Target recognition method, device and equipment based on multi-platform radar RCS data

Abstract

The present application relates to a target recognition method, device, and apparatus based on multi-platform radar RCS data. The method involves encoding and processing the RCS data obtained by detecting the same target on multiple different platforms to obtain a coding sequence. A data fusion model is then used to fuse the local and global features in each RCS data through the coding sequence to obtain a first collaborative feature. The angle data corresponding to different platforms are respectively encoded with the first collaborative feature based on the angle information to obtain corresponding angle features. After interacting with each angle feature, the data fusion model is used to perform feature fusion. The fused data are interacted again. Finally, feature fusion is performed on the interacted data, and a classification head is used to predict and classify the target based on the fused features to achieve target recognition. This method can be used to fuse and represent targets in RCS data from multiple angles on multiple different platforms, effectively improving the recognition performance of downstream recognition algorithms.

Description

Technical Field

[0001] The present application relates to the field of radar signal processing and computer intelligence technology, and in particular to a method, device and equipment for target recognition based on multi-platform radar RCS data. Background Art

[0002] Radar Automatic Target Recognition (RATR) plays a vital role in national security and aviation safety. Radar Cross Section (RCS) data is readily available in RATR missions and is therefore widely used across various platforms to monitor sensitive enemy targets. However, the amount of target RCS data acquired by a single station is limited, failing to provide strong separable features for perception and recognition algorithms. Therefore, achieving collaborative RCS characterization across multiple platforms to obtain comprehensive radar characteristic information about a target is an urgent challenge in the field of multi-platform radar target recognition. Summary of the Invention

[0003] Based on this, it is necessary to provide a target recognition method, device and equipment based on multi-platform radar RCS data, which can improve target recognition capability through multi-platform radar RCS data collaboration to address the above technical problems.

[0004] A target recognition method based on multi-platform radar RCS data, the method comprising:

[0005] Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms;

[0006] Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence;

[0007] Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0008] Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0009] Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features;

[0010] Inputting each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0011] Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features;

[0012] A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0013] In one embodiment, when pre-processing each RCS echo data, normalization processing and center-of-gravity alignment processing are sequentially performed on each RCS echo data.

[0014] In one embodiment, encoding according to each weighted subsequence to obtain a coded sequence includes:

[0015] Encode each weighted subsequence into a D-dimensional vector to obtain the corresponding weighted coded subsequence;

[0016] Processing each weighted coding subsequence, adding a category hint vector and a segmentation vector to the front end and the back end of each weighted coding subsequence, respectively, to obtain a processed weighted coding subsequence;

[0017] The coding sequence is obtained by constructing according to all processed weighted coding subsequences, the sequence segmentation coding matrix and the position coding matrix.

[0018] In one embodiment, the data fusion model based on the autonomous force mechanism includes multiple feature extraction layers connected in sequence;

[0019] Each feature extraction layer includes the first normalization layer, the multi-head attention layer, the second normalization layer and the feedforward layer connected in sequence.

[0020] In one embodiment, the angle information-based coding model includes multiple angle guidance layers, where the input of each angle guidance layer is the angle data of the corresponding layer and the first collaborative feature. In each angle guidance layer:

[0021] Linearly encode the input angle data to obtain angle-encoded data;

[0022] Perform feature mapping on the first collaborative feature of the input to obtain a mapping feature;

[0023] The angle encoding data is multiplied by the mapping feature, and the multiplication result is summed with the first collaborative feature using a residual connection to obtain the angle feature output by the angle guidance layer.

[0024] In one embodiment, in the first front-stage feature interaction model and the second front-stage feature interaction model, each input data is weightedly added to all other input data to obtain the corresponding interaction feature.

[0025] In one embodiment, the target recognition method further includes:

[0026] Constructing a multi-angle target recognition network based on the signal coding model based on Markov Monte Carlo sampling, the data fusion model based on the self-attention mechanism, the coding model based on angle information, the first front-stage feature interaction model, the second front-stage feature interaction model, the deep attention fusion model, and the classification head;

[0027] The pre-processed RCS echo data are input into the multi-angle target recognition network to obtain the target recognition result.

[0028] In one embodiment, when training the multi-angle target recognition network, the multi-angle target recognition network is trained using a loss function based on a cross entropy formula to construct an algorithm;

[0029] The signal coding model based on Markov Monte Carlo sampling includes adjustable weights, and the weights are used to weight each of the subsequences.

[0030] The present application also provides a target recognition device based on multi-platform radar RCS data, the device comprising:

[0031] A multi-platform data acquisition module is used to acquire RCS echo data at multiple different angles, each of which is obtained by detecting the same target from different radar platforms;

[0032] a coding sequence obtaining module, configured to preprocess each of the RCS echo data, divide the preprocessed RCS echo data into multiple weighted subsequences of the same length using a signal coding model based on Markov Monte Carlo sampling, and encode each weighted subsequence to obtain a coding sequence;

[0033] A first collaborative feature obtaining module is used to input the coding sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0034] an angle feature acquisition module, configured to input the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information, to obtain an angle feature corresponding to each angle data;

[0035] A first interaction feature obtaining module is configured to input each of the angle features into a first previous feature interaction model for feature interaction, thereby obtaining a first interaction feature corresponding to each of the angle features;

[0036] A second collaborative feature obtaining module is configured to input each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a plurality of corresponding second collaborative features;

[0037] A second interaction feature obtaining module, configured to input each of the second collaborative features into a second previous-level feature interaction model for feature interaction, and obtain a second interaction feature corresponding to each of the second collaborative features;

[0038] The target recognition module is used to use a deep attention fusion model to fuse the second interaction features to obtain fused features, and use a classification head to identify the target based on the fused features.

[0039] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0040] Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms;

[0041] Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence;

[0042] Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0043] Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0044] Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features;

[0045] Inputting each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0046] Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features;

[0047] A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0048] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0049] Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms;

[0050] Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence;

[0051] Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0052] Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0053] Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features;

[0054] Inputting each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0055] Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features;

[0056] A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0057] The above-mentioned target recognition method, device, and equipment based on multi-platform radar RCS data are obtained by encoding the RCS echo data obtained by detecting the same target from multiple different platforms based on Markov Monte Carlo sampling to obtain a coding sequence. The data fusion model based on the self-attention mechanism is then used to fuse the local and global features in each RCS data through the coding sequence to obtain a first collaborative feature. The angle data of each platform is then encoded with the first collaborative feature based on angle information to obtain corresponding angle features. After the angle features are interacted, the data fusion model is used to perform feature fusion. The fused data are then interacted again. Finally, the interacted data is subjected to feature fusion through a deep attention fusion model. The classification head is used to predict and classify the target based on the fused features to achieve target recognition. This method can be used to fuse the targets in the RCS data of multiple angles from multiple different platforms, effectively improving the recognition performance of the downstream recognition algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 1 is a flow chart of a target recognition method based on multi-platform radar RCS data in one embodiment;

[0059] Figure 2 1 is a flowchart of a target recognition method based on multi-platform radar RCS data in one embodiment;

[0060] Figure 3 1 is a flow chart of a signal encoding step based on Markov Monte Carlo sampling in one embodiment;

[0061] Figure 4 is a schematic structural diagram of an angle guiding layer in one embodiment;

[0062] Figure 5 A schematic diagram of the interaction of front-end features in one embodiment;

[0063] Figure 6 The following is a schematic diagram of the separability experimental verification results of this method in the experimental simulation. Figure 6 (a) is a schematic diagram of the target echo data of the first radar station. Figure 6 (b) is a schematic diagram of the target echo data of the second radar station. Figure 6 (c) is a schematic diagram of the fusion of two radar stations;

[0064] Figure 7 is a structural block diagram of a target recognition device based on multi-platform radar RCS data in one embodiment;

[0065] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0067] In the existing technology, the target RCS data obtained by single-platform radar detection is limited in the amount of target information, which cannot provide strong analytical features for the perception and recognition algorithm, resulting in unsatisfactory automatic recognition performance. Figure 1-2 As shown, a target recognition method based on multi-platform radar RCS data is provided, comprising the following steps:

[0068] Step S100, acquiring RCS echo data at multiple different angles, where each RCS echo data is obtained by detecting the same target from different radar platforms;

[0069] Step S110: Preprocess each RCS echo data, divide the preprocessed RCS echo data into multiple weighted subsequences of the same length using a signal coding model based on Markov Monte Carlo sampling, and encode each weighted subsequence to obtain a coded sequence;

[0070] Step S120: input the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0071] Step S130: Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0072] Step S140: Input each angle feature into a first previous feature interaction model to perform feature interaction, and obtain a first interaction feature corresponding to each angle feature;

[0073] Step S150: inputting each first interaction feature into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0074] Step S160: Input each second collaborative feature into a second previous-level feature interaction model to perform feature interaction, thereby obtaining a second interaction feature corresponding to each second collaborative feature;

[0075] In step S170 , a deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0076] This method, based on deep learning, combines input multi-platform RCS information with angle information. A Markov Monte Carlo process-based data encoding method is used to achieve semantic fusion of radar RCS data, enabling collaborative representation of multi-platform radar RCS data. Experiments demonstrating dimensionality reduction visualization of fused features and the accuracy of downstream recognition tasks demonstrate that this technology can achieve collaborative representation of RCS data sequences across multiple platforms and improve the performance of downstream recognition algorithms.

[0077] In this method, a data fusion model based on the self-attention mechanism, a coding model based on angle information, and a signal coding model based on the Markov Monte Carlo process are proposed. Combined with the existing advanced natural language processing model based on the self-attention mechanism, the angle information of radar RCS data under multiple platforms and the scattering characteristic information of the RCS data itself are introduced to realize the fusion representation of RCS data under multiple platforms.

[0078] In step S110 , when pre-processing each RCS echo data, normalization processing and center-of-gravity alignment processing are sequentially performed on each RCS echo data.

[0079] Specifically, radar echo data exhibits azimuth sensitivity, amplitude sensitivity, and translation sensitivity, significantly impacting the robust target feature extraction model. To mitigate the radar echo data's sensitivity to amplitude, modulo-2 norm normalization is used to process the RCS echo data to obtain amplitude-normalized radar echo data. To reduce the radar echo data's translation sensitivity, a center alignment method is used to perform a cyclic shift on the amplitude-normalized RCS echo data, aligning its center to the center of the range window. This yields the preprocessed RCS echo data.

[0080] Furthermore, based on the preprocessed RCS echo data, different RCS echo data sequences are sampled based on the Markov Monte Carlo process according to their different local importance to obtain weighted subsequences, which are then input into a feature extraction model based on the self-attention mechanism for feature extraction.

[0081] In this embodiment, a signal coding model based on Markov Monte Carlo sampling is used to process each pre-processed RCS echo data. First, each processed RCS echo data is divided according to a preset length to obtain multiple weighted subsequences of equal length, which are expressed as:

[0082]

[0083] Here, formula (1) and formula (2) represent the RCS echo data obtained by different radar platforms. These two formulas do not mean that this method is only applicable to two platforms. They are only used as examples. This method can be applied to multiple radar platforms.

[0084] Then, for each subsequence:

[0085]

[0086] In formula (3) and formula (4), and Represent the learnable weights of the two RCS sequences. The weights will be iteratively updated according to the loss function during model training.

[0087] In this embodiment, encoding each weighted subsequence to obtain a coding sequence includes: encoding each weighted subsequence into a D-dimensional vector to obtain a corresponding weighted coding subsequence, processing each weighted coding subsequence, adding a category hint vector and a segmentation vector to the front end and the back end of each weighted coding subsequence, respectively, to obtain a processed weighted coding subsequence, and constructing based on all the processed weighted coding subsequences, a sequence segmentation coding matrix, and a position coding matrix to obtain a coding sequence.

[0088] Specifically, for each subsequence after division and Perform input encoding and encode it into a D-dimensional vector to obtain the corresponding encoded sequence x MCMC，emb with y MCMC，emb ,Right now:

[0089]

[0090]

[0091] In formula (5) and formula (6), represents the encoder, represents weighted subsequence coding, Indicates the encoded RCS sequence.

[0092] In this embodiment, the signal coding model based on Markov Monte Carlo sampling uses a linear fully connected layer to achieve encoding of the input sequence, and all weighted subsequences share one input encoder.

[0093] At the same time, in order to aggregate the information of each subsequence in the radar echo data without bias, this model adds a category hint vector before each weighted subsequence Implement the aggregation of RCS echo data features in the deep attention feature fusion module. Also add segment vectors after each weighted subsequence Used to distinguish different input sequences. And add data sequence segmentation coding E seq , providing data sequence segmentation information for the model. Secondly, in order to make full use of the spatial position relationship of the subsequences in the radar echo data, in this embodiment, the category prompt vector, segment vector, x MC，emb with y MC，emb Each weighted subsequence in the pos ,Right now:

[0094] z in =[z cls ;x MCMC，emb ;z sep ;y MCMC.emb ;z sep ]+ Eseq +E pos , (7)

[0095] In formula (7), Represent the sequence segmentation encoding matrix and position encoding matrix respectively, z in Represents the output of the signal coding model based on Markov Monte Carlo sampling, and also serves as the input of the feature extraction layer, that is, the coding sequence. The entire data processing process of the signal coding model based on Markov Monte Carlo sampling is as follows Figure 3 shown.

[0096] The self-attention mechanism has context-awareness and can fully utilize the local and global correlations between radar sequences. In step S120, the data fusion model based on the self-attention mechanism is used to extract relevant information containing target scattering characteristics from each RCS echo data based on the coding sequence.

[0097] In this embodiment, the data fusion model based on the autonomous mechanism includes multiple layers of feature extraction layers connected in sequence, and each feature extraction layer includes a first normalization layer, a multi-head attention layer, a second normalization layer and a feedforward layer connected in sequence.

[0098] Specifically, the data fusion model based on the autonomous mechanism is expressed as:

[0099] T n (Z in )=T(T(...T(Z in ))), (8)

[0100] In formula (8), n represents the number of feature extraction layers included in the data fusion model, z in represents the input encoding sequence, and T(·) represents a feature extraction layer based on the self-attention mechanism.

[0101] For each feature extraction layer, the input sequence after feature extraction can be expressed as:

[0102] T(z in )=f FFN +f MSA , (9)

[0103] In formula (9), f MSA represents the output of the multi-head attention mechanism layer in the feature extraction layer, f FFN Represents the output of the feed-forward layer in the feature extraction layer.

[0104] Among them, the multi-head attention mechanism layer f MSA It can be described as:

[0105] f MSA =Concat(head1, head2,..., head M )W O , (10)

[0106] In formula (10), M represents the number of attention heads, Concat represents the concatenation operation, Represents the recovery matrix, which is used to ensure the consistency of input and output dimensions.

[0107] The feedforward layer of the feature extraction layer includes a multi-layer perceptron, which can map the input features to a high-dimensional latent space and then map it back to the original space to extract and filter the input features. The specific process of the feedforward layer can be defined as:

[0108]

[0109] In formula (11), is the input of the feed-forward layer, and MLP(·) represents a multilayer perceptron with two fully connected layers.

[0110] Therefore, the encoded radar RCS data is finally obtained through the data fusion model based on the autonomous mechanism to obtain the collaborative representation under multi-platform data, that is, the first collaborative feature.

[0111] In step S130, in order to introduce angle information under different platforms, an encoding model based on angle information is constructed in this method, and the angle information is added to the output of the fusion model in the form of a feature vector. Then, the multi-stage feature fusion is realized through the previous feature interaction model in step S140, and finally the fused features are input into the downstream classification and recognition task.

[0112] In this embodiment, the coding model based on angle information includes multiple angle guidance layers, and the input of each angle guidance layer is the angle data of the corresponding layer and the first collaborative feature. In each angle guidance layer: the input angle data is linearly encoded to obtain angle coding data, the input first collaborative feature is feature mapped to obtain mapping features, the angle coding data is multiplied by the mapping features, and the multiplication result is summed with the first collaborative feature using a residual connection to obtain the angle feature output by the angle guidance layer, such as Figure 4 shown.

[0113] Specifically, the angle guidance layer first linearly encodes a through the angle encoding module. The encoding module consists of two fully connected layers, and the output encoding information γ(a) can be expressed as:

[0114] γ(a)=(aW1+b1)W2+b2, (12)

[0115] In formula (12), W1, W2 and b1, b2 represent the weights and biases of the two linear transformations respectively.

[0116] Specifically, the mapping function based on the convolution module converts the first collaborative feature T output by the data fusion model based on the self-attention mechanism into n (z in ) for feature mapping. The mapping function R includes two convolutional layers, a batch normalization layer and a GELU activation layer. The feature mapping process can be expressed as:

[0117] R(T n (z in ))=Conv(GELU(BN(Conv(T n (z in ))))), (13)

[0118] In formula (13), BN(·) represents batch normalization, Conv(·) represents a one-dimensional convolution operation, and GELU(·) represents a nonlinear activation layer.

[0119] Finally, the mapped features are multiplied with the angle-encoded data and combined with the input features T using a residual connection. n (z in )Sum.

[0120] Furthermore, the angle-guided layer outputs the feature f out It can be expressed as:

[0121] f out =T n (z in )+R(T n (z in )).γ(a). (14)

[0122] To achieve multi-stage feature fusion in the model, this method also designs a front-end feature interaction model, enabling multi-stage hierarchical fusion of multi-platform features during the feature extraction process. By fusing data features from each platform, the model can associate and learn with shallow features from other radars during shallow feature extraction.

[0123] In step S140, a multi-stage hierarchical fusion is achieved through a first front-stage feature interaction model, such as Figure 5 shown.

[0124] In this embodiment, in the first front-stage feature interaction model, each angle feature is weighted and added to all other angle features to obtain a corresponding interaction feature.

[0125] Specifically, the input data of the first front-end feature interaction model is the output of the angle guidance layer of each platform The superscripts represent the serial numbers of different platforms. For illustration purposes, three radar platforms are used as examples. The output of the front-end feature interaction module can be expressed as:

[0126]

[0127] In formula (15), Represents the three interactive features output by the previous feature interaction module, ω 11 ,ω 12 ,ω 13 ,ω 21 ,ω 22 ,ω 23 ,ω 31 ,ω 32 ,ω 33 is the weight of each platform feature interaction.

[0128] After the first pre-stage feature interaction model is run, first interaction features corresponding to the number of radar platforms are obtained. In step S150, the data fusion model based on the autonomous mechanism is used to fuse each of the first interaction features again, obtaining multiple second collaborative features corresponding to each of the first interaction features. The data processing process in the autonomous mechanism-based data fusion model is consistent with the post-processing process of inputting the encoded sequence into the self-attention mechanism-based data fusion model, and therefore will not be further described.

[0129] Next, each second collaborative feature is passed through the second front-stage feature interaction model. The process is consistent with the data processing process in the first front-stage feature interaction model, so it will not be repeated here.

[0130] In step S160, the second collaborative features are subjected to feature interaction by the second previous-stage feature interaction model to obtain corresponding second interaction features.

[0131] In step S170, finally in this step, the second interaction features corresponding to multiple angles obtained by processing the signal coding model based on Markov Monte Carlo sampling, the data fusion model based on the self-attention mechanism, the coding model based on angle information, the first front-stage feature interaction model, and the second front-stage feature interaction model are handed over to the target recognition algorithm downstream of the task for target recognition.

[0132] In this embodiment, a deep attention fusion model is first used to fuse the second interaction features to obtain fusion features, and then a classification head is used to classify and predict the target based on the fusion features to achieve target recognition.

[0133] In this embodiment, each of the aforementioned model structures is implemented using a learning network. Therefore, a multi-angle target recognition network can be constructed based on a signal encoding model based on Markov Monte Carlo sampling, a data fusion model based on a self-attention mechanism, an encoding model based on angular information, a first-stage feature interaction model, a second-stage feature interaction model, a deep attention fusion model, and a classification head. This allows for target recognition by simply inputting the preprocessed RCS echo data into the multi-angle target recognition network to obtain the target recognition result.

[0134] In this embodiment, when training the multi-angle target recognition network, a loss function based on a cross-entropy formula is used to train the multi-angle target recognition network. The signal coding model based on Markov Monte Carlo sampling includes adjustable weights, which are used to weight each of the subsequences.

[0135] In order to realize the training of multi-angle target recognition network, in this method, the specific downstream target recognition and classification tasks and the corresponding loss function are combined to realize the iterative update of model parameters.

[0136] Specifically, the algorithm inputs a multi-platform RCS signal sequence with random target category combinations during the training phase, and then extracts and fuses it through multiple layers of feature extraction layers based on the self-attention mechanism. Finally, the fused features combined with the signal coding are combined. Input the classification head to obtain the final prediction of the RCS target category.

[0137] Assume that the target category label of the RCS sequence is Then in each batch, the sequence labels are set as:

[0138]

[0139] In formula (16), m represents the total number of sequences during training, and C represents the total number of targets. Then, the loss function of the algorithm is constructed based on the cross entropy formula, namely:

[0140]

[0141] In formula (17), C(•) represents the classification head, which is used to output the predicted probability.

[0142] Furthermore, the training process of the multi-angle target recognition network can be divided into four steps. First, the input multi-platform RCS signal sequence is encoded based on the radar echo data of Markov Monte Carlo sampling to obtain the corresponding encoding vector Secondly, the obtained encoding vector is input into the cross-domain data fusion model to obtain the final feature vector Then the obtained fusion feature vector The final fusion feature output is obtained after angle information encoding; finally, the fusion feature obtained by the model is input into the classification head of the downstream task to calculate the loss function of target recognition and classification, and the learnable parameters in the overall algorithm are gradient updated based on this.

[0143] The testing process for the multi-angle object recognition network is divided into two steps. First, the trained data fusion model is connected to the recognition and classification head of the downstream task, and the parameters of the fusion model are adjusted (fixing the fusion model parameters). After the parameters are adjusted, the parameters of the fusion model and the recognition and classification head are fixed, and recognition testing is performed on the downstream dataset.

[0144] In the above-mentioned target recognition method based on multi-platform radar RCS data, the multi-platform radar RCS data collaborative characterization technology based on the Markov Monte Carlo process can realize the feature-level unified characterization of RCS signal sequences under multiple platforms, and the obtained feature representation has good separability. The obtained multi-platform RCS signal sequence fusion features can improve the performance of the classification and recognition algorithm.

[0145] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0146] In one embodiment, Figure 7 As shown, a target recognition device based on multi-platform radar RCS data is provided, comprising: a multi-platform data acquisition module 200, a coding sequence acquisition module 210, a first collaborative feature acquisition module 220, an angle feature acquisition module 230, a first interactive feature acquisition module 240, a second collaborative feature acquisition module 250, a second interactive feature acquisition module 260, and a target recognition module 270, wherein:

[0147] The multi-platform data acquisition module 200 is used to acquire RCS echo data at multiple different angles, where each RCS echo data is obtained by detecting the same target from different radar platforms;

[0148] The coding sequence obtaining module 210 is configured to preprocess each RCS echo data, divide the preprocessed RCS echo data into multiple weighted subsequences of the same length using a signal coding model based on Markov Monte Carlo sampling, and encode each weighted subsequence to obtain a coding sequence;

[0149] A first collaborative feature obtaining module 220 is configured to input the encoding sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0150] An angle feature acquisition module 230 is configured to input the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0151] A first interaction feature obtaining module 240 is configured to input each of the angle features into a first previous-level feature interaction model for feature interaction, and obtain a first interaction feature corresponding to each of the angle features;

[0152] A second collaborative feature obtaining module 250 is configured to input each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a plurality of corresponding second collaborative features;

[0153] A second interaction feature obtaining module 260 is configured to input each of the second collaborative features into a second previous-level feature interaction model for feature interaction, and obtain a second interaction feature corresponding to each of the second collaborative features;

[0154] The target recognition module 270 is used to use a deep attention fusion model to perform feature fusion on each of the second interaction features to obtain a fusion feature, and use a classification head to identify the target based on the fusion feature.

[0155] The specific definitions of the target recognition device based on multi-platform radar RCS data can be found in the definitions of the target recognition method based on multi-platform radar RCS data above and will not be repeated here. Each module in the aforementioned target recognition device based on multi-platform radar RCS data can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each of the aforementioned modules.

[0156] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a target recognition method based on multi-platform radar RCS data is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0157] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0158] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0159] Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms;

[0160] Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence;

[0161] Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0162] Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0163] Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features;

[0164] Inputting each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0165] Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features;

[0166] A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0168] Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms;

[0169] Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence;

[0170] Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature;

[0171] Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data;

[0172] Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features;

[0173] Inputting each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features;

[0174] Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features;

[0175] A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

[0176] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0177] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A target recognition method based on multi-platform radar RCS data, characterized in that: The method comprises: Acquire RCS echo data at multiple different angles, each RCS echo data being obtained by detecting the same target from different radar platforms; Preprocessing each of the RCS echo data respectively, dividing each of the preprocessed RCS echo data using a signal coding model based on Markov Monte Carlo sampling to obtain a plurality of weighted subsequences of the same length, and encoding each weighted subsequence to obtain a coded sequence; Inputting the encoded sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature; Inputting the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information to obtain an angle feature corresponding to each angle data; Inputting each of the angle features into a first previous-level feature interaction model for feature interaction to obtain a first interaction feature corresponding to each of the angle features; Inputting each of the first interaction features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features, wherein the data fusion model based on the autonomous mechanism includes multiple layers of feature extraction layers connected in sequence, each feature extraction layer including a first normalization layer, a multi-head attention layer, a second normalization layer, and a feedforward layer connected in sequence; Inputting each of the second collaborative features into a second previous-level feature interaction model for feature interaction to obtain a second interaction feature corresponding to each of the second collaborative features; A deep attention fusion model is used to fuse the second interaction features to obtain fused features, and a classification head is used to identify the target based on the fused features.

2. The target recognition method according to claim 1, characterized in that: When pre-processing each of the RCS echo data, normalization processing and center-of-gravity alignment processing are sequentially performed on each of the RCS echo data.

3. The target recognition method according to claim 2, characterized in that: The step of encoding the weighted subsequences to obtain the encoded sequence includes: Encode each weighted subsequence into a D-dimensional vector to obtain the corresponding weighted coded subsequence; Processing each weighted coding subsequence, adding a category hint vector and a segmentation vector to the front end and the back end of each weighted coding subsequence, respectively, to obtain a processed weighted coding subsequence; The coding sequence is obtained by constructing according to all processed weighted coding subsequences, the sequence segmentation coding matrix and the position coding matrix.

4. The target recognition method according to claim 1, characterized in that: The coding model based on angle information includes multiple angle guidance layers, and the input of each angle guidance layer is the angle data of the corresponding layer and the first collaborative feature. In each angle guidance layer: Linearly encode the input angle data to obtain angle-encoded data; Perform feature mapping on the first collaborative feature of the input to obtain a mapping feature; The angle encoding data is multiplied by the mapping feature, and the multiplication result is summed with the first collaborative feature using a residual connection to obtain the angle feature output by the angle guidance layer.

5. The target recognition method according to claim 1, characterized in that: In the first front-stage feature interaction model and the second front-stage feature interaction model, each input data is weighted and added to all other input data to obtain the corresponding interaction feature.

6. The target recognition method according to any one of claims 1 to 5, characterized in that: The target recognition method further includes: Constructing a multi-angle target recognition network based on the signal coding model based on Markov Monte Carlo sampling, the data fusion model based on the self-attention mechanism, the coding model based on angle information, the first front-stage feature interaction model, the second front-stage feature interaction model, the deep attention fusion model, and the classification head; The pre-processed RCS echo data are input into the multi-angle target recognition network to obtain the target recognition result.

7. The target recognition method according to claim 6, characterized in that: When training the multi-angle target recognition network, the multi-angle target recognition network is trained using a loss function based on a cross entropy formula to construct an algorithm; The signal coding model based on Markov Monte Carlo sampling includes adjustable weights, and the weights are used to weight each of the subsequences.

8. A target recognition device based on multi-platform radar RCS data, characterized in that: The device comprises: A multi-platform data acquisition module is used to acquire RCS echo data at multiple different angles, each of which is obtained by detecting the same target from different radar platforms; a coding sequence obtaining module, configured to preprocess each of the RCS echo data, divide the preprocessed RCS echo data into multiple weighted subsequences of the same length using a signal coding model based on Markov Monte Carlo sampling, and encode each weighted subsequence to obtain a coding sequence; A first collaborative feature obtaining module is used to input the coding sequence into a data fusion model based on a self-attention mechanism to perform feature fusion to obtain a first collaborative feature; an angle feature acquisition module, configured to input the angle data corresponding to each RCS echo data and the first collaborative feature into a coding model based on angle information, to obtain an angle feature corresponding to each angle data; A first interaction feature obtaining module is configured to input each of the angle features into a first previous feature interaction model for feature interaction, thereby obtaining a first interaction feature corresponding to each of the angle features; a second collaborative feature acquisition module, configured to input each of the first interactive features into a corresponding data fusion model based on an autonomous mechanism to obtain a corresponding plurality of second collaborative features, wherein the data fusion model based on the autonomous mechanism includes multiple feature extraction layers connected in sequence, each feature extraction layer including a first normalization layer, a multi-head attention layer, a second normalization layer, and a feedforward layer connected in sequence; A second interaction feature obtaining module, configured to input each of the second collaborative features into a second previous-level feature interaction model for feature interaction, and obtain a second interaction feature corresponding to each of the second collaborative features; The target recognition module is used to use a deep attention fusion model to fuse the second interaction features to obtain fused features, and use a classification head to identify the target based on the fused features.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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