Radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion

Abstract

The invention discloses a radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion, and the method comprises the steps: carrying out the data coding operation of dual-platform PDW data according to a data coding module, and guaranteeing the complete coding of pulses; according to a depth time difference feature extraction branch, performing feature extraction on the double-platform features, performing extracted feature correlation analysis, performing mean value calculation on an analysis result, performing dimension splicing to obtain a four-dimensional feature body, and according to the four-dimensional feature body, performing matching on sample points with approximate time difference, and determining a final depth TDOA feature; a branch is extracted according to the inter-pulse sequence features, a double-layer bidirectional LSTM network is adopted for main platform data to capture a long-range dependency relationship in a pulse sequence, and the inter-pulse features are extracted; and the depth TDOA features and the inter-pulse features are fused through a classification module, a linear layer is used for mapping to radar number tags, and finally tags corresponding to a coding sequence are mapped to original PDW tags to obtain a final sorting result.

Description

Technical Field

[0001] The invention belongs to the technical field of radar electronic reconnaissance, and in particular relates to a radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion. Background Art

[0002] Existing radar pulse sorting methods mainly include two categories: one is the sorting method based on a single reconnaissance platform, such as the cumulative difference histogram (CDIF) algorithm, the sequence difference histogram (SDIF) algorithm, the PRI (Pulse Repetition Interval) transformation algorithm and other PRI-based sorting algorithms; or the sorting methods of multi-parameter clustering such as K-means clustering, density-based clustering, Bayesian clustering, fuzzy clustering, incremental clustering, deep clustering; and the recent sorting methods based on deep learning such as recurrent neural networks (RNNs) and image segmentation networks. However, the radar pulse sequences received in the modern complex electromagnetic environment often have complex inter-pulse modulation types and non-ideal situations such as measurement errors, missing pulses, and false pulses. The methods based on a single reconnaissance platform have fewer features that can be used and cannot achieve good sorting results.

[0003] Another type of method is the sorting method based on multiple reconnaissance platforms. These methods match the PDW (Pulse Description Word) intercepted by different platforms through specific constraint rules to obtain the time difference of arrival (TDOA) of the successfully paired pulse pairs. For example, the time difference window (TDW) is combined with the PDW parameter distance for pulse matching, and then recursive histogram statistics, grid clustering, or evaluation of the similarity between the TDOA vectors of the pulse pairs are performed to achieve sorting. These methods usually perform well when there are at least three reconnaissance platforms and the TDOA vectors of different radars are different. However, when there are fewer available platforms, time difference ambiguity is prone to occur, resulting in missed batches in sorting. In addition, the clustering distance threshold has a significant impact on the deinterleaving performance. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion to solve the above technical problems.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion, comprising:

[0007] Build a radar pulse deinterleaving network based on dual platforms to complete the collaborative sorting of dual platform data;

[0008] Among them, the radar pulse deinterleaving network based on dual platforms includes a data encoding module, an inter-pulse sequence feature extraction branch, a deep time difference feature extraction branch and a classification module; the deep time difference feature extraction branch includes a single platform feature extraction module, a bidirectional group correlation module and a matching module;

[0009] The dual-platform data is collaboratively sorted based on the dual-platform radar pulse deinterleaving network, including:

[0010] Step 1: Perform data encoding operation on the dual-platform PDW data according to the data encoding module to ensure that the time intervals between input data sample points are equal;

[0011] Step 2: Extract branches based on the deep time difference feature, extract features from the dual platform features, perform feature correlation analysis, calculate the mean of the analysis results, and then perform dimension splicing to obtain a four-dimensional feature body. Match sample points with close time differences based on the four-dimensional feature body to determine the final deep TDOA feature.

[0012] Step 3: Extract branches based on the inter-pulse sequence features, use a two-layer bidirectional LSTM network to capture the long-range dependencies in the pulse sequence for the main platform data, and extract the inter-pulse features;

[0013] Step 4: The deep TDOA features and inter-pulse features are fused through the classification module and mapped to the radar number label using a linear layer. Finally, the label corresponding to the coding sequence is mapped to the original PDW label to obtain the final sorting result.

[0014] Furthermore, a radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion also includes: the dual platform includes a main platform and a sub-platform; in the process of completing the collaborative sorting of the dual platform data based on the dual-platform radar pulse deinterleaving network, the PDW data intercepted by the sub-platform is sent to the main platform for unified processing.

[0015] Further, step 1 includes:

[0016] Step 11, normalize the radio frequency value of the PDW data to be between 0.1 and 1;

[0017] Step 12: For each pulse x i , calculate the unit time points of its start and end, including [toa j / Δt] and [(toa j +pw j ) / Δt];

[0018] Step 13: between the start and end time units, assign normalized radio frequency values ​​and labels in sequence to complete the encoding of the pulse data.

[0019] Further, step 2 includes:

[0020] Step 21: extract features from the data of the two platforms according to the single-platform feature extraction module, and obtain features of platform A and platform B respectively; wherein platform A is the main platform and platform B is the secondary platform;

[0021] Step 22: Based on the bidirectional group correlation module, the features of platform A and the features of platform B are subjected to correlation analysis, and a four-dimensional feature body is obtained by merging the analysis results;

[0022] Step 23: Based on the matching module, sample points with close time differences are matched according to the four-dimensional feature body to obtain the final deep TDOA feature.

[0023] Further, step 21 comprises:

[0024] Step 211: Use a pair of ResNet-like networks with shared parameters to map the data from the two platforms into a high-dimensional feature space:

[0025]

[0026] Among them, A represents the main platform in the dual platform, Conv1 represents the convolution operation of the first layer, including multiple groups of convolution layers with batch normalization and ReLU activation functions, represents the l-layer convolutional network of platform A, t A Represents the encoded input data;

[0027] Step 212: Connect the outputs of different layers of the convolutional network in step 212 to extract the features of platform A:

[0028]

[0029] Among them, f A represents the extracted features of platform A;

[0030] Step 213: According to the method of step 211 and step 212, extract the feature f of platform B B .

[0031] Further, step 22 comprises:

[0032] Step 221: Divide feature f along the channel dimension A and f B N g groups, so that each feature group has N c / Ng channels;

[0033] Step 222: Based on the principle that the TDOA between the dual-platform data can be positive or negative, the bidirectional group correlation feature of the g-th feature group is calculated:

[0034]

[0035] Among them, <·,·> represents the inner product, D max Indicates the maximum time difference level;

[0036] Merge all group-related features into a bidirectional intra-group related feature volume

[0037] Step 223: Take the average along the time difference level dimension and copy the mean back to the original dimension to get Finally and Splice along the feature group dimension to obtain a four-dimensional feature body

[0038] Further, step 23 includes:

[0039] The improved U-net architecture is used to analyze the four-dimensional feature volume Process it and upsample its output features to restore the original time resolution to obtain the final deep TDOA feature f tdoa .

[0040] The beneficial effects of the present invention are:

[0041] The present invention proposes a radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion, introduces deep neural network into multi-platform collaborative sorting task, and the proposed DPCD-Net can simultaneously extract deep TDOA features and inter-pulse sequence features. The sequence feature extraction branch captures the change relationship between pulses from the data of the main platform. At the same time, the deep TDOA feature extraction branch adopts a bidirectional group correlation (Bi-GWC) module to perform bidirectional displacement correlation calculation along the time dimension to mine the relationship between the data of the two platforms. Compared with the existing single-platform sorting method, the present invention combines multi-platform information and can adapt to more complex radar signal sorting scenarios. Compared with the traditional sorting method based on TDOA vector, the present invention can extract more effective deep TDOA features and has better sorting performance. At the same time, since the present invention combines inter-pulse features and deep TDOA features at the same time, the present invention can also show better effects for radiation sources with time difference ambiguity.

[0042] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or they may be taught from the practice of the present invention. The purposes and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a method flow chart of a radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion in an embodiment of the present invention;

[0046] Figure 2 It is a schematic structural diagram of a radar pulse deinterlacing network based on a dual platform in a radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion in an embodiment of the present invention;

[0047] Figure 3 The module diagram of the bidirectional group correlation module in a radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0049] like Figure 1 As shown, the present invention proposes a radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion, comprising:

[0050] Build a radar pulse deinterleaving network based on dual platforms to complete the collaborative sorting of dual platform data;

[0051] Among them, the radar pulse deinterleaving network based on dual platforms includes a data encoding module, an inter-pulse sequence feature extraction branch, a deep time difference feature extraction branch and a classification module; the deep time difference feature extraction branch includes a single platform feature extraction module, a bidirectional group correlation module and a matching module.

[0052] The dual-platform data is collaboratively sorted based on the dual-platform radar pulse deinterleaving network, including:

[0053] Step 1: Perform data encoding operation on the dual-platform PDW data according to the data encoding module to ensure that the time intervals between input data sample points are equal;

[0054] Step 2: Extract branches based on the deep time difference feature, extract features from the dual platform features, perform feature correlation analysis, calculate the mean of the analysis results, and then perform dimension splicing to obtain a four-dimensional feature body. Match sample points with close time differences based on the four-dimensional feature body to determine the final deep TDOA feature.

[0055] Step 3: Extract branches based on the inter-pulse sequence features, use a two-layer bidirectional LSTM network to capture the long-range dependencies in the pulse sequence for the main platform data, and extract the inter-pulse features;

[0056] Step 4: The deep TDOA features and inter-pulse features are fused through the classification module and mapped to the radar number label using a linear layer. Finally, the label corresponding to the coding sequence is mapped to the original PDW label to obtain the final sorting result.

[0057] In actual operation, step 2 and step 3 are performed in parallel.

[0058] Among them, the dual platform includes a main platform and a sub-platform; in the process of collaborative sorting of dual platform data based on the radar pulse de-interleaving network of the dual platforms, the PDW data intercepted by the sub-platform is sent to the main platform for unified processing.

[0059] Step 1 includes:

[0060] Step 11, normalize the radio frequency value of the PDW data to be between 0.1 and 1;

[0061] Step 12: For each pulse x i , calculate the unit time points of its start and end, including [toa j / Δt] and [(toa j +pw j ) / Δt];

[0062] Step 13: between the start and end time units, assign normalized radio frequency values ​​and labels in sequence to complete the encoding of the pulse data.

[0063] Step 2 includes:

[0064] Step 21: extract features from the data of the two platforms according to the single-platform feature extraction module, and obtain features of platform A and platform B respectively; wherein platform A is the main platform and platform B is the secondary platform;

[0065] Step 22: Based on the bidirectional group correlation module, the features of platform A and the features of platform B are subjected to correlation analysis, and a four-dimensional feature body is obtained by merging the analysis results;

[0066] Step 23: Based on the matching module, sample points with close time differences are matched according to the four-dimensional feature body to obtain the final deep TDOA feature.

[0067] Step 21 includes:

[0068] Step 211: Use a pair of ResNet-like networks with shared parameters to map the data from the two platforms into a high-dimensional feature space:

[0069]

[0070] Among them, A represents the main platform in the dual platform, Conv1 represents the convolution operation of the first layer, including multiple groups of convolution layers with batch normalization and ReLU activation functions, represents the l-layer convolutional network of platform A, t A Represents the encoded input data;

[0071] Step 212: Connect the outputs of different layers of the convolutional network in step 212 to extract the features of platform A:

[0072]

[0073] Among them, f A represents the extracted features of platform A;

[0074] Step 213: According to the method of step 211 and step 212, extract the feature f of platform B B .

[0075] Step 22 includes:

[0076] Step 221: Divide feature f along the channel dimension A and f B N g groups, so that each feature group has N c / N g channels;

[0077] Step 222: Based on the principle that the TDOA between the dual-platform data can be positive or negative, the bidirectional group correlation feature of the g-th feature group is calculated:

[0078]

[0079] Among them, <·,·> represents the inner product, D max Indicates the maximum time difference level;

[0080] Merge all group-related features into a bidirectional intra-group related feature volume

[0081] Step 223: Take the average along the time difference level dimension and copy the mean back to the original dimension to get Finally and Splice along the feature group dimension to obtain a four-dimensional feature body

[0082] Step 23 includes:

[0083] The improved U-net architecture is used to analyze the four-dimensional feature volume Process it and upsample its output features to restore the original time resolution to obtain the final deep TDOA feature f tdoa .

[0084] The working principle and beneficial effects of the above technical solution are as follows: The purpose of the present invention is to provide a radar pulse cooperative sorting method based on deep correlation and inter-pulse feature fusion, which sends the PDW data intercepted by the secondary platform to the main platform for unified processing, and explores stable TDOA features and complex inter-pulse modulation features; based on this, the present invention designs a dual-platform cooperative deinterleaving network (Dual-Platform Cooperative Deinterleaving Network, DPCD-Net), and the overall framework of the network is detailed in Figure 2 , which mainly includes two feature extraction branches: the deep TDOA feature extraction branch based on dual-platform data and the inter-pulse sequence feature extraction branch based on the main platform data, which can simultaneously extract and fuse the deep features of the two branches.

[0085] First, in order to mine the correlation between the data of the two platforms, the data needs to be encoded in a uniform time. The specific steps adopted by the present invention are as follows:

[0086] 1. Normalize the RF value of the PDW data to be between 0.1 and 1.

[0087] 2. For each pulse x i , calculate the unit time points of its start and end [toa j / Δt] and [(toa j +pw j ) / Δt];

[0088] 3. Between the start and end time units, normalized RF values ​​and labels are assigned in sequence to complete the encoding of the pulse data.

[0089] The specific encoding process in the above steps is relatively mature in the prior art, so it will not be described here. For the specific encoding process, it is preferred to refer to the relevant encoding process in the prior art "Z. Kang, Y. Zhong, Y. Wu, and Y. Cai, "Signal deinterleaving based on u-net networks," in 2023 8th International Conference on Computer and Communication Systems (ICCCS), 2023, pp. 62–67."

[0090] The specific steps of deep TDOA feature extraction branch are as follows:

[0091] 1. Single-platform feature extraction module. The present invention uses a pair of ResNet-like networks with shared parameters to map the data from two platforms into a high-dimensional feature space (taking platform A as an example):

[0092]

[0093] Conv1 represents the convolution operation of layer 1, including multiple groups of convolution layers with batch normalization and ReLU activation functions. A Represents the encoded input data. At the same time, in order to better extract the relevant features between the data of the two platforms, it is necessary to capture as much useful information as possible from the data of each platform respectively. Therefore, we connect the outputs of different layers of the network to make full use of information at different levels:

[0094]

[0095] Among them, f A is the extracted feature of platform A.

[0096] 2. Bidirectional Group-Wise Correlation (Bi-GWC). For details of the module, see Figure 3 The process of estimating TDOA is to perform correlation analysis on the data of the two platforms. The present invention uses a deep learning model to construct a relevant feature body to extract deep TDOA features, and performs bidirectional shift correlation calculation along the time dimension on the features obtained by the single platform feature extraction module to explore the relationship between the data of the two platforms at different time differences.

[0097] Specifically, the bidirectional group correlation module first divides the feature f along the channel dimension A and f B Ng groups, so each feature group has N c / N g channels. Since the TDOA between the two platform data can be positive or negative, the bidirectional group correlation feature of the g-th feature group is calculated as follows:

[0098]

[0099] Where <·,·> represents the inner product. max Indicates the maximum time difference level. Merge the related features of all groups into a bidirectional intra-group related feature body

[0100] Next, in order to capture the relationship between different time difference levels, Take the average along the time difference level dimension and copy the mean back to the original dimension to get Finally and Splice along the feature group dimension to obtain a combined feature body

[0101] 3. Feature matching module. After constructing the four-dimensional feature body After that, it is necessary to further match the sample points with close time differences and refine the TDOA features.

[0102] Although 3D convolutional networks can directly process 4D features, the present invention chooses 2D convolutional networks to process layer by layer to improve real-time performance. Specifically, an improved U-net architecture is adopted, in which cross-layer connections help preserve fine-grained temporal details, which are essential for accurate TDOA estimation. The features output by the network are then upsampled to restore the original temporal resolution, and the final deep TDOA features f are obtained. tdoa .

[0103] The specific steps of the inter-pulse sequence feature extraction branch are as follows:

[0104] In addition to the TDOA characteristics between platforms, it is also essential to capture the inter-pulse sequence characteristics of the radar.

[0105] In order to effectively model these complex changing relationships, the present invention uses a two-layer bidirectional LSTM network to capture the long-range dependencies in the pulse sequence in a parallel feature extraction branch for the data of the main platform A, and extracts the inter-pulse feature f s .

[0106] The specific steps of feature fusion and classification are as follows:

[0107] After obtaining the deep TDOA features and inter-pulse sequence features, they are fused in the classification module and mapped to the radar number labels using a linear layer.

[0108] Finally, the labels corresponding to the coding sequences are mapped to the original PDW labels to obtain the final sorting results.

[0109] The present invention introduces deep neural networks into multi-platform collaborative sorting tasks. The proposed DPCD-Net can simultaneously extract deep TDOA features and inter-pulse sequence features. The sequence feature extraction branch captures the changing relationship between pulses from the data of the main platform. At the same time, the deep TDOA feature extraction branch uses a bidirectional group correlation (Bi-GWC) module to perform bidirectional displacement correlation calculations along the time dimension to mine the relationship between the data of the two platforms.

[0110] Compared with the existing single-platform sorting method, the present invention combines multi-platform information and can adapt to more complex radar signal sorting scenarios. Compared with the traditional TDOA vector-based sorting method, the present invention can extract more effective deep TDOA features and has better sorting performance. At the same time, since the present invention combines both inter-pulse features and deep TDOA features, the present invention can also show better results for radiation sources with time difference ambiguity.

[0111] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A radar pulse collaborative sorting method based on deep correlation and inter-pulse feature fusion, characterized in that: include: Build a dual-platform radar pulse deinterleaving network to complete the collaborative sorting of radar signals; Among them, the radar pulse deinterleaving network based on dual platforms includes a data encoding module, an inter-pulse sequence feature extraction branch, a deep time difference feature extraction branch and a classification module; the deep time difference feature extraction branch includes a single platform feature extraction module, a bidirectional group correlation module and a matching module; The radar signal is collaboratively sorted based on a dual-platform radar pulse deinterleaving network, including: Step 1: Perform data encoding operation on the dual-platform PDW data according to the data encoding module to ensure that the time intervals between input data sample points are equal; Step 2: Extract branches based on the deep time difference feature, extract features from the dual platform features, perform feature correlation analysis, calculate the mean of the analysis results, and then perform dimension splicing to obtain a four-dimensional feature body. Match sample points with close time differences based on the four-dimensional feature body to determine the final deep TDOA feature. Step 3: Extract branches based on the inter-pulse sequence features, use a two-layer bidirectional LSTM network to capture the long-range dependencies in the pulse sequence for the main platform data, and extract the inter-pulse features; Step 4: The deep TDOA features and inter-pulse features are fused through the classification module and mapped to the radar number label using a linear layer. Finally, the label corresponding to the coding sequence is mapped to the original PDW label to obtain the final sorting result.

2. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 1 is characterized in that: Also includes: The dual platform includes a main platform and a secondary platform; In the process of collaborative sorting of dual-platform data based on the dual-platform radar pulse deinterleaving network, the PDW data intercepted by the secondary platform is sent to the primary platform for unified processing.

3. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 1 is characterized in that: Step 1 includes: Step 11, normalize the radio frequency value of the PDW data to be between 0.1 and 1; Step 12: For each pulse x i , calculate the unit time points of its start and end, including [toa j / Δt] and [(toa j +pw j ) / Δt]; Step 13: between the start and end time units, assign normalized radio frequency values ​​and labels in sequence to complete the encoding of the pulse data.

4. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 1 is characterized in that: Step 2 includes: Step 21: extract features from the data of the two platforms according to the single-platform feature extraction module, and obtain features of platform A and platform B respectively; wherein platform A is the main platform and platform B is the secondary platform; Step 22: Based on the bidirectional group correlation module, the features of platform A and the features of platform B are subjected to correlation analysis, and a four-dimensional feature body is obtained by merging the analysis results; Step 23: Based on the matching module, sample points with close time differences are matched according to the four-dimensional feature body to obtain the final deep TDOA feature.

5. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 4 is characterized in that: Step 21 includes: Step 211: Use a pair of ResNet-like networks with shared parameters to map the data from the two platforms into a high-dimensional feature space: Among them, A represents the main platform in the dual platform, Conv1 represents the convolution operation of the first layer, including multiple groups of convolution layers with batch normalization and ReLU activation functions, represents the l-layer convolutional network of platform A, t A Represents the encoded input data; Step 212: Connect the outputs of different layers of the convolutional network in step 212 to extract the features of platform A: Among them, f A represents the extracted features of platform A; Step 213: According to the method of step 211 and step 212, extract the feature f of platform B B .

6. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 4 is characterized in that: Step 22 includes: Step 221: Divide feature f along the channel dimension A and f B N g groups, so that each feature group has N c / N g channels; Step 222: Based on the principle that the TDOA between the dual-platform data can be positive or negative, the bidirectional group correlation feature of the g-th feature group is calculated: Among them, <·,·> represents the inner product, D max Indicates the maximum time difference level; Merge all group-related features into a bidirectional intra-group related feature volume Step 223: Take the average along the time difference level dimension and copy the mean back to the original dimension to get Finally and Splice along the feature group dimension to obtain a four-dimensional feature body 7. The radar pulse collaborative sorting method based on depth correlation and inter-pulse feature fusion according to claim 4 is characterized in that: Step 23 includes: The improved U-net architecture is used to Process it and upsample its output features to restore the original time resolution to obtain the final deep TDOA feature f tdoa .

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