, it can be adjusted appropriately according to the actual needs.

[0052] Step 1.5: If , then it is determined that there is interference in the current sector segment, and the interference flag and interference intensity of the current sector are output. .

[0053] Step 2: Identify interference types based on wavelet transform feature extraction

[0054] Step 2.1: In order to eliminate the influence of signal gain on feature extraction, first perform the Energy normalization is performed to keep the energy of all interference signals at the same level, that is, .

[0055] Step 2.2: Echo Signal The discrete wavelet transform of is defined as:

[0056]

[0057] in, is the wavelet packet coefficient, is the discrete wavelet basis function, 、 are positive integers, representing wavelet sequences The scale factor and translation factor, , in the present invention, Haar wavelet is used as the wavelet basis function.

[0058] Step 2.3: Wavelet packet coefficients It is expressed as the inner product of the signal and the wavelet function. Therefore, the original signal can be reconstructed using the wavelet packet coefficients and discrete wavelet basis functions, that is,

[0059]

[0060] Step 2.4: If Figure 3 As shown in the figure, a tree decomposition diagram of three-layer wavelet packets is given. Through multiple iterations of wavelet transform, the wavelet packet coefficients of the signal at different decomposition levels are obtained. Through step 2.3, different and Value, reconstruct the time domain signal, and get , is the scale factor, i.e. the number of decomposition levels, is the translation factor, i.e. layer wavelet packet coefficients, In practice, it is found that only three layers of wavelet packet decomposition are needed for the wavelet domain signal component of the interference signal to have good classification performance. Take 0 to 3.

[0061] Step 2.5: Based on the result of step 2.4, calculate the echo power of the reconstructed time domain signal. , is the length of the reconstructed signal. The wavelet domain eigenvector of the signal is obtained, that is,

[0062]

[0063] Step 2.6: Analyze the wavelet domain signal components based on the wavelet domain eigenvectors. The signal components of the dense false target interference signal are regularly distributed in a sawtooth shape, while the signal components of the noise suppression interference are irregularly distributed. The interference type can be determined based on this.

[0064] Using the embodiment of the present invention, Figure 4 This is the measured data of the interference signal of a certain type of radar's single pulse dense false target. Figure 5 This is the measured data of a single pulse noise suppression interference signal of a certain type of radar. Figure 6 The first four signal components of the third layer are decomposed by wavelet packet for dense false target interference signals. Figure 7 The first four signal components of the third layer of the wavelet packet decomposition of the noise suppression interference signal are shown in Figure 1. It can be found that the signal components of the dense false target interference signal are regularly distributed in a sawtooth shape, while the signal components of the noise suppression interference are irregularly distributed.

[0065] Figure 8 is the total energy ratio of each signal component under different pulses. It can be seen that the energy ratio of each signal component in noise suppression interference remains basically stable and does not change with the number of pulses. However, the signal components of dense false target interference show severe jitter as the number of pulses changes.

[0066] In general, the proportion of total energy contributed by different signal components from different interference sources varies. The energy ratios of various signal components in noise suppression interference remain essentially stable, resulting in a small variance in each component. This characteristic can be exploited to effectively distinguish them by dividing thresholds. This invention can effectively improve equipment's interference perception capabilities, thereby guiding equipment to adopt optimal anti-interference measures, thereby enhancing its survivability and combat effectiveness in complex electromagnetic environments.

[0067] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structures or equivalent process changes made using the contents of the present invention's description and drawings, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for interference direction finding and identification using energy detection and wavelet transform in electronic countermeasures, characterized by: The steps include: Step 1: Calculate interference direction and intensity based on energy detection method Step 1.1: Adjust the radar detection airspace from 0° to 360° according to the azimuth beam width , the 360° azimuth sector is divided into segments part; Step 1.2: The radar echo signal without interference and without transmission is , by counting the signal energy and averaging it to get the signal power when there is no interference , , For sampling points The corresponding echo signal, , is the number of sampling points of the received signal during the statistical time period; Step 1.3: When there is interference, the radar echo signal is: , where It is an interference signal. is the target echo signal, is the input system noise signal, The signal echo power is: , where , is the total number of sampling points of the received signal during the detection period; Step 1.4: Determine whether there is interference using a constant false alarm strategy and set the threshold , is the control coefficient, Step 1.5: If , then it is determined that there is interference in the current sector segment, and the interference flag and interference intensity of the current sector are output. ; Step 2: Identify interference types based on wavelet transform feature extraction Step 2.1: Echo signal Energy normalization is performed to keep the energy of all interference signals at the same level, that is, . Step 2.2: Echo Signal The discrete wavelet transform of is defined as: in, is the wavelet packet coefficient, is the discrete wavelet basis function, 、 is a positive integer, represents the scale factor, represents the translation factor; Step 2.3: The original signal can be reconstructed using wavelet packet coefficients and discrete wavelet basis functions. Step 2.4: Set different and Value, reconstruct the time domain signal, and get , No. layer wavelet packet coefficients, ; Only after 3 layers of wavelet packet decomposition, the wavelet domain signal component of the interference signal has good classification performance, so Take 0 to 3. Step 2.5: Calculate the echo power of the reconstructed time domain signal, , is the length of the reconstructed signal. The wavelet domain eigenvector of the signal is obtained, that is, Step 2.6: Analyze the wavelet domain signal components based on the wavelet domain eigenvectors. The signal components of the dense false target interference signal are regularly distributed in a sawtooth shape, while the signal components of the noise suppression interference are irregularly distributed.

2. The interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures according to claim 1 is characterized in that: Control coefficient The value range is 6dB to 15dB.

3. The interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures according to claim 1 is characterized in that: The value range is 0 to 3.

4. The interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures according to claim 1 is characterized in that: Used in electronic countermeasure systems.

5. The interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures according to claim 1 is characterized in that: Radar jammer identification system.

6. The interference direction finding and identification method using energy detection and wavelet transform in electronic countermeasures according to claim 1 is characterized in that: is 120.

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