Implementation method and device for anti-bait function of anti-radiation missile

Abstract

The invention discloses an implementation method and device for an anti-bait function of an anti-radiation missile, and the method comprises the steps: obtaining a batch of pulse description words, namely PDW, through a sliding window; performing angle clustering on the PDW to obtain a PDW clustering result; performing signal sorting on the PDW to generate a radiation source description word, namely EDW; performing angle clustering on the EDW to obtain an EDW clustering result; comparing the confidence of the PDW clustering result with the confidence of the EDW clustering result, and taking the azimuth angle and the pitch angle corresponding to the result with higher confidence as output; and the anti-radiation missile tracks the radiation source according to the output azimuth angle and pitch angle. According to the method, the leading edge angle measurement technology is adopted, the angle characteristics of the bait and radar aliasing signals are extracted, and the signal sorting and angle clustering technologies are combined, so that the accurate and stable tracking of the radiation source target is realized.

Description

Technical Field

[0001] The invention belongs to the field of anti-radiation technology, and in particular relates to a method and a device for realizing an anti-decoy function of an anti-radiation missile. Background Art

[0002] As one of the main weapons to destroy the enemy's air defense and anti-missile system, the anti-radiation missile identifies and sorts the received radiation source signals, detects the angle deviation signal between the drone and the target radiation source in real time, forms a control command to guide the drone to fly to the target and finally destroy the target. In order to deal with the threat of anti-radiation weapons, active decoys are deployed near the radar, and the decoy radiation signals are the same in the time domain, frequency domain, and energy domain. In order to protect the radar itself, the radar signal front will be covered by the signal front of the decoy radiation, and the decoy flashes so that the anti-radiation missile cannot stably track the same radiation source target through the front direction finding.

[0003] In the existing technical solutions, if only forward tracking is used, the anti-radiation weapon can only be guided close to the radar position, and stable tracking of a certain radiation source cannot be achieved. The estimation method of the flashing period can make the drone always track a certain decoy, but there are certain adaptability requirements for the flashing timing. Summary of the invention

[0004] Technical purpose: In view of the above technical problems, the present invention proposes a method and device for realizing the anti-decoy function of an anti-radiation missile, which adopts cutting-edge angle measurement technology, extracts the angle characteristics of decoy and radar aliasing signals, and combines signal sorting and angle clustering technology to realize accurate and stable tracking of radiation source targets.

[0005] Technical solution: To achieve the above technical objectives, the present invention adopts the following technical solution:

[0006] A method for realizing an anti-decoy function of an anti-radiation missile comprises the following steps:

[0007] S100, using a sliding window method to obtain a batch of pulse description words, that is, multiple PDW sequences, each PDW sequence corresponding to a set of azimuth angles and elevation angles;

[0008] S200, performing angle clustering on the PDW sequence to obtain a PDW clustering result;

[0009] S300, performing signal sorting on the PDW sequence to generate radiation source description words, that is, multiple EDW sequences;

[0010] S400, performing angle clustering on the EDW sequence to obtain an EDW clustering result;

[0011] S500, comparing the confidences of the PDW clustering result and the EDW clustering result, and taking the azimuth and elevation angles corresponding to the result with higher confidence as output;

[0012] S600: The anti-radiation missile tracks the radiation source according to the azimuth and elevation angle output in step S500.

[0013] Preferably, in the step S100, the sliding window method uses a pulse number greater than a preset number or a pulse duration greater than a preset duration as the window length.

[0014] Preferably, both step 200 and step S300 include the following angle clustering process:

[0015] (1) The azimuth angle θ and pitch angle corresponding to each input sequence Mapped to three-dimensional coordinate points (x, y, z) on the sphere:

[0016] z=R*sinθ

[0017]

[0018]

[0019] R is the radius of the sphere;

[0020] (2) Map the angle tolerance to the distance tolerance:

[0021]

[0022] Compare the coordinate values ​​of each input sequence with the coordinate values ​​of each cluster center. If the distance difference is less than the preset tolerance value, the input sequence belongs to the corresponding cluster; otherwise, a new cluster center is established.

[0023] (3) Compare the distance differences between all cluster centers. If the difference is less than the preset tolerance value, merge the neighboring clusters.

[0024] (4) Based on the merged cluster centers, each input sequence is re-assigned to a cluster;

[0025] (5) Eliminate clusters where the number of input sequences is less than a preset threshold;

[0026] (6) Calculate the centroid of the remaining cluster centers, that is, the average coordinate value, take the cluster center farthest from the total centroid, convert it into azimuth and elevation angles, and use it as the angle of the target radiation source;

[0027] If multiple clusters are at the same distance from the centroid, the one with the smallest azimuth angle is taken as the angle of the target radiation source;

[0028] (7) Calculate the DOA clustering confidence of all input sequences, that is, the percentage of the number of clustered PDWs corresponding to the output DOA to the number of PDWs participating in the clustering.

[0029] Preferably, the step 300 specifically includes the following steps:

[0030] Pre-sorting: placing the PDW into different pre-sorting grids according to the frequency value interval, dividing the PDW into corresponding pre-sorting grids, and obtaining multiple main sorting queues;

[0031] Main sorting: traverse each main sorting queue in turn, sort the repetition frequency period by the histogram sorting algorithm, generate EDW segments, and save the PDW sequence of each EDW segment;

[0032] Batching: Batching EDW fragments to generate the final EDW.

[0033] Preferably, in the pre-sorting step, the carrier frequency, the intra-pulse modulation type, etc. are configured as pre-sorting parameters of the PDW, different clustering indexes are generated according to the pre-sorting parameters of the PDW, and each PDW sequence is hung in a queue of a corresponding index in the order of arrival time TOA from small to large, and finally the clustering index is hung in the main sorting queue;

[0034] Preferably, in the main sorting step, the corresponding clustering table is found according to the clustering index in the main sorting queue, and the PDW queue of each pre-sorting clustering table is processed in turn;

[0035] The histogram sorting algorithm comprises the steps of:

[0036] Calculate the TOA differences between adjacent PDWs, count the number of occurrences of each difference, and form a first-level histogram;

[0037] Traverse each level one histogram and compare the number of occurrences of each difference and twice the difference with the threshold. The threshold is calculated as follows:

[0038] T threshold =k*(T / PRI)

[0039] Where, T is the total sampling interval, PRI is the TOA difference, and k (k<1) is the adjustable coefficient;

[0040] When both the difference and the double difference exceed the detection threshold, the corresponding PRI is used for sequence search. If the histogram of the corresponding level does not meet the PRI of the detection threshold, the number of interval pulses between the two pulses is increased, the histogram of the next level is calculated and accumulated with the statistical results of the histograms of the previous levels;

[0041] If the sequence retrieval is successful, an EDW segment is generated based on all the retrieved PDWs, the histogram is cleared, the first-level histogram is recalculated for the remaining PDWs, and the threshold comparison and retrieval process is repeated;

[0042] If the retrieval fails, the next level of histogram will continue to be calculated until the histogram level reaches the set threshold or the number of remaining PDWs does not meet the sorting threshold, and the main sorting ends.

[0043] A device for realizing the anti-decoy function of an anti-radiation missile, comprising:

[0044] The PDW acquisition module is used to acquire a batch of pulse description words, i.e., multiple PDW sequences, in a sliding window manner, each PDW sequence corresponding to a set of azimuth and elevation angles;

[0045] A signal sorting module, used for performing signal sorting on the PDW sequence to generate radiation source description words, i.e., multiple EDW sequences;

[0046] An angle clustering module, used for performing angle clustering on an input sequence to obtain a clustering result, wherein the input sequence is a PDW sequence or an EDW sequence;

[0047] The judgment and output module is used to compare the confidence of the PDW clustering results and the EDW clustering results, and take the azimuth and elevation angles corresponding to the results with higher confidence as output;

[0048] The execution module drives the anti-radiation missile to track the radiation source according to the azimuth and pitch angle determined by the judgment and output modules.

[0049] Beneficial effects: Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0050] The present invention adopts cutting-edge angle measurement technology to extract the angle characteristics of decoy and radar aliasing signals, and combines signal sorting and PDW angle clustering technology. The signal sorting technology ensures that when the time domain characteristics of the aliasing signal can be extracted, the frequency, pulse width, repetition rate and other information of the signal can be obtained as much as possible, and the PDW matching the characteristics is angle clustered, thereby improving the credibility of the result. For more complex aliasing scenarios that cannot be sorted, the DOA clustering results of the PDW are used to guide the tracking direction of the anti-radiation missile. The clustering process identifies several fixed clustering centers, and through the radiation source selection strategy, it is ensured that the anti-radiation missile can stably track the same target in the process of approaching the target position. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a method for realizing the anti-decoy function of an anti-radiation missile proposed by the present invention;

[0052] Figure 2Schematic diagram of the timing of the radar signal and three kinds of decoy signals in Example 1;

[0053] Figure 3 Schematic diagram of angle clustering for three bait scenarios;

[0054] Figure 4 Schematic diagram of drone trajectories in three decoy scenarios. DETAILED DESCRIPTION

[0055] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] Combined with Figure 1 The present invention proposes a method for realizing the anti-decoy function of an anti-radiation missile, which is suitable for the scenario that the anti-radiation weapon faces the radar decoy signal. The decoy radiation signal and the radar signal are similar or even overlapped in the time domain, spatial domain, frequency domain, energy domain, code domain, and polarization domain. Each decoy flashes sequentially according to a certain period, and the order of leading and lagging the radar in each period changes randomly. The time difference between each decoy and radar signal reaching the drone includes two aspects. One is the signal transmission time difference caused by the sequential flashing, and the other is the signal propagation time difference caused by the different distances between each radiation source and the drone. Therefore, when the drone is located at any position in space, at least one radiation source signal among the multiple radiation sources arrives at the anti-radiation seeker in advance. When there are multiple radiation sources aliasing in the leading edge signal, since it is difficult for each radiation source to be completely coherent, the angle of the leading edge detection is a random jump error angle; when there is no aliasing in the leading edge signal, the detection angle is fixed, so several fixed clustering centers are identified through the clustering process, and one of the target radiation sources is stably tracked.

[0057] The method for realizing the anti-decoy function of the anti-radiation missile proposed by the present invention, in order to meet the requirement of continuously and quickly sending azimuth and elevation angle information to the target link and avoid the waiting time of pulse accumulation, adopts a sliding window method, takes the number of pulses greater than N or the pulse duration greater than T as the window length, accumulates a certain number of pulses each time, and obtains EDW information from PDW by sorting the pulse signal. PDW contains information such as arrival time, arrival angle, pulse width, frequency, amplitude, etc. Through the sorting method, the PDW with consistent rules is extracted and statistically obtained to obtain the radiation source description information EDW, and EDW contains the repetition period, frequency value, pulse width value, arrival angle, average amplitude, etc. obtained according to PDW. Considering the random overlap relationship between the target radar pulse and the decoy pulse, the pulse width and repetition period are both quantities of random jitter changes, which will become unfavorable factors for PRI sorting. Therefore, in the case of no sorting result, the azimuth and elevation angle information is output by angle clustering the PDW.

[0058] Embodiment 1

[0059] First, step one performs an angle clustering on the PDW within the sliding window. The specific steps are:

[0060] (1) The azimuth angle range is 0° to 360°, and the elevation angle range is -90° to 90°. In order to achieve spatial stereoscopic angle clustering, the azimuth and elevation angles of the PDW are mapped to three-dimensional coordinate points on the sphere.

[0061] The specific mapping method is:

[0062] z=R*sinθ

[0063]

[0064]

[0065] (2) Map the angle tolerance to the distance tolerance:

[0066]

[0067] Each PDW coordinate value is compared with each cluster center coordinate value. If the distance difference is less than the tolerance value, the PDW belongs to the cluster; otherwise, a new cluster is established. The initial cluster center is the PDW coordinate. Angle tolerance θ thres Manual setting, refer to engineering measurement error. Azimuth and elevation are three-dimensional spatial angle descriptions. For example, 0 degrees and 360 degrees of azimuth are the same angle, which makes it inconvenient to calculate the average value of the azimuth, and it is also inconvenient to calculate the angle between the two groups (azimuth, elevation). After converting to distance, it is convenient to calculate the distance between two points, and the calculation is more accurate.

[0068] (3) Compare the distance differences between cluster centers. If they are less than the tolerance value, merge the neighboring clusters.

[0069] (4) Based on the merged cluster centers, each PDW is reassigned to a new cluster.

[0070] (5) According to the number of PDWs of each cluster, the clusters with fewer occurrences are eliminated. The judgment method is to take K% of the cluster with the largest number of PDWs as the PDW number threshold for elimination. K% is a percentage threshold, which is generally determined based on experience.

[0071] (6) Calculate the centroid of the remaining cluster centers, that is, the average coordinate value, take the cluster center farthest from the total centroid, convert it into azimuth and elevation angles, and use it as the angle of the target radiation source. The total centroid is the position determined by the calculated average coordinate value. If multiple clusters are at the same distance from the centroid, since the azimuth is the angle between the radiation source and the true north direction, the one with the smallest azimuth can be used as the angle of the target radiation source to ensure that the anti-radiation missile can stably track the same target during flight.

[0072] (7) The DOA (direction of arrival) cluster confidence of PDW is the percentage of the number of cluster PDWs corresponding to the output DOA to the number of PDWs participating in the cluster. For example, the number of PDWs participating in the angle cluster is N. After the multi-step calculation of the angle cluster, the remaining clusters after elimination are obtained after step (5). The specific results are the cluster centers of the remaining clusters and the number of PDWs belonging to this cluster. Finally, the cluster farthest from the total centroid is taken as the result output. The number of PDWs contained in this cluster is M, and the confidence is M / N.

[0073] As attached Figure 2 As shown in the figure, the three decoys always cover the leading edge of the radar signal, and the advance time fluctuates randomly. Therefore, the leading edge angle measurement may measure the decoy or the aliased part of the signal to obtain a randomly jumping angle. The 200 PDWs are clustered. As shown in the figure, cluster centers are formed at the decoy angles (10°, 10°), (15°, 20°), and (18°, -30°). According to the decoy selection strategy, (18°, -30°) is finally output as the target direction.

[0074] Since the decoy signal has the same characteristics as the radar signal, when the decoy signal is used to cover the radar, the aliased pulse may have jitter in the repetition and pulse width, but it still has the emission law of the original radar signal. Due to the timing alternation of the decoy, the angle of the decoy may be measured when measuring the leading edge, or the random angle of the aliased part may be measured. Therefore, when sorting, no angle comparison is performed. After the EDW is formed, the PDW belonging to the EDW is clustered.

[0075] In steps 2 and 3, the batch of PDWs is subjected to signal sorting. The specific steps are as follows:

[0076] The sorting stage is divided into three processes: pre-sorting, main sorting, and batching. Pre-sorting dilutes the pulse density flow. The carrier frequency, intra-pulse modulation type, etc. can be configured as pre-sorting parameters. Different clustering indexes are generated according to the pre-sorting parameters of PDW, and PDWs are placed in the queue of corresponding indexes in the order of TOA (arrival time) from small to large. Finally, the clustering index is placed in the main sorting queue.

[0077] The main sorting finds the cluster table according to the cluster index in the main sorting queue, and processes the PDW queue of each pre-sorting cluster table in turn, using CDIF (cumulative difference histogram algorithm). CDIF first calculates the TOA difference between adjacent PDWs, counts the number of occurrences of each difference, and forms a first-level histogram. Traverse the difference histogram, compare the number of occurrences of each difference and its twice difference with the threshold, and the threshold calculation method is as follows:

[0078] T threshold=k*(T / PRI)

[0079] Where T is the total sampling interval, PRI is the TOA difference, and k (k<1) is an adjustable coefficient. When a certain difference and its twice difference both exceed the detection threshold, the sequence search is performed with the PRI. If the histogram of this level does not meet the PRI of the detection threshold, the number of interval pulses between the two pulses is increased, the next level of histogram is calculated and the statistical results of the previous levels of histograms are accumulated. If the sequence search is successful, an EDW segment is generated based on all the retrieved PDWs, the histogram is cleared, the first level histogram is recalculated for the remaining PDWs, and the process is repeated; if the search fails, the next level of histogram is calculated. The main sorting ends until the histogram level reaches the set threshold or the number of remaining PDWs does not meet the sorting threshold. Each EDW segment saves the PDW sequence belonging to the EDW.

[0080] The batch merge process merges the EDW fragments generated by the primary sorting to form the final EDW. If there are multiple EDWs, the EDW containing the largest number of PDWs is taken as the result.

[0081] Perform angle clustering on the PDWs contained in the final EDW, following the same steps as above, and calculate the confidence.

[0082] Step 4: Compare the confidence of the DOA clustering results of PDW and the confidence of the sorting clustering results of EDW, and take the result with higher confidence and upload it to the host computer.

[0083] Combined with Figure 4 The scene simulation is expressed as distance coordinates, the radar position is (0, 0, 0), the three decoy station positions are (0, 300, 0), (-150, 240, 0), (150, 240, 0), the UAV speed is 185km / h, the maximum time that the decoy leads and lags the radar signal is 300ns, and the pulse front measurement time is 53ns. The simulation results are shown in the attached Figure 4 As shown in the figure, the purple dots represent the radar positions, the other dots represent the decoy station positions, and the star-shaped track points represent the drone tracks. As can be seen from the figure, as the drone flies towards the decoy position, it continuously makes angle clustering decisions, and eventually the drone is able to hit the decoy.

[0084] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for realizing the anti-decoy function of an anti-radiation missile, characterized in that: Includes steps: S100, using a sliding window method to obtain a batch of pulse description words, that is, multiple PDW sequences, each PDW sequence corresponding to a set of azimuth angles and elevation angles; S200, performing angle clustering on the PDW sequence to obtain a PDW clustering result; S300, performing signal sorting on the PDW sequence to generate radiation source description words, that is, multiple EDW sequences; S400, performing angle clustering on the EDW sequence to obtain an EDW clustering result; S500, comparing the confidences of the PDW clustering result and the EDW clustering result, and taking the azimuth and elevation angles corresponding to the result with higher confidence as output; S600: The anti-radiation missile tracks the radiation source according to the azimuth and elevation angle output in step S500.

2. The method for realizing the anti-decoy function of an anti-radiation missile according to claim 1, characterized in that: In the step S100, the sliding window method uses the pulse number greater than the preset number or the pulse duration greater than the preset duration as the window length.

3. The method for realizing the anti-decoy function of an anti-radiation missile according to claim 1, characterized in that: Both step 200 and step S300 include the following angle clustering process: (1) The azimuth angle θ and pitch angle corresponding to each input sequence Mapped to three-dimensional coordinate points (x, y, z) on the sphere: z=R*sinθ R is the radius of the sphere; (2) Map the angle tolerance to the distance tolerance: Compare the coordinate values ​​of each input sequence with the coordinate values ​​of each cluster center. If the distance difference is less than the preset tolerance value, the input sequence belongs to the corresponding cluster; otherwise, a new cluster center is established. (3) Compare the distance differences between all cluster centers. If the difference is less than the preset tolerance value, merge the neighboring clusters. (4) Based on the merged cluster centers, each input sequence is re-assigned to a cluster; (5) Eliminate clusters where the number of input sequences is less than a preset threshold; (6) Calculate the centroid of the remaining cluster centers, that is, the average coordinate value, take the cluster center farthest from the total centroid, convert it into azimuth and elevation angles, and use it as the angle of the target radiation source; If multiple clusters are at the same distance from the centroid, the one with the smallest azimuth angle is taken as the angle of the target radiation source; (7) Calculate the DOA clustering confidence of all input sequences, that is, the percentage of the number of clustered PDWs corresponding to the output DOA to the number of PDWs participating in the clustering.

4. The method for realizing the anti-decoy function of an anti-radiation missile according to claim 1, characterized in that: The step 300 specifically includes the following steps: Pre-sorting: placing the PDW into different pre-sorting grids according to the frequency value interval, dividing the PDW into corresponding pre-sorting grids, and obtaining multiple main sorting queues; Main sorting: traverse each main sorting queue in turn, sort the repetition frequency period by the histogram sorting algorithm, generate EDW segments, and save the PDW sequence of each EDW segment; Batching: Batching EDW fragments to generate the final EDW.

5. The method for realizing the anti-decoy function of an anti-radiation missile according to claim 4, characterized in that: In the pre-sorting step, the carrier frequency, intra-pulse modulation type, etc. are configured as pre-sorting parameters of the PDW, different clustering indexes are generated according to the pre-sorting parameters of the PDW, and each PDW sequence is hung in the queue of the corresponding index in the order of arrival time TOA from small to large, and finally the clustering index is hung in the main sorting queue.

6. The method for realizing the anti-decoy function of an anti-radiation missile according to claim 4, characterized in that: In the main sorting step, the corresponding cluster table is found according to the cluster index in the main sorting queue, and the PDW queue of each pre-sorting cluster table is processed in turn; The histogram sorting algorithm comprises the steps of: Calculate the TOA differences between adjacent PDWs, count the number of occurrences of each difference, and form a first-level histogram; Traverse each level one histogram and compare the number of occurrences of each difference and twice the difference with the threshold. The threshold is calculated as follows: T threshold =k*(T / PRI) Where, T is the total sampling interval, PRI is the TOA difference, and k (k<1) is the adjustable coefficient; When both the difference and the double difference exceed the detection threshold, the corresponding PRI is used for sequence search. If the histogram of the corresponding level does not meet the PRI of the detection threshold, the number of interval pulses between the two pulses is increased, the histogram of the next level is calculated and accumulated with the statistical results of the histograms of the previous levels; If the sequence retrieval is successful, an EDW segment is generated based on all the retrieved PDWs, the histogram is cleared, the first-level histogram is recalculated for the remaining PDWs, and the threshold comparison and retrieval process is repeated; If the retrieval fails, the next level of histogram will continue to be calculated until the histogram level reaches the set threshold or the number of remaining PDWs does not meet the sorting threshold, and the main sorting ends.

7. A device for realizing the anti-decoy function of an anti-radiation missile, characterized in that: include: The PDW acquisition module is used to acquire a batch of pulse description words, i.e., multiple PDW sequences, in a sliding window manner, each PDW sequence corresponding to a set of azimuth and elevation angles; A signal sorting module, used for performing signal sorting on the PDW sequence to generate radiation source description words, i.e., multiple EDW sequences; An angle clustering module, used for performing angle clustering on an input sequence to obtain a clustering result, wherein the input sequence is a PDW sequence or an EDW sequence; The judgment and output module is used to compare the confidence of the PDW clustering results and the EDW clustering results, and take the azimuth and elevation angles corresponding to the results with higher confidence as output; The execution module drives the anti-radiation missile to track the radiation source according to the azimuth and pitch angle determined by the judgment and output modules.

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