There is a growing body of research on the swarm unmanned aerial vehicle (UAV) in recent years, which has the characteristics of small, low speed, and low height as radar target. To confront the swarm UAV, the design of anti-UAV radar system based on multiple input multiple output (MIMO) is put forward, which can elevate the performance of resolution, angle accuracy, high data rate, and tracking flexibility for swarm UAV detection. Target resolution and detection are the core problem in detecting the swarm UAV. The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar. Since MIMO radar has better performance in resolution, swarm UAV detection still has difficulty in target detection. This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect. Subsequently, signal processing and data processing based on the detection fusion algorithm above are designed, forming a high resolution detection loop. Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.
In modern war, radar countermeasure is becoming increasingly fierce, and the enemy jamming time and pattern are changing more randomly. It is challenging for the radar to efficiently identify jamming and obtain precise parameter information, particularly in low signal-to-noise ratio (SNR) situations. In this paper, an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue. Firstly, a joint algorithm based on YOLOv5 convolutional neural networks (CNNs) is proposed, which is used to achieve the jamming signal classification and preliminary parameter estimation. Furthermore, an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test, feature region search, position regression, spectrum interpolation, etc., which realizes the accurate estimation of jamming carrier frequency, relative delay, Doppler frequency shift, and other parameters. Finally, the approach has improved performance for complex jamming recognition and parameter estimation under low SNR, and the recognition rate can reach 98% under ?15 dB SNR, according to simulation and real data verification results.
Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.
For bistatic multiple-input multiple-output (MIMO) radar, this paper presents a robust and direction finding method in strong impulse noise environment. By means of a new lower order covariance, the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood (ML) estimation method. A quantum equilibrium optimizer algorithm (QEOA) is devised to resolve the corresponding objective function for efficient and accurate direction finding. The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations, e.g., locating coherent signal sources with very few snapshots in strong impulse noise. Other than that, the Cramér-Rao bound (CRB) under impulse noise environment has been drawn to test the capability of the presented method.
The quality of synthetic aperture radar (SAR) image degrades in the case of multiple imaging projection planes (IPPs) and multiple overlapping ship targets, and then the performance of target classification and recognition can be influenced. For addressing this issue, a method for extracting ship targets with overlaps via the expectation maximization (EM) algorithm is proposed. First, the scatterers of ship targets are obtained via the target detection technique. Then, the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP. Afterwards, a novel image amplitude estimation approach is proposed, with which the radar image of a single target with a single IPP can be generated. The proposed method can accomplish IPP selection and targets separation in the image domain, which can improve the image quality and reserve the target information most possibly. Results of simulated and real measured data demonstrate the effectiveness of the proposed method.
The application scope of the forward scatter radar (FSR) based on the Global Navigation Satellite System (GNSS) can be expanded by improving the detection capability. Firstly, the forward-scatter signal model when the target crosses the baseline is constructed. Then, the detection method of the forward-scatter signal based on the Rényi entropy of time-frequency distribution is proposed and the detection performance with different time-frequency distributions is compared. Simulation results show that the method based on the smooth pseudo Wigner-Ville distribution (SPWVD) can achieve the best performance. Next, combined with the geometry of FSR, the influence on detection performance of the relative distance between the target and the baseline is analyzed. Finally, the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate (CFAR) detection.
Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood (BML) algorithm. However, the difference beam is rarely used in super-resolution methods, especially in low elevation estimation. The target airspace information in the difference beam is different from the target airspace information in the sum beam. And the use of difference beams does not significantly increase the complexity of the system and algorithms. Thus, this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm. And the direction and number of beams can be adjusted according to the actual needs. The theoretical target elevation angle root means square error (RMSE) and the computational complexity of the proposed algorithms are analyzed. Finally, computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
Three-dimensional (3D) synthetic aperture radar (SAR) extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry (InSAR) and SAR tomography (TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation (AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of 360°, which is similar to the flight path of airborne circular SAR (CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.
Passive jamming is believed to have very good potential in countermeasure community. In this paper, a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed. The amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state, and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying effects. Dependency of the decoying effect on corner reflectors ’ radar cross section and positions are analyzed and simulated. Results show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.
In this paper, we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing (OFDM) signal. A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden. Firstly, the modulation symbol domain (MSD) method is used to roughly estimate the delay and Doppler of targets. Then, to obtain high-precision Doppler estimation, the atomic norm (AN) based on the multiple measurement vectors (MMV) model (MMV-AN) is used to manifest the signal sparsity in the continuous Doppler domain. At the same time, a reference signal compensation (RSC) method is presented to obtain high-precision delay estimation. Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms. In addition, the proposed method also possesses computational advantages compared with the joint parameter estimation.
Mainlobe jamming (MLJ) brings a big challenge for radar target detection, tracking, and identification. The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures (ECCM) field. Target parameters and target direction estimation is difficult in radar MLJ. A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper. The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle. Precisely, the eigen-projection matrix processing (EMP) algorithm is adopted to suppress the MLJ, and the target range is estimated effectively through the beamforming and pulse compression. Then the target angle can be effectively estimated by the atom-reconstruction method. Without any prior knowledge, the MLJ can be canceled, and the angle estimation accuracy is well preserved. Furthermore, the proposed method does not have strict requirement for radar array construction, and it can be applied for linear array and planar array. Moreover, the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth (or elevation) equals to the jamming azimuth (or elevation), because the MLJ is suppressed in spatial plane dimension.
Link16 data link is the communication standard of the joint tactical information distribution system (JTIDS) used by the U.S. military and North Atlantic Treaty Organization, which is applied as the opportunistic illuminator for passive radar in this paper. The time-domain expression of the Link16 signal is established, and its ambiguity function expression is derived. The time-delay dimension and Doppler dimension side peaks of which lead to the appearance of the false target during target detection. To solve the problem, the time-delay dimension and Doppler dimension side peaks suppression methods are proposed. For the problem that the conventional mismatched filter (MMF) cannot suppress the time-delay dimension side peaks, a neighborhood MMF (NMMF) is proposed. Experimental results demonstrate the effectiveness of the proposed methods.
In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.
The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.
This paper considers the non-line-of-sight (NLOS) vehicle localization problem by using millimeter-wave (MMW) automotive radar. Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results. Firstly, an electromagnetic (EM) wave NLOS multipath propagation model for vehicle scene is established. Subsequently, with the help of available multipath echoes, a complete NLOS vehicle localization algorithm is proposed. Finally, simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.
Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recognition. To solve this problem, an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed. First, a small amount of labeled data are randomly sampled by using the bootstrap method, loss functions for three common deep learning networks are improved, the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification. Subsequently, the dataset obtained after sampling is adopted to train three improved networks so as to build the initial model. In addition, the unlabeled data are preliminarily screened through dynamic time warping (DTW) and then input into the initial model trained previously for judgment. If the judgment results of two or more networks are consistent, the unlabeled data are labeled and put into the labeled data set. Lastly, the three network models are input into the labeled dataset for training, and the final model is built. As revealed by the simulation results, the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.
The conventional two dimensional (2D) inverse synthetic aperture radar (ISAR) imaging fails to provide the targets’ three dimensional (3D) information. In this paper, a 3D ISAR imaging method for the space target is proposed based on mutli-orbit observation data and an improved orthogonal matching pursuit (OMP) algorithm. Firstly, the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension. Then, an improved OMP algorithm is applied to recover the space target’s amplitude information via the 2D matrix data. Finally, scattering centers can be reconstructed with specific three dimensional locations. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.
Ultrahigh resolution synthetic aperture radar (SAR) imaging for ship targets is significant in SAR imaging, but it suffers from high frequency vibration of the platform, which will induce defocus into SAR imaging results. In this paper, a novel compensation method based on the sinusoidal frequency modulation Fourier-Bessel transform (SFMFBT) is proposed, it can estimate the vibration errors, and the phase shift ambiguity can be avoided via extracting the time frequency ridge consequently. By constructing the corresponding compensation function and combined with the inverse SAR (ISAR) technique, well-focused imaging results can be obtained. The simulation imaging results of ship targets demonstrate the validity of the proposed approach.
Pulse repetition interval (PRI) modulation recognition and pulse sequence search are significant for effective electronic support measures. In modern electromagnetic environments, different types of inter-pulse slide radars are highly confusing. There are few available training samples in practical situations, which leads to a low recognition accuracy and poor search effect of the pulse sequence. In this paper, an approach based on bi-directional long short-term memory (BiLSTM) networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed. The simulation results demonstrate that the proposed algorithm can recognize unilinear, bilinear, sawtooth, and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.
To address the problem that dynamic wind turbine clutter (WTC) significantly degrades the performance of weather radar, a WTC mitigation algorithm using morphological component analysis (MCA) with group sparsity is studied in this paper. The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo. After that, the MCA algorithm is applied and the window used in the short-time Fourier transform (STFT) is optimized to lessen the spectrum leakage of WTC. Finally, the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution, thus contributing to better estimation performance of weather signals. The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.
Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.
Polarization feature is one of the important features of radar targets, which has been used in many fields. In this paper, the grid models of some typical foreign moving targets are constructed on the simulation platform, such as glider, cruiser, fixed wing aircraft, and rotorcraft. The electromagnetic scattering characteristics of the moving platforms under the incidence of circular polarization waves are calculated. The typical polarization characteristics which the orthogonal and in-phase components have in the echoes are analyzed and proved. Based on the polarization scattering matrix (PSM) theory, from the point of view of the physical reproduction, the technical status quo that the existing technical approaches are difficult to realize the passive simulation of polarization characteristic of the target is summarized. To solve this problem, combined with the vector synthesis law, the realization mechanism of controllable polarization characteristic of target echoes is proposed, the analytical expressions of polarization control matrix and polarization ratio are deduced, and the controllability of polarization ratio feature in the case of circular polarization is verified by simulation calculation.
In airborne array synthetic aperture radar (SAR), the three-dimensional (3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection (BP) and the data extraction method based on modified uniformly redundant arrays (MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing (CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally, by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.
To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming (ILP) and exhaustive method (EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers’ number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.
Phased array radar ’s measurements include two direction cosine and range measurements, which can be obtained in the direction cosine coordinates. State equation of the target is nonlinear with the measurements and in order to solve the nonlinear problem, debiased conversion measurements based target tracking with direction cosine and range measurements in direction cosine coordinates named DCMKF-PreDcos is proposed first in this paper, where the predicted information is introduced to calculate the converted measurement errors ’ statistical characteristics to eliminate the correlation between measurement noise and the converted measurement errors covariance. When range rate information can be obtained further, based on the above DCMKF-PreDcos ’ filtering result, the sequential filtering is adopted to process the additional range rate measurement and the DCMKF-PreDcos algorithm with extra range rate information is given. The predicted information is also introduced to calculate the involved statistical characteristics of converted measurements. The effectiveness of the proposed algorithms is shown in simulation results.
This study deals with the problem of mainlobe jamming suppression for rotated array radar. The interference becomes spatially nonstationary while the radar array rotates, which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio (SNR) of pulse compression. In this paper, we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal (RAMS) model firstly. Then the corresponding algorithm improved blind source separation (BSS) using the frequency domain of robust principal component analysis (FD-RPCA-BSS) is proposed based on the established rotating model. It can eliminate the influence of the rotating parts and address the problem of loss of SNR . Finally, the measured peak-to-average power ratio (PAPR) of each separated channel is performed to identify the target echo channel among the separated channels. Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.
Introducing frequency agility into a distributed multiple-input multiple-output (MIMO) radar can significantly enhance its anti-jamming ability. However, it would cause the sidelobe pedestal problem in multi-target parameter estimation. Sparse recovery is an effective way to address this problem, but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity. In this paper, we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars, by modifying the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed method is then verified by simulation results.
The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.
Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.
