Radar Signal Sorting Research Articles

A Study of Various Clustering Algorithms Used for Radar Signal Sorting

Radar signal sorting plays a significant part in radar countermeasure technology and reconnaissance systems. By means of radar signal sorting, several radars and their parameters in the battlefield are precisely recognized and placed in the radar records for subsequent positioning and jamming processing. The basic sorting methods cannot fulfill the sorting process with accurate and efficient results. Therefore, in this paper, we conduct a study on two main classes clustering techniques, the first is hierarchical based clustering, and the second one is partition based clustering, which have different characteristics into groups of pulses and they can sort and handle large number of radar sequence with high precision and accuracy. The numerical simulations studied and compared both methods under different perspectives to clarify a new directions based on the insightful investigations of these sorting techniques.

Improved radar signal sorting algorithm based on density-peak-clustering approach

Improved radar signal sorting algorithm based on density-peak-clustering approach

Radar signal sorting based on image semantic segmentation

Abstract This paper explores the application of image semantic segmentation networks in radar signal sorting. Unlike traditional methods, it does not depend on predefined parameters or prior information, so it can show enhanced adaptability in dealing with a more complex electromagnetic environment. Firstly, the pulse descriptor PDW of collected signals is encoded into corresponding sequence images, and then U-net is used to train pulse sequence images. The trained model can be applied with high accuracy in radar signal sorting. At the same time, an attention mechanism is introduced into the U-net, which improves the recognition rate of signal features.

Radar Reconnaissance Pulse-Splitting Modeling and Detection Method

When radar receivers adopt digital channelization, it is prone to generating a cross-channel split signal, the rabbit ear effect, and a transition band repeated signal, leading to errors in radar signal sorting or identification. The pulse-splitting model and detection method proposed in this paper can model split pulses and identify them in radar pulse streams, facilitating the merging of split pulses to enhance sorting and identification performance. Firstly, the mechanism of splitting pulse generation is deeply analyzed, and the splitting site theory is proposed. Then, the split pulse signal model and the split pulse number statistical model based on geometric distribution are constructed, which are used to guide the construction of simulation data of split pulse flow with different characteristics. Furthermore, a time-domain convergence degree (TCD) index is proposed to characterize the pulse split phenomenon. At the same time, in order to avoid a large number of threshold searching problems in pulse-splitting detection, an empirical formula for the pulse-splitting detection threshold based on the TCD is given to quickly determine whether there is a pulse train split problem. The selected measured radar pulse stream is verified to follow a geometric distribution at a significance level of 0.05. The proposed method achieved a detection accuracy of at least 99.55% on the simulation dataset and at least 95.68% on the experimental dataset, validating the rationality of pulse-splitting modeling and the effectiveness of the detection method.

Radar Signal Sorting With Multiple Self-Attention Coupling Mechanism Based Transformer Network

Radar Signal Sorting With Multiple Self-Attention Coupling Mechanism Based Transformer Network

Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation

Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.

An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

AbstractThe accuracy of radar emitter signal sorting nowadays deteriorates due to the high flexibility and complexity of modern radar pulse streams and the density of crowded electromagnetic environment. In modern radar signal sorting based on pulse repetition interval, conventional methods usually fail to achieve acceptable accuracy and lack stable performance for two main reasons: (1) Conventional methods require a large number of pulses in the stream, which is not practical in many applications. (2) These methods are sensitive to pulse loss and random noise pulses. These two reasons are the main problem that is addressed in this paper. Our proposed model is a machine learning architecture called Unified Residual Recurrent Neural Network (URRNN). In this architecture, residual neural network and recurrent neural network are combined and modified to alleviate the forementioned shortcomings of traditional approaches and enhance the model performance in both classification and deinterleaving tasks. This aim is achieved due to the fact that URRNN extracts both spatial and temporal features, which means more information about processed stream that is exploited to enhance model performance. Three different architectural combinations of URRNN, which show high accuracy and reasonable processing time, are built and trained. The structural and functional description are provided for each architecture. Simulation demonstrates high accuracy and reliable performance of the proposed methods in different circumstances. The results are compared with the results obtained by other conventional machine learning techniques.

A Novel Radar Signals Sorting Method via Residual Graph Convolutional Network

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.

Radar Pulse Stream Clustering Based on MaskRCNN Instance Segmentation Network

In this paper, a clustering algorithm based on the MaskRCNN instance segmentation network is proposed to address the radar pulse stream clustering problem. Compared with traditional algorithms, the suggested algorithm successfully avoids the problem of preset parameters and algorithm structure constraints by using instance segmentation networks as clustering algorithms, and provides an end-to-end solution for the radar signal sorting issue. A PDW lattice mapping method is proposed to adapt to the input form of the instance segmentation network. The instance segmentation network based on ResNet, FPN, RPN, and RolAlign structure detects and masks the individual from the same or different radar signals. The simulation results show that the suggested algorithm performs the sorting of radar pulse stream under the condition of serious overlapping of radar parameters.

Design Principle of RF Stealth Anti-Sorting Signal Based on Multi-Dimensional Compound Modulation with Pseudo-Center Width Agility

Anti-sorting signal design is an important direction of radio frequency (RF) stealth signal design. The RF stealth signal design is based on the anti-sorting signal design principle, which is essentially the failure principle of the radar signal sorting algorithm. Cluster pre-sorting, the key to radar signal sorting, has the advantages of fast sorting, simultaneous sorting of multiple sources, and greatly reduced computational pressure of the main sorting. However, a unified and widely applicable cluster-sorting failure principle guiding the anti-sorting signal design has not been formally reported in RF stealth anti-sorting signal design. In this paper, the principles of the data field-based K-means clustering algorithm and the fuzzy C-means clustering algorithm are first studied. Aiming at the key step of data similarity measurement in the clustering algorithm, the failure principle of cluster sorting based on pseudo-center wide-agile multi-dimensional compound modulation is proposed. This principle can correctly guide the design of the anti-clustering sorting signal, so it is also called the design principle of the RF stealth anti-sorting signal based on pseudo-center wide-agile multi-dimensional compound modulation. The correctness of the principle is proved by formula derivation, signal simulation, and a sorting experiment. Through a signal comparison simulation with random interference pulse anti-sorting signals, it is strongly proved that the anti-sorting performance of signals designed under the guidance of the anti-clustering signal design principle proposed in this paper is stronger than that of random interference pulse signals. This study provides theoretical support for designing RF stealth anti-sorting signals. Using the signal design principle proposed in this paper, the anti-sorting performance of the RF stealth signal is improved by 10%. The principle of signal design helps to improve design efficiency.

Contrastive time–frequency learning for radar signal sorting

With the development of radar technology, the electromagnetic environment becomes more and more complex. The acquisition of labeled data is costly, and the existing radar signal sorting and recognition methods have poor generalization capability. To address this problem, a radar signal recognition technique based on time–frequency analysis and contrastive learning is proposed in this paper. The classification process is improved according to the characteristics of radar signals without using labels. The proposed method, contrastive time–frequency learning, can use the unsupervised data to sort and identify radar signals. By augmenting the data with various channel conditions, the generalization ability of the classification model is also improved, especially for the situation with low SNR. The experiments prove the good performance of the proposed method, and classification accuracy for radar signals can be up to 98.17%.

Research on the SDIF Failure Principle for RF Stealth Radar Signal Design

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation.

Improvement of Starting Point Selection of Data Field Clustering Algorithm

Since it does not depend on the starting point selection, data field clustering can perform unsupervised clustering according to the data distribution characteristics. However, due to its drawback of high computational complexity caused by iterative updates, it is not suitable for the scenarios with high real-time requirements such as radar signal sorting. In this paper, an improved method of starting point selection is proposed to address the problems of low timeliness and poor interference immunity of data field clustering in radar signal sorting. The performance of the improved algorithm is simulated and verified in a complex electromagnetic environment, and the results show that the improvement of the data field clustering in this paper improves the performance of the algorithm.

Key Radar Signal Sorting and Recognition Method Based on Clustering Combined PRI transform Algorithm

In this paper, we investigate the problem of key radar signal sorting and recognition in electronic intelligence (ELINT). Our major contribution is the development of a combined approach based on clustering and PRI transform algorithm, to solve the problem that the traditional methods based on Pulse Description Words (PDW) were not exclusively targeted at tiny particular signals and, less time-efficient. We achieve this in three steps: firstly, PDW presorting is carried out by the DBSCAN clustering algorithm, then, PRI estimates of each cluster are obtained by the PRI transformation algorithm, finally, by judging the matching between various PRI estimates and key targets, it is determined whether the current signal contains key target signals or not. Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.

Subspace Decomposition Based Adaptive Density Peak Clustering for Radar Signals Sorting

Radar signal sorting (RSS) plays an important role in the electronic support measurement system. However, the existing clustering-based RSS methods depend heavily on prior knowledge to achieve excellent performance, which may bring severe challenges to RSS in actual scenarios. This letter proposes a novel subspace decomposition based adaptive density peak clustering (SD-ADPC) method to address the problems of low accuracy and high computational cost in RSS. First, the original complex radar signal data is directly decomposed into two-dimensional (2D) subspace by t-distributed random neighborhood embedding (t-SNE). Then, based on the outlier detection of the products between the peak density and the distance of the data points, ADPC is used to adaptively determine the optimal clustering centers of the original data in 2D subspace. Finally, the reminding data is assigned to its nearest cluster with Euclidean distance in one step. The experimental results of the simulated RSS dataset and the open baselines show that our proposed method does not require any knowledge and can achieve better or competitive performance in terms of accuracy and computational cost, compared to existing state-of-the-art methods.

UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial vehicle (UAV) swarm to monitor reconnaissance areas and put forward a novel deep transfer learning based signal sorting method. The UAV swarm can collect the pulses from different time and spatial domains, and interference and missing pulses in main sorting processing can be relieved dramatically. In our model, we pre-train our model with the data collected from multiple source areas, which corresponds to different areas detected by different parts of UAV swarms. Then we fine-tune our model with the data of the target area. The experimental results prove that the signal sorting accuracy of methods based on deep transfer learning, i.e., YOLO-MobileNet, F-RCNN and cascade RCNN, are higher than that of the baseline methods. In addition, the signal sorting accuracy of traditional methods based on deep learning can be greatly improved with the help of transfer learning.

Research on the improved algorithm of radar signal sorting based on maximum SNR

This paper proposes an improved radar signal sorting algorithm based on the maximum signal-to-noise ratio criterion, aiming at the high computational complexity or poor separation effect of traditional signal sorting algorithms. The algorithm uses the maximum signal-to-noise ratio when the independent signals are completely separated to establish an objective function. The source signal is replaced by the mixed signal processed by the adaptive length moving average. The extreme value problem of the objective function is transformed into a generalized eigenvalue problem. Compared with the traditional method, the improved algorithm not only retains the separation effect of the traditional information theory sorting algorithm, but also has lower computational complexity. Experimental simulation proves that the algorithm can separate linearly aliased radar signals more effectively.

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Radar signal sorting is the core part of electronic support measures, which is responsible for deinterleaving the overlapping pulse sequences received by the receiver from the complex environment, separating different radiation source signals, and providing support for radiation source identification. Particle swarm optimization (PSO) is a population-based global optimization algorithm with great advantages in intelligent sorting of complex signals, which can adapt to the electromagnetic environment with complex and variable radiation source signals and high pulse stream density. However, the PSO-based sorting method is prone to premature convergence and cannot adaptively adjust particle swarm parameters and positions. In this paper, a dynamic modified chaotic PSO algorithm (DMCPSO) is proposed. Chaotic search is used to increase the diversity of particle swarm in the later iteration to avoid premature convergence and falling into local optimum. Adaptive adjustment parameters related to the particle fitness value are adopted to balance the ability of global search and local search. A new fitness function is proposed and the particle position is dynamically corrected by clustering analysis to improve the accuracy of particle position optimization and avoid the influence from the distribution of feature parameters. The simulation results show that the DMCPSO algorithm provides stable and fast performance with excellent sorting indexes in complex, variable, and dense signal environment.

A new clustering and sorting algorithm for radar emitter signals

The traditional k-means clustering algorithm needs to set parameters manually in radar signal sorting application, which is sensitive to isolated points and prone to “batch” phenomenon, and the selection of initial clustering center has a direct impact on clustering effect. In order to solve the above problems, a radar signal sorting algorithm based on data field and improved k-means clustering is proposed. Firstly, the potential values of all data samples are calculated according to the theory of data field. After the isolated points are removed, the maximum local potential values are found. The nearest sample data is selected as the initial clustering center, and the maximum number of local potential values is selected as the clustering number. Finally, the improved k-means clustering algorithm is used to complete the radar signal sorting. The algorithm can automatically obtain the initial clustering center and the number of clusters, and the clustering results have larger inter cluster distance and smaller intra cluster distance, so the clustering results are better. Simulation results verify the feasibility of the algorithm.