A radar unknown interference clustering method based on adaptive DBSCAN algorithm

Abstract

The present invention discloses a radar unknown interference clustering method based on an adaptive DBSCAN algorithm. The present invention improves the traditional DBSCAN algorithm. When determining the maximum radius, the maximum radius corresponding to the core point is updated based on a preset minimum number of points and the distance between the core point and its corresponding data point to obtain an updated maximum radius, thereby achieving adaptive changes in the maximum radius corresponding to each core point, and then using the updated maximum radius to update each core point; thereby reducing clustering time, improving clustering efficiency, and enhancing the clustering effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a radar unknown interference clustering method based on an adaptive DBSCAN algorithm. Background Art

[0002] Radar is widely used in detection, communication, early warning, and navigation scenarios, operating around the clock and in all weather conditions. However, with the increasing complexity of the electromagnetic environment, various active interference signals are emerging, severely degrading radar detection performance. Therefore, effective anti-interference measures are necessary to ensure proper operation of radar. Interference identification, as the primary step in anti-interference efforts, plays a crucial role by accurately identifying interference types and providing guidance for the effective implementation of anti-interference measures.

[0003] Current mainstream radar active jammer identification methods primarily rely on closed-set identification for known jammers. Closed-set identification refers to identifying only known targets in the presence of radar jammers, without making any identification judgments for unknown targets. However, advancements in radar jamming technology have led to rapidly evolving jammer patterns, making traditional closed-set identification inadequate for current practical applications. Consequently, research on open-set identification methods for radar active jammers is gaining increasing attention. However, in open-set identification, after identifying unknown jammers, the radar's data becomes chaotic, making it impossible to determine which of these signals are meaningful. Therefore, clustering the jammer signals is necessary, grouping those with similar characteristics into a single category to facilitate the subsequent detection and identification of new jammers in the environment. Clustering algorithms can be used to achieve this clustering of sample points.

[0004] The traditional DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters data points by inputting two parameters: maximum radius and minimum number of points. This requires the DBSCAN algorithm to optimize parameter selection through a series of methods during use, making the accurate clustering of unknown interference time-consuming and inefficient. In addition, since the minimum number of points and maximum radius remain unchanged during the clustering iteration process, this limits the clustering effect of the traditional DBSCAN algorithm on complex data with uneven density. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a radar unknown interference clustering method based on the adaptive DBSCAN algorithm. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] The present invention provides a radar unknown interference clustering method based on an adaptive DBSCAN algorithm, which defines ε-area, core points, direct density reachability, density reachability, density connectivity, noise points, boundary points and unvisited points respectively; the method includes:

[0007] Preprocessing the obtained interference signal to obtain a time-frequency diagram and an RD diagram of the interference signal;

[0008] Using a radar active interference recognition network model based on a multi-layer channel attention mechanism, feature extraction is performed on the time-frequency graph and the RD graph respectively to obtain a time-frequency feature graph and an RD feature graph;

[0009] Performing dimensionality reduction processing on the time-frequency feature map and the RD feature map to obtain feature parameters of several dimensions as data points;

[0010] Processing all data points based on the preset maximum radius and the preset minimum number of points to obtain a corresponding core point set;

[0011] Randomly select any core point in the core point set, and obtain an initial maximum radius based on the distance between the core point and the remaining data points and a preset minimum number of points;

[0012] The first core point in the core point set is updated according to the initial maximum radius; for each core point in the core point set except the first core point, each core point is sequentially used as the current core point, and based on the current core point and the directly density-reachable point corresponding to it, the maximum radius within the cluster of the cluster to which the current core point belongs is obtained; based on the current core point and the unvisited point corresponding to it, an adjustment parameter is obtained; based on the maximum radius within the cluster and the adjustment parameter, an updated maximum radius of the current core point is obtained; and the current core point is updated according to the updated maximum radius, thereby obtaining an updated core point set.

[0013] Based on the updated core point set, a clustering result of each unknown interference in the interference signal is obtained.

[0014] In one embodiment of the present invention, the ε-area, core point, direct density reachable, density reachable, density connected, noise point, boundary point and unvisited point are defined respectively, including:

[0015] The ε-domain is defined as follows: for a data point p, a circle is set with p as the center and a preset maximum radius as the radius. The set of data points contained in the circle is called the ε-domain;

[0016] Definition of the core point: In the ε-domain of a data point, if the number of elements contained is greater than or equal to the preset minimum number of points, then the data point is called a core point;

[0017] Definition of direct density reachability: If a data point x2 is contained in the ε-domain of a data point x1, and the data point x1 is a core point, then x2 is said to be directly density reachable from x1;

[0018] The density is defined as follows: if there are data points x1, x2, ..., x N-1 , x N , and x i+1 By x i Direct density can be reached, then x N It is reachable by x1 density; where i∈[1,N], N≥2;

[0019] Definition of density connection: If for data point x1 and data point x2, there exists a core point x3 such that both x1 and x2 are density-reachable from the core point x3, then data point x1 and data point x2 are said to be density-connected;

[0020] Definition of the noise point: a point that does not belong to any cluster is a noise point;

[0021] Defining the boundary points: data points that are within the maximum radius of a core point but are not core points are boundary points;

[0022] Definition of the unvisited points: Data points that are not defined as core points, boundary points, or noise points are unvisited points.

[0023] In one embodiment of the present invention, the step of preprocessing the obtained interference signal to obtain a time-frequency diagram and an RD diagram of the interference signal includes:

[0024] Performing short-time Fourier transform on the interference signal obtained within a pulse repetition period to obtain a time-frequency diagram of the interference signal;

[0025] Perform moving target detection on the interference signal under the condition of pulse accumulation to obtain an RD graph of the interference signal.

[0026] In one embodiment of the present invention, a radar active interference identification network model based on a multi-layer channel attention mechanism is used to extract features from the time-frequency graph and the RD graph to obtain a time-frequency feature graph and an RD feature graph, including the following steps:

[0027] The time-frequency graph and RD graph are respectively processed by the corresponding convolutional neural network module, attention mechanism module and multi-layer channel attention feature extraction module in the radar active interference identification network model, thereby obtaining the time-frequency feature graph and RD feature graph.

[0028] In one embodiment of the present invention, any core point in the core point set is randomly selected, and an initial maximum radius is obtained based on the distance between the core point and the remaining data points and a preset minimum number of points, including:

[0029] Randomly select any core point in the core point set and obtain the distance between the core point and the remaining data points;

[0030] According to the distance between the core point and the rest of the data points, the mean is obtained. initialize and standard deviation std initialize ;

[0031] Use the first formula to calculate the mean initialize and standard deviation std initialize Processing is performed to obtain the initial maximum radius; the first formula is as follows:

[0032] epsilon1=mean initialize +2*std initialize ;

[0033] Among them, epsilon1 is less than 0.1 times the first target distance, where the first target distance is the maximum distance between the core point and the remaining data points, and the data points within the initial maximum radius corresponding to the core point are all unvisited points.

[0034] In one embodiment of the present invention, updating the first core point in the core point set according to the initial maximum radius includes:

[0035] The ε-domain corresponding to the first core point in the core point set is determined according to the initial maximum radius, thereby completing the update of the first core point in the core point set.

[0036] In one embodiment of the present invention, based on the current core point and its corresponding directly density-reachable points, obtaining the maximum radius within the cluster to which the current core point belongs includes:

[0037] Accumulate the distance between the current core point and its corresponding directly reachable point to form a vector distants;

[0038] According to the vector distances, the mean of the vector distances is obtained. dis , standard deviation std dis and moment skewness ske dis ;

[0039] The second formula is used to calculate the mean dis , standard deviation std dis and moment skewness ske dis Processing is performed to obtain the maximum radius within the cluster; the second formula is as follows:

[0040]

[0041] In one embodiment of the present invention, obtaining adjustment parameters based on the current core point and its corresponding unvisited points includes:

[0042] Based on the distance between the current core point and the second target twice as large as its surrounding distance, the mean is obtained. inVisit and standard deviation std inVisit ; The second target distance is the distance between the preset minimum number of unvisited points;

[0043] The third formula is used to calculate the mean inVisit and standard deviation std inVisit Processing is performed to obtain the adjustment parameter; the third formula is as follows:

[0044] b=mean inVisit +2*std inVisit .

[0045] In one embodiment of the present invention, obtaining an updated maximum radius of the current core point based on the maximum radius within the cluster and the adjustment parameter includes:

[0046] The updated maximum radius is obtained by processing the maximum radius within the cluster and the adjustment parameter using the fourth formula; the fourth formula is as follows:

[0047]

[0048] Among them, a represents the maximum radius within the cluster, and b represents the adjustment parameter.

[0049] In one embodiment of the present invention, obtaining a clustering result of each unknown interference in the interference signal based on the updated core point set includes:

[0050] Select any core point in the updated core point set, and form a cluster for all density-reachable points corresponding to the core point; form a cluster for all density-reachable points corresponding to the unvisited points in the updated core point set; until all data points in the updated core point set are visited, based on all clusters, obtain the clustering results of each unknown interference in the interference signal.

[0051] Beneficial effects of the present invention:

[0052] In the solution provided by the present invention, the traditional DBSCAN algorithm is improved. When determining the maximum radius, the maximum radius corresponding to the core point is updated based on the preset minimum number of points and the distance between the core point and its corresponding data point to obtain an updated maximum radius, thereby achieving adaptive changes in the maximum radius corresponding to each core point, and then using the updated maximum radius to update each core point; thereby reducing clustering time, improving clustering efficiency, and enhancing the clustering effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of the steps of a radar unknown interference clustering method based on an adaptive DBSCAN algorithm provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of the structure of an interference signal provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of core points and noise points provided by an embodiment of the present invention;

[0056] Figure 4a-4f A schematic diagram of clustering results of a radar unknown interference clustering method based on an adaptive DBSCAN algorithm provided by an embodiment of the present invention;

[0057] Figure 5a-5d This is a comparison chart of the results of the radar unknown interference clustering method based on the adaptive DBSCAN algorithm provided by an embodiment of the present invention and the traditional DBSCAN algorithm. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] Radar interference open set recognition enables the radar to determine whether the received interference signal is a known interference signal. However, for the radar, the unknown interference data is chaotic, and the radar cannot determine which of the unknown interference signals are meaningful. Therefore, it is necessary to cluster the obtained interference signals and classify interference signals with similar characteristics into one category to facilitate the subsequent acquisition and recognition of new interference in the environment.

[0060] In order to achieve the purpose of clustering various types of interference in interference signals, an embodiment of the present invention provides a radar unknown interference clustering method based on an adaptive DBSCAN algorithm.

[0061] The embodiment of the present invention provides a radar unknown interference clustering method based on the adaptive DBSCAN algorithm, which defines ε-area, core point, direct density reachable, density reachable, density connected, noise point, boundary point and unvisited point respectively; the radar unknown interference clustering method, such as Figure 1 As shown, the following steps may be included:

[0062] S1, preprocessing the obtained interference signal to obtain a time-frequency diagram and an RD diagram of the interference signal;

[0063] S2, using the radar active interference recognition network model based on the multi-layer channel attention mechanism, extracts features from the time-frequency graph and RD graph to obtain the time-frequency feature graph and RD feature graph;

[0064] S3, perform dimensionality reduction processing on the time-frequency feature map and the RD feature map to obtain feature parameters of several dimensions as data points;

[0065] S4, processing all data points based on a preset maximum radius and a preset minimum number of points to obtain a corresponding core point set;

[0066] S5, randomly selecting any core point in the core point set, and obtaining an initial maximum radius based on the distance between the core point and the remaining data points and the preset minimum number of points;

[0067] S6, updating the first core point in the core point set according to the initial maximum radius; for each core point in the core point set except the first core point, taking it as the current core point in turn, and obtaining the maximum radius of the cluster to which the current core point belongs based on the current core point and its corresponding directly density-reachable points; obtaining an adjustment parameter based on the current core point and its corresponding unvisited points; obtaining an updated maximum radius of the current core point based on the maximum radius within the cluster and the adjustment parameter; and updating the current core point according to the updated maximum radius, thereby obtaining an updated core point set;

[0068] S7, based on the updated core point set, obtain the clustering results of each unknown interference in the interference signal.

[0069] The radar unknown interference clustering method provided by the embodiment of the present invention determines the maximum radius based on a preset minimum number of points and the distance between each core point and its corresponding data point, thereby achieving adaptive change of the maximum radius corresponding to each core point.

[0070] For ease of understanding, each step of the radar unknown interference clustering method provided by an embodiment of the present invention is described in detail below.

[0071] Define ε-area, core points, direct density reachable, density reachable, density connected, noise points, boundary points and unvisited points respectively, which can include:

[0072] Definition of ε-domain: For a data point p, a circle is set with p as the center and a preset maximum radius as the radius. The set of data points contained in the circle is called the ε-domain;

[0073] Definition of core point: In the ε-domain of a data point, if the number of elements contained is greater than or equal to the preset minimum number of points, then the data point is called a core point;

[0074] Definition of direct density reachability: If the data point x2 is contained in the ε-domain of the data point x1, and the data point x1 is a core point, then x2 is said to be directly density reachable from x1;

[0075] Definition density is reachable: if there are data points x1, x2, ..., x N-1 , x N , and x i+1 By x i Direct density can be reached, then x N It is reachable by x1 density; where i∈[1,N], N≥2;

[0076] Definition of density connection: If for data point x1 and data point x2, there exists a core point x3 such that both x1 and x2 are density-reachable from the core point x3, then data point x1 and data point x2 are said to be density-connected;

[0077] Definition of noise points: Points that do not belong to any cluster are noise points;

[0078] Define boundary points: Data points that are within the maximum radius of a core point but are not core points are boundary points;

[0079] Definition of unvisited points: Data points that are not defined as core points, boundary points, or noise points are unvisited points.

[0080] For S1, this may include:

[0081] S11, obtaining interference signals;

[0082] For a schematic diagram of the structure of the interference signal in the embodiment of the present invention, please refer to Figure 2 .from Figure 2 As can be seen from the figure, the interference signal may include:

[0083] Noise amplitude modulation interference, noise frequency modulation interference, noise phase modulation interference, noise convolution interference, noise sweep frequency interference, range deception interference, speed deception interference, range wave gate interference, speed wave gate interference, dense false target interference, intermittent sampling and forwarding interference, slice forwarding interference, spectrum dispersion interference, and smart noise interference. Among the above-mentioned interference signals, the first five interference signals can be classified as suppression interference signals, and the last nine interference signals can be classified as deception interference signals. Among them, noise phase modulation interference, noise sweep frequency interference, speed deception interference, range wave gate interference, spectrum dispersion interference, and smart noise interference can be unknown interference, and noise amplitude modulation interference, noise frequency modulation interference, noise convolution interference, range deception interference, speed wave gate interference, dense false target interference, intermittent sampling and forwarding interference, and slice forwarding interference can be known interference.

[0084] S12, performing short-time Fourier transform on the interference signal within a pulse repetition period to obtain a time-frequency diagram of the interference signal;

[0085] S13, performing moving target detection on the interference signal under the condition of pulse accumulation to obtain an RD graph of the interference signal.

[0086] Specifically, the obtained interference signal is subjected to short-time Fourier transform within a pulse repetition period to obtain a time-frequency diagram of the interference signal within a pulse repetition period. At the same time, under the condition of pulse accumulation, the interference signal is subjected to moving target detection to obtain the RD (Range-Dopple) diagram of the interference signal.

[0087] For S2, this may include:

[0088] The time-frequency graph and RD graph are processed in sequence by the corresponding convolutional neural network module, attention mechanism module and multi-layer channel attention in the radar active interference identification network model to obtain the time-frequency feature graph and RD feature graph.

[0089] As can be understood, the radar active jamming identification network model includes a time-frequency network branch and a RD network branch. Both branches include a sequentially connected convolutional neural network module, an attention mechanism module, a multi-layer channel attention feature extraction module, and a fully connected module. The time-frequency network branch and the RD network branch in the radar active jamming identification network model are used to process the time-frequency graph and the RD graph, respectively, to extract features of the radar active jamming signal and obtain the corresponding time-frequency feature graph and RD feature graph.

[0090] For S3, this can include:

[0091] Before the fully connected layer of the convolutional neural network, the high-dimensional time-frequency feature map and RD feature map obtained by feature extraction need to be reduced in dimensionality to obtain the corresponding feature parameters. The proportion of the corresponding feature parameters after dimensionality reduction is from high to low. Among the feature parameters after dimensionality reduction, the feature parameters of several dimensions that account for 95% are taken as data points.

[0092] Specifically, the dimensionality reduction method may include PCA dimensionality reduction.

[0093] For S4, this may include:

[0094] All data points are processed based on the preset maximum radius and the preset minimum number of points to obtain the corresponding core points, and all core points are taken as the core point set.

[0095] It can be understood that the preset maximum radius can represent the radius for measuring the similarity of data during the clustering process. If the distance between two data is less than the preset maximum radius, the two data can be considered similar. A smaller preset maximum radius will produce a smaller cluster, and vice versa. A larger cluster will be produced. The preset minimum number of points can mean that if there are data points greater than or equal to the preset minimum number of points within a preset maximum radius, then these data points are considered to belong to the same cluster. The preset minimum number of points is equivalent to a threshold for measuring whether a cluster exists. If the threshold is large, the generated cluster will be more robust, but if the preset minimum number of points is too large, it will also result in the inability to generate a cluster. For a schematic diagram of the core points and noise points provided in an embodiment of the present invention, please refer to Figure 3 .from Figure 3 As can be seen from the figure, the points that do not belong to any cluster are divided into two groups with each core point as the center and the preset maximum radius as the radius. In this embodiment of the present invention, the maximum radius can be represented by epsilon, and the minimum number of points can be represented by MinPts.

[0096] For S5, this may include:

[0097] S51, randomly selecting any core point in the core point set, and obtaining the distance between the core point and the remaining data points;

[0098] S52, according to the distance between the core point and the rest of the data points, the mean is obtained. initialize and standard deviation std initialize ;

[0099] S53, using the first formula to calculate the mean initialize and standard deviation std initialize Processing is performed to obtain the initial maximum radius; the first formula is as follows:

[0100] epsilon1=mean initialize +2*std initialize ;

[0101] Among them, epsilon1 is less than 0.1 times the first target distance. The first target distance is the maximum distance between the core point and the remaining data points. The data points within the initial maximum radius corresponding to the core point are all unvisited points.

[0102] Taking into account that the maximum radius requires at least a minimum number of data points near the current core point to be enclosed in the corresponding circle, an initial maximum radius is obtained by randomly selecting any core point and processing it in step S5 to ensure that the circle corresponding to the initial maximum radius with the current core point as the center can include data points greater than or equal to the minimum number of points.

[0103] For S6, this may include:

[0104] S61, updating the first core point in the core point set according to the initial maximum radius, may include:

[0105] The ε-domain corresponding to the first core point in the core point set is determined according to the initial maximum radius, thereby completing the update of the first core point in the core point set.

[0106] S62, for each core point in the core point set except the first core point, based on the current core point and its corresponding directly density-reachable points, obtain the maximum radius within the cluster to which the current core point belongs, which may include:

[0107] Accumulate the distance between the current core point and its corresponding directly reachable point to form a vector distants;

[0108] Get the mean of vector distances according to vector distances dis , standard deviation std dis and moment skewness ske dis ;

[0109] Use the second formula to calculate the mean dis , standard deviation std dis and moment skewness ske dis Processing is performed to obtain the maximum radius within the cluster; the second formula is as follows:

[0110]

[0111] It can be understood that the moment skewness of vector distances is ske dis It reflects the distance distribution between the core point in the cluster to which the current core point belongs and its directly reachable points. If the moment skewness is negative, it means that its distance distribution is left-skewed, with a shorter left tail, and the radius only needs a small expansion to circle most of the samples that should belong to this cluster; if the moment skewness is positive, a larger radius needs to be expanded to circle most of the samples that should belong to this cluster. Therefore, you can choose to use the moment skewness ske of the vector distants dis to describe the maximum radius within the cluster. The coefficient of 3 in the second formula is to increase the slope of the Sigmiod function near 0, so as to facilitate sensitive control of the radius change. Through step S62, the maximum radius within the cluster corresponding to each core point in the core point set except the first core point can be obtained.

[0112] S63, obtaining adjustment parameters based on the current core point and its corresponding unvisited points, may include:

[0113] Based on the distance between the current core point and the second target twice as large as its surrounding distance, the mean is obtained. inVisit and standard deviation std inVisit ; The second target distance is the distance between the preset minimum number of unvisited points;

[0114] Use the third formula to calculate the mean inVisit and standard deviation std inVisit Processing is performed to obtain the adjustment parameters; the third formula is as follows:

[0115] b=mean inVisit +2*std inVisit .

[0116] Step S63 can obtain the adjustment parameters corresponding to each core point in the core point set except the first core point. The adjustment parameters obtained based on each core point and its corresponding unvisited point can be used to subsequently update the maximum radius corresponding to each core point.

[0117] S64, obtaining an updated maximum radius of the current core point based on the maximum radius within the cluster and the adjustment parameter, may include:

[0118] The fourth formula is used to process the maximum radius within the cluster and the adjustment parameters to obtain the updated maximum radius; the fourth formula is as follows:

[0119]

[0120] Among them, a represents the maximum radius within the cluster, and b represents the adjustment parameter.

[0121] The maximum radius within the cluster corresponding to each core point and the adjustment parameter are used to update the maximum radius corresponding to each core point, so as to facilitate the subsequent update of the core point set.

[0122] S65: Update the current core point according to the updated maximum radius, thereby obtaining an updated core point set.

[0123] It can be understood that the embodiment of the present invention realizes the adaptive change of the maximum radius parameter during the search process by accumulating the distance between the core points in the cluster to which the core point currently being searched belongs and its directly density-reachable points, and by the distance between the core point currently being searched and the surrounding unvisited points, so as to complete the update of the maximum radius.

[0124] For S7, this can include:

[0125] Select any core point in the updated core point set, and form a cluster for all density-reachable points corresponding to the core point; form a cluster for all density-reachable points corresponding to the unvisited points in the updated core point set; until all data points in the updated core point set are visited, the clustering results of each unknown interference in the interference signal are obtained based on all clusters.

[0126] Specifically, in step S7, starting from any core point, all points that are reachable from that core point through density are found to form a cluster. This cluster forms a connected region. This process is then repeated for unvisited points until all data points have been visited. Once all points have been visited, the connected regions constructed based on density reachability form the final clustering result, which serves as the clustering result for each unknown interference in the interference signal.

[0127] It can be understood that the specific algorithm flow of the radar unknown interference clustering method based on the adaptive DBSCAN algorithm proposed in the embodiment of the present invention is as follows:

[0128] Input: unclustered data X (data of unknown radar interference type), minimum number of points MinPts.

[0129] Output: Data type label vector Y.

[0130] Initialization: isVisit=zeros(1,N), isNoise=zeros(1,N), Y=zeros(1,N), C=0, N is the number of data, D={|x n -x m ||x n ∈X,x m ∈X,n≠m},m,n=1,2,3,...,N.

[0131] 1) Randomly select an unaccessed data x i , distants = [], if there is no unvisited data, the algorithm stops;

[0132] 2) Select 2*MinPts distances x i The nearest data, whose distance is D initialize ;

[0133] 3) Calculate D initialize The mean and variance are mean initialize and std initialize ;

[0134] 4) epsilon = mean initialize +2*std initialize ;

[0135] 5) epsilon < 0.1 * max (D), otherwise select a new data;

[0136] 6) Search for x i The data set within the maximum radius epsilon is represented as X new , these data need to be unaccessed, otherwise select a new data;

[0137] 7) If X new If the number of elements is greater than the minimum number of points MinPts, then mark x i The position corresponding to isVisit is 1, C=C+1, and the corresponding position in Y is marked x i With X new The elements in are of type C, X new The elements and x i The clustering can be represented as a vector D new , then distants=[distants,D new ]; otherwise, the data is considered to be noise, the corresponding positions of isNoise and isVisit are marked as 1, and 1 is returned);

[0138] 8) Traverse the other unvisited points of type C and denote them as x j , mark the position corresponding to isVisit as 1, and calculate the mean of distants dis , variance std dis and moment skewness ske dis ,but

[0139]

[0140] 9) Search for x j 2*MinPts data that have not been accessed nearby, which are to x j The distance is represented by D unVisit , calculate D unVisit mean unVisit and variance std unVisit , b = mean isVisit +2*std isVisit ;

[0141] Then the updated maximum radius is:

[0142]

[0143] 10) Search for x j The data set within the maximum radius epsilon is represented as X new ;

[0144] 11) If X newIf the number of elements is greater than the minimum number of points MinPts, then mark the corresponding position x in Y j With X new The elements in are of type C, X new The elements and x j The cluster can be represented as a vector D new , then dis=[dis,D new ]; until all data of type C are accessed, return to 1).

[0145] In order to facilitate understanding of the beneficial effects of the radar unknown interference clustering method based on the adaptive DBSCAN algorithm proposed in the embodiment of the present invention, a specific simulation experiment is performed on the method below to verify the method.

[0146] Setting up simulation data

[0147] The input parameters used can include noise amplitude modulation jamming, noise frequency modulation jamming, noise phase modulation jamming, noise convolution jamming, noise sweep jamming, range deception jamming, speed deception jamming, range waveguide jamming, speed waveguide jamming, dense false target jamming, intermittent sampling and forwarding jamming, slice forwarding jamming, spectrum dispersion jamming, and smart noise jamming. The final feature map obtained by extracting feature maps from the six unknown jamming types using a radar active jamming identification network model based on a multi-layer channel attention mechanism has a dimension of 512. The convolutional neural network used for feature extraction is designed for the eight known jamming types. PCA dimensionality reduction is performed on these features to obtain features that account for greater than 95%, resulting in a 15-dimensional feature map. The input data is the 15-dimensional features obtained after dimensionality reduction for the six jamming types.

[0148] Simulation content

[0149] The radar unknown interference clustering method based on the adaptive DBSCAN algorithm proposed in the embodiment of the present invention is used to adaptively cluster the above unknown interferences, and the corresponding clustering results are shown in FIG. Figure 4a-4f As shown. Figure 4a-4f It can be seen that Figure 4a and Figure 4b The clustering results are all within the range of JNR (Jamming to Noise Ratio) = 11 to 20 dB. It can be observed that the traditional DBSCAN algorithm clusters the data into 7 clusters, while the adaptive DBSCAN algorithm clusters the data into 6 clusters, but the overall observation is not much different. Figure 4c and Figure 4dThe clustering results are for the JNR range of 6 to 20 dB. As can be seen, as the interference-to-noise ratio decreases, the clustering results of both algorithms become more chaotic, with a significant increase in the number of noise points. The traditional DBSCAN algorithm clusters the data into 14 clusters, while the adaptive DBSCAN algorithm still manages to cluster the data into 6 clusters. Since the simulation data consists of 100 samples for each interference-to-noise ratio, there are 1500 samples of each interference type within the JNR range of 6 to 20 dB. Figure 4e and Figure 4f To cluster 200 samples randomly selected from 1500 samples, the corresponding clustering results are the same as Figure 4c and Figure 4d The corresponding results are similar, which verifies that the clustering effect has little correlation with the amount of data, but has a greater correlation with JNR.

[0150] In order to verify the effectiveness of the adaptive DBSCAN algorithm compared to the traditional DBSCAN algorithm, the following three groups of data are set, where the "*" data is the first group of data, in which the data points within the class are closely spaced, the "x" data is the second group of data, and the "+" data is the third group of data. Each group of data contains 200 sample points, and the data points in the third group are more spaced than those in the first group. The results of the radar unknown interference clustering method based on the adaptive DBSCAN algorithm provided by the embodiment of the present invention and the traditional DBSCAN algorithm are compared. See Figure 5a-5d , the specific data diagram is as follows Figure 5a As shown in the figure, when the minimum number of points Minpts is fixed to 5, if a smaller maximum radius epsilon is used, as shown in the figure, Figure 5b As shown in , the first and third groups of data can be clustered normally, but since the intra-class spacing of the second group of data is large, more clusters will be generated, and most of the data will be judged as noise. If a larger maximum radius epsilon is used, such as Figure 5c As shown, the second set of data can be clustered normally, but the first and second sets of data have been mixed and belong to the same cluster. This is because the interval between the first and third sets of data is smaller than the intra-class interval of the second set of data. When the adaptive DBSCAN algorithm is used, there is no need to consider the selection of the maximum radius corresponding to the clustering algorithm. It is only necessary to measure the minimum number of points Minpts. Figure 5d The overall clustering effect is obvious due to the traditional DBSCAN algorithm. In addition, when finding the optimal clustering, the adaptive DBSCAN algorithm only needs to optimize the minimum number of points Minpts as a discrete parameter, which can find the optimal clustering more conveniently and quickly.

[0151] In the solution provided in the embodiment of the present invention, the traditional DBSCAN algorithm is improved. When determining the maximum radius, the maximum radius corresponding to the core point is updated based on the preset minimum number of points and the distance between the core point and its corresponding data point to obtain an updated maximum radius, thereby achieving adaptive changes in the maximum radius corresponding to each core point, and then using the updated maximum radius to update each core point; thereby reducing clustering time, improving clustering efficiency, and enhancing the clustering effect.

[0152] It should be noted that, in the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A radar unknown interference clustering method based on the adaptive DBSCAN algorithm, which defines ε-area, core points, direct density reachability, density reachability, density connectivity, noise points, boundary points and unvisited points respectively; its characteristics are: include: Preprocessing the obtained interference signal to obtain a time-frequency diagram and an RD diagram of the interference signal; Using a radar active interference recognition network model based on a multi-layer channel attention mechanism, feature extraction is performed on the time-frequency graph and the RD graph respectively to obtain a time-frequency feature graph and an RD feature graph; Performing dimensionality reduction processing on the time-frequency feature map and the RD feature map to obtain feature parameters of several dimensions as data points; All data points are processed based on the preset maximum radius and the preset minimum number of points to obtain the corresponding core point set; Randomly select any core point in the core point set, and obtain an initial maximum radius based on the distance between the core point and the remaining data points and a preset minimum number of points; Updating the first core point in the core point set according to the initial maximum radius; For each core point in the core point set except the first core point, take it as the current core point in turn, and obtain the maximum radius of the cluster to which the current core point belongs based on the direct density reachable point corresponding to the current core point; Based on the current core point and its corresponding unvisited points, an adjustment parameter is obtained; based on the maximum radius within the cluster and the adjustment parameter, an updated maximum radius of the current core point is obtained; Updating the current core point according to the updated maximum radius, thereby obtaining an updated core point set; Based on the updated core point set, a clustering result of each unknown interference in the interference signal is obtained.

2. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The definitions of ε-area, core points, direct density reachable, density reachable, density connected, noise points, boundary points and unvisited points respectively include: The ε-domain is defined as follows: for a data point p, a circle is set with p as the center and a preset maximum radius as the radius. The set of data points contained in the circle is called the ε-domain; Definition of the core point: In the ε-domain of a data point, if the number of elements contained is greater than or equal to the preset minimum number of points, then the data point is called a core point; Definition of direct density reachability: If a data point x2 is contained in the ε-domain of a data point x1, and the data point x1 is a core point, then x2 is said to be directly density reachable from x1; The density is defined as follows: if there are data points x1, x2, ..., x N-1 , x N , and x i+1 By x i Direct density can be reached, then x N It is reachable by x1 density; where i∈[1,N], N≥2; Definition of density connection: If for data point x1 and data point x2, there exists a core point x3 such that both x1 and x2 are density-reachable from the core point x3, then data point x1 and data point x2 are said to be density-connected; Definition of the noise point: a point that does not belong to any cluster is a noise point; Defining the boundary points: data points that are within the maximum radius of a core point but are not core points are boundary points; Definition of the unvisited points: Data points that are not defined as core points, boundary points, or noise points are unvisited points.

3. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The step of preprocessing the obtained interference signal to obtain a time-frequency diagram and an RD diagram of the interference signal includes: Performing short-time Fourier transform on the interference signal obtained within a pulse repetition period to obtain a time-frequency diagram of the interference signal; Perform moving target detection on the interference signal under the condition of pulse accumulation to obtain an RD graph of the interference signal.

4. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The step of extracting features from the time-frequency graph and the RD graph using a radar active interference identification network model based on a multi-layer channel attention mechanism to obtain a time-frequency feature graph and an RD feature graph includes: The time-frequency graph and RD graph are respectively processed by the corresponding convolutional neural network module, attention mechanism module and multi-layer channel attention feature extraction module in the radar active interference identification network model, thereby obtaining the time-frequency feature graph and RD feature graph.

5. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The randomly selecting any core point in the core point set and obtaining an initial maximum radius based on the distance between the core point and the remaining data points and a preset minimum number of points includes: Randomly select any core point in the core point set and obtain the distance between the core point and the remaining data points; According to the distance between the core point and the rest of the data points, the mean is obtained. initialize and standard deviation std initialize ; Use the first formula to calculate the mean initialize and standard deviation std initialize Processing is performed to obtain the initial maximum radius; the first formula is as follows: epsilon1=mean initialize +2*std initialize ; Among them, epsilon1 is less than 0.1 times the first target distance, where the first target distance is the maximum distance between the core point and the remaining data points, and the data points within the initial maximum radius corresponding to the core point are all unvisited points.

6. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: Updating the first core point in the core point set according to the initial maximum radius includes: The ε-domain corresponding to the first core point in the core point set is determined according to the initial maximum radius, thereby completing the update of the first core point in the core point set.

7. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The method of obtaining the maximum radius of the cluster to which the current core point belongs based on the current core point and its corresponding directly density-reachable points includes: Accumulate the distance between the current core point and its corresponding directly reachable point to form a vector distants; According to the vector distances, the mean of the vector distances is obtained. dis , standard deviation std dis and moment skewness ske dis ; The second formula is used to calculate the mean dis , standard deviation std dis and moment skewness ske dis Processing is performed to obtain the maximum radius within the cluster; the second formula is as follows:

8. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1 is characterized in that: The adjusting parameters are obtained based on the current core point and the corresponding unvisited points, including: Based on the distance between the current core point and the second target twice as large as its surrounding distance, the mean is obtained. inVisit and standard deviation std inVisit ; The second target distance is the distance between the preset minimum number of unvisited points; The third formula is used to calculate the mean inVisit and standard deviation std inVisit Processing is performed to obtain the adjustment parameter; the third formula is as follows: b=mean inVisit +2*std inVisit 。 9. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1, characterized in that: The updated maximum radius of the current core point is obtained based on the maximum radius within the cluster and the adjustment parameter, including: The updated maximum radius is obtained by processing the maximum radius within the cluster and the adjustment parameter using the fourth formula; the fourth formula is as follows: Among them, a represents the maximum radius within the cluster, and b represents the adjustment parameter.

10. The radar unknown interference clustering method based on the adaptive DBSCAN algorithm according to claim 1, characterized in that: The step of obtaining a clustering result of each unknown interference in the interference signal based on the updated core point set includes: Select any core point in the updated core point set, and form a cluster for all density-reachable points corresponding to the core point; form a cluster for all density-reachable points corresponding to the unvisited points in the updated core point set; until all data points in the updated core point set are visited, based on all clusters, obtain the clustering results of each unknown interference in the interference signal.

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