CN110632570A - Active interference detection method based on multi-stage judgment - Google Patents
Active interference detection method based on multi-stage judgment Download PDFInfo
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Abstract
The invention relates to the field of anti-interference, in particular to an active interference detection method based on multi-stage judgment. The invention adopts different characteristics to simultaneously detect noise suppression interference and dense false target interference respectively, and judges that the interference meets the two characteristics simultaneously, namely the interference is combined. In order to solve the problem of false detection from the side lobe caused by unreasonable threshold setting when the interference is too strong, the method judges whether the current interference direction is located in the side lobe or not through the shadow comparison of the main antenna and the auxiliary antenna, thereby avoiding the false interference direction.
Description
Technical Field
The invention relates to the field of anti-interference, in particular to an active interference detection method based on multi-stage judgment.
Background
In modern war, the status occupied by electronic countermeasure technology and anti-countermeasure technology is increasingly important. Various interference technologies emerge endlessly, and the influence on the radar detection performance is more serious. The most common interference means at present include noise suppression interference and dense decoy interference. In radar anti-rejection methods, it is necessary to detect the source of the disturbance as the first thing. The radar can be known to be interfered and oriented accurately at the first time, and then countermeasures can be taken in a targeted mode. Through the development of recent decades, the active interference detection methods mainly include the following types:
1) average power based interference detection
Firstly, sampling signals received by a radar, intercepting a section of signals with the length being the pulse repetition period of the signals transmitted by the radar, calculating the energy of the signals, and judging whether the received signals have noise suppression interference or not by comparing whether the average power of the section of signals exceeds a set threshold value or not. The method is effective for detecting noise suppression interference, but cannot effectively detect dense false target interference. When the method orients the interference source, the direction with the strongest power in the region of which the average power detection threshold is over is often used as the orientation result of the interference source, and once the threshold is set unreasonably, more false alarms may occur in the side lobe region.
2) Interference detection based on post-pulse-pressure peak-to-average ratio
After the radar transmitting signals with large bandwidth are subjected to pulse compression, the output signals of the radar transmitting signals have an energy accumulation phenomenon. The method is characterized in that the output result has larger amplitude fluctuation, and the noise suppression interference signal generally does not have the phenomenon after pulse compression, so that the fluctuation degree of the received signal after pulse compression is calculated by taking the fluctuation degree as a characteristic to judge whether the radar noise suppression interference exists in the received signal. The method has certain detection probability on noise suppression interference, but has no effect on interference source orientation.
3) Interference detection based on fractal characteristics
The fractal characteristics of the active suppression interference are analyzed, and the characteristics are described through a box dimension and an information dimension, and finally the characteristics are used for detecting the interference signal. The method has large calculation amount and complex hardware implementation.
Disclosure of Invention
Aiming at the defects of the background technology, the invention utilizes the energy domain characteristics, not only can finish the detection of noise suppression interference, but also can consider the detection of dense false target interference. And respectively adopting different characteristics to simultaneously detect the noise suppression interference and the dense false target interference, and judging that the interference meeting the two characteristics is combined interference. In order to solve the problem of false detection from the side lobe caused by unreasonable threshold setting when the interference is too strong, the method judges whether the current interference direction is located in the side lobe or not through the shadow comparison of the main antenna and the auxiliary antenna, thereby avoiding the false interference direction.
The technical scheme of the invention is as follows: the active interference detection method based on multi-stage judgment comprises the following steps:
step one, comparing two characteristics simultaneously, and detecting noise suppression interference and dense false target interference respectively. The absolute threshold and the proportional threshold ensure that the two interference forms can be detected simultaneously.
And step two, comparing the data of the main channel and the auxiliary channel to ensure that the interference detection false alarm in the side lobe direction cannot be caused under the saturation attack.
And step three, performing windowing condensation processing on the echo signals judged as the true interference in the main lobe direction to obtain the central directions of all interference directions.
And step four, performing interframe correlation processing, and performing correlation processing on the interference source directing lines of multiple frames, wherein the interference source directing lines meeting a certain criterion can be identified as a real interference source, so that the false alarm probability is further reduced.
The invention has the advantages that: the method can simultaneously detect noise suppression interference, dense false target interference and combined interference; the device has azimuth orientation capability and higher azimuth orientation precision; false interference detection in the side lobe direction can be suppressed and the false alarm probability is low. Aiming at the problem of low pointing accuracy caused by the fact that the traditional method only searches the energy maximum direction as the interference pointing direction, the method adopts the window-dividing condensation idea to carry out weighted condensation on the directions of all PRIs really interfered by the main lobe direction, finds the gravity centers of all PRIs and avoids unstable pointing of the maximum energy method. In order to further improve the stability of interference detection, the method adopts an interframe correlation method, and the interference directions in adjacent directions are condensed into one line through multi-circle comparison, so that some transient interference directions are removed, and the false alarm probability of interference detection is further reduced.
Drawings
Fig. 1 is a schematic block diagram of an active interference detection method based on multi-stage decision.
Detailed Description
The noun explains: PRI: the radar repeats the cycle.
A protection unit: in order to prevent the noise estimation from being inaccurate, a plurality of units directly adjacent to the left and right of the current unit are removed (generally taking 2 to 3), and do not participate in the noise estimation.
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the method of the present invention includes the following steps, step one, analyzing data after radar pulse pressure, each PRI is a sample, and counting the absolute value of each data unit of the current sample.
And (3) performing threshold judgment and statistics on the data units in each PRI: and the noise suppression type interference and the dense false target interference can be ensured to be detected by reasonably setting the absolute threshold and the proportional threshold. In general, the absolute threshold is set 30dB higher than the noise in the radar, and the proportional threshold is set at 0.1 to 0.2. The noise suppression type interference is characterized in that the amplitude of most data units is raised, and the dense false target interference is characterized in that pulse compression is obtained, so that the amplitude of the data unit where the false target is located is greatly improved, but the amplitude of the data unit where the false target does not exist is not improved basically or is improved only in a small amplitude. Thus, both interference patterns cannot be effectively detected by comparison of absolute thresholds alone. After the proportional threshold is added, the absolute threshold can be set higher, and meanwhile, the interference of the dense false target can be ensured to be correctly detected by counting the proportion of the data unit passing the absolute threshold, so that false alarm caused by fluctuation of noise is avoided. If the data of the current PRI can cross the absolute threshold and the proportional threshold, marking the current PRI as interference. Further, the interference is preliminarily divided into noise suppression interference (the ratio of the over-absolute threshold point reaches 0.4) and other interference (the ratio of the over-threshold point does not reach 0.4) by increasing the ratio threshold. Through preliminary discrimination, it can be determined whether the current PRI carries noise suppression interference characteristics.
The PRI data determined to have interference is processed as follows. Typically, a PRI has 4000-6000 numbersAccording to the unit. Arranging the data in sequence, taking i to i +500 data for processing, and setting SiRepresenting the amplitude value of the ith data cell of the current PRI, NiRepresents SiThe noise estimate of (2). N is a radical ofiCalculated from the following formula:
in the formula, SjRepresenting the amplitude value of the jth data cell of the current PRI. Generally, k units (except for the protection unit) on the left and right of the ith data unit of the current PRI are taken, the average values of the k units are respectively calculated, and the larger value is taken as SiNoise estimate N ofi. Calculating Si/NiIf the current PRI number is larger than 10dB, judging that a false target exists in the ith data unit of the current PRI. And (4) counting the data units from i to i +500 in three sections, wherein the value of i is generally 2000, 2500 and 3000. And if the number of the data units with the false targets in one section of the three sections is more than or equal to 10, judging that the current PRI has the dense false target interference characteristics.
And analyzing the current PRI, and if the interference exists, further searching the interference type of the current PRI. If the interference characteristics are judged to be with noise suppression interference characteristics and the dense false target interference characteristics are not possessed during preliminary distinguishing, the current PRI is judged to be noise suppression interference; if the interference is judged to be other interference during the primary distinguishing and then is further judged to have dense false target interference characteristics, the current PRI is judged to be dense false target interference; if the interference characteristic is judged to be with noise suppression during the initial distinguishing, and then the subsequent further judgment is made to be with the dense false target interference characteristic, the current PRI is judged to be combined interference; if the interference is judged to be other interference in the preliminary discrimination, and the subsequent further judgment is carried out without the dense decoy interference characteristics, the interference is judged to be unknown interference, and finally, no interference type is given to the unknown interference.
And step two, analyzing the data after pulse pressure of the main channel and the auxiliary channel. For PRIs determined to be noisy, a noise estimate is made for each PRI. Typically, a PRI has 4000-6000 data cells. The data are arranged in sequence, and the data from a to b are taken for processing, wherein a is generally 2000, and b is generally the maximum data unit number of the current PRI. Let SkRepresentsThe amplitude value of the current PRI kth data unit, PN, represents the noise estimate for the current PRI. PN is calculated from the following formula:
setting PN1Representing the noise estimate, PN, of the current PRI main channel2Representing the noise estimate of the current PRI auxiliary channel. The noise estimates for the primary and secondary channels are processed as follows:
and when the alpha is less than 15dB, judging that the interference carried by the current PRI is the incoming false interference in the main antenna side lobe direction, marking a false identifier on the current PRI, and not performing subsequent processing, namely, judging that the direction of the current PRI is not the direction of the interference source. Otherwise, judging that the interference carried by the current PRI is the real interference coming from the main lobe direction of the main antenna, marking a real interference identifier on the current PRI, and performing subsequent azimuth aggregation processing.
And step three, carrying out azimuth aggregation treatment on the PRI of the real interference direction coming from the main lobe direction of the main antenna determined in the previous step to obtain the central azimuth of the interference source. When external interference comes in, PRI of continuous multiple periods is often caused to be judged as real interference coming in from the main lobe direction of the main antenna. Occasionally a small number of discontinuous PRIs are determined to be true interference coming in the main antenna lobe direction and will be filtered out in this stage of processing. The radar scans a circle, and typically 3000-10000 PRIs can be obtained, which are sequentially arranged and numbered with the true north direction as the starting point. With the windowing process, the window length is typically set to 15 PRIs. In a window length, namely 10 PRIs in 15 continuous PRIs are judged as the actual interference coming in from the main lobe direction of the main antenna, namely the active interference exists in the current electromagnetic environment, the PRI number of the first PRI in the window length judged as the actual interference coming in from the main lobe direction of the main antenna is recorded as the interference starting code. In a window length, if the PRI of continuous 6 periods is not judged as the real interference coming from the main lobe direction of the main antenna, judging that the real interference coming from the main lobe direction of the antenna does not exist in the current window length, recording the PRI number of the last PRI judged as the real interference coming from the main lobe direction of the main antenna in the window length, and recording the PRI number as the interference ending code. In order to obtain the accurate position of the interference source, the center position of the interference is calculated by adopting a gravity center method. The center orientation is calculated by:
in which AC represents the center orientation code of the interference, AiAzimuth code, PN, representing the ith PRIiRepresenting the noise estimate for the ith PRI. L represents interference start code and K represents interference end code. The PRI between L and K is the PRI for the duration of the interference. The strength of the noise estimate is used to perform a weighted calculation to obtain the center orientation AC.
And step four, performing interframe correlation processing on the interference direction detected by each circle of scanning. And counting the center position of each circle of interference directions, and performing correlation statistics on the interference directions with similar center positions appearing in different circles. Generally, for interference directions with interference center positions located within plus and minus one beam width (3dB width) among different scanning circles, the interference directions are regarded as the same interference direction. More than 2 circles can continuously appear in one statistical period (3 circles) to be considered as the true interference direction. And further eliminating false interference through inter-frame correlation processing.
Claims (5)
1. An active interference detection method based on multi-stage judgment is characterized in that: the method comprises the following steps:
step one, carrying out threshold judgment and statistics on data units in each PRI: the absolute threshold is set to be 30dB higher than the noise in the radar, the proportional threshold is set to be 0.1-0.2, and if the data of the current PRI can pass the absolute threshold and the proportional threshold, the current PRI is marked as interference;
raising the proportional threshold, and preliminarily distinguishing the interference into noise suppression interference and other interference;
judging dense false target interference characteristics;
preliminary analysis of interference type: if the interference characteristics are judged to be with noise suppression interference characteristics and the dense false target interference characteristics are not possessed during preliminary distinguishing, the current PRI is judged to be noise suppression interference; if the interference is judged to be other interference during the primary distinguishing and then is further judged to have dense false target interference characteristics, the current PRI is judged to be dense false target interference; if the interference characteristic is judged to be with noise suppression during the initial distinguishing, and then the subsequent further judgment is made to be with the dense false target interference characteristic, the current PRI is judged to be combined interference; if the interference is judged to be other interference in the preliminary discrimination, and the subsequent further judgment is carried out without the dense false target interference characteristics, the interference is judged to be unknown interference;
step two, removing false interference coming from the direction of the main antenna side lobe;
thirdly, performing azimuth aggregation treatment on the PRI of the real interference direction coming from the main lobe direction of the main antenna determined in the second step to obtain the central azimuth of the interference source;
and step four, performing inter-frame association processing, and performing association processing on the interference source directing lines of multiple frames, wherein the interference source directing lines meeting the criterion are determined as real interference sources.
2. The active interference detection method based on multi-stage decision as claimed in claim 1, characterized in that: the method for judging the dense false target interference characteristics comprises the following steps: and processing the PRI data unit judged to have interference: arranging PRI data in sequence, taking i to i +500 data for processing, and setting SiRepresenting the amplitude value of the ith data cell of the current PRI, NiRepresents SiThe noise estimate of (2); n is a radical ofiCalculated from the following formula:
in the formula, SjRepresenting the amplitude value of the jth data unit of the current PRI, wherein k represents k units on the left and right of the ith data unit of the current PRI; calculating Si/NiIf the current PRI is greater than 10dB, the ith data unit of the current PRI is judgedAnd if the number of the data units with the false targets in one section of the three sections is more than or equal to 10, judging that the current PRI has the dense false target interference characteristics.
3. The active interference detection method based on multi-stage decision as claimed in claim 1, characterized in that: the direction for removing the false interference coming from the main antenna side lobe direction is shown as follows; for PRIs determined to have interference, performing a noise estimate for each PRI; taking the data from a to b for processing, and setting SkRepresenting the amplitude value of the kth data unit of the current PRI, and PN representing the noise estimate of the current PRI, then
Setting PN1Representing the noise estimate, PN, of the current PRI main channel2Representing the noise estimate for the current PRI auxiliary channel, the main and auxiliary channel noise estimates are processed as follows:
when alpha is less than 15dB, marking a false identifier for PRI without subsequent processing; otherwise, marking the PRI with a real interference identifier.
4. The active interference detection method based on multi-stage decision as claimed in claim 1, characterized in that: and calculating the center position of the interference by adopting a gravity center method, wherein the center position is calculated by the following formula:
in which AC represents the center orientation code of the interference, AiAzimuth code, PN, representing the ith PRIiRepresents the noise estimate for the ith PRI, L represents the interference start code, and K represents the interference end code.
5. The active interference detection method based on multi-stage decision as claimed in claim 1, characterized in that: the criterion for the direction of the interference source in step four is: counting the center position of each circle of interference directions, and performing correlation statistics on the interference directions with similar center positions appearing in different circles; for interference directions of which the interference center position is located in a positive beam width and a negative beam width among different scanning circles, the interference directions are considered as the same interference direction; more than 2 circles can continuously appear in a statistical period, and the fact that interference orientation is true is considered.
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|---|---|---|---|---|
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4584579A (en) * | 1983-11-01 | 1986-04-22 | The United States Of America As Represented By The Secretary Of The Air Force | Medium PRF pulse doppler radar processor for dense target environments |
| CN103235289A (en) * | 2013-04-19 | 2013-08-07 | 武汉滨湖电子有限责任公司 | Radar double-gate two-step branch prediction flight path tracking method |
| US20140347213A1 (en) * | 2012-03-09 | 2014-11-27 | U.S. Army Research Laboratory Attn: Rdrl-Loc-I | Method and System for Estimation and Extraction of Interference Noise from Signals |
| CN104931939A (en) * | 2015-06-03 | 2015-09-23 | 河海大学 | Missile-borne forwarding type interference inhibition method based on false target characteristics |
| CN107045122A (en) * | 2017-03-30 | 2017-08-15 | 安徽工程大学 | A kind of object detection system and its detection method |
| CN108037493A (en) * | 2017-11-27 | 2018-05-15 | 西安电子科技大学 | Double threshold CFAR detection method under clutter subspace based on multistatic radar |
| CN109031221A (en) * | 2018-08-20 | 2018-12-18 | 中国人民解放军海军航空大学 | Data Layer resists intensive false target jamming profile method based on the radar of various dimensions feature extraction |
-
2019
- 2019-08-29 CN CN201910805705.3A patent/CN110632570B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4584579A (en) * | 1983-11-01 | 1986-04-22 | The United States Of America As Represented By The Secretary Of The Air Force | Medium PRF pulse doppler radar processor for dense target environments |
| US20140347213A1 (en) * | 2012-03-09 | 2014-11-27 | U.S. Army Research Laboratory Attn: Rdrl-Loc-I | Method and System for Estimation and Extraction of Interference Noise from Signals |
| CN103235289A (en) * | 2013-04-19 | 2013-08-07 | 武汉滨湖电子有限责任公司 | Radar double-gate two-step branch prediction flight path tracking method |
| CN104931939A (en) * | 2015-06-03 | 2015-09-23 | 河海大学 | Missile-borne forwarding type interference inhibition method based on false target characteristics |
| CN107045122A (en) * | 2017-03-30 | 2017-08-15 | 安徽工程大学 | A kind of object detection system and its detection method |
| CN108037493A (en) * | 2017-11-27 | 2018-05-15 | 西安电子科技大学 | Double threshold CFAR detection method under clutter subspace based on multistatic radar |
| CN109031221A (en) * | 2018-08-20 | 2018-12-18 | 中国人民解放军海军航空大学 | Data Layer resists intensive false target jamming profile method based on the radar of various dimensions feature extraction |
Non-Patent Citations (3)
| Title |
|---|
| DAVID A. SHNIDMAN，NATHAN R. SHNIDMAN: "Sidelobe Blanking with Expanded Models", 《 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 * |
| GUANG CAI，YINBO SHAO，RONGFENG LI， LIMING DING: "A method for countering dense false target jamming based on correlation sample selection", 《2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING 》 * |
| 孙殿星等: "基于多特征的密集假目标干扰融合识别与抑制", 《系统工程与电子技术》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113494959A (en) * | 2020-04-08 | 2021-10-12 | 苏州文泽可智能科技有限公司 | Method for judging noise of plant |
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