Search Results (408)

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target’s position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging. However, owing to the limitation of antenna aperture, the imaging resolution of real aperture radar is restricted. By implementing the super-resolution techniques in array signal processing into missile-borne radar 3D forward-looking imaging, the resolution can be further improved. In this paper, a 3D forward-looking imaging algorithm based on the two-dimensional (2D) super-resolution algorithm is proposed for missile-borne planar array radars. In the proposed algorithm, a forward-looking planar array with scanning beams is considered, and each range-pulse cell in the received data is processed one by one using a 2D super-resolution method with the error function constructed according to the weighted least squares (WLS) criterion to generate a group of 2D spectra in the azimuth-pitch domain. Considering the lack of training samples, the super-resolution spectrum of each range-pulse cell is estimated via adaptive iteration processing only with one sample, i.e., the cell under process. After that, all the 2D super-resolution spectra in azimuth-pitch are accumulated according to the changes in instantaneous beam centers of the beam scanning. As is verified by simulation results, the proposed algorithm outperforms the real aperture imaging method in terms of azimuth-pitch resolution and can obtain 3D forward-looking images that are of a higher quality. Full article

With the continuous development of synthetic aperture radar (SAR) jamming technology, low-power maneuvering repeater jammers are now flexible and can be located on multiple unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) at the same time, which greatly increases the difficulty of the anti-maneuvering repeater jamming method for spaceborne SAR. Due to the low-power transmission, the locations of the low-power repeater jammers and the protected areas in the imaged swath are relatively close in distance, while the transmission delay of the jamming is approximately equal to the pulse repetition interval (PRI). According to this phenomenon, an anti-maneuvering repeater jamming method using up- and down-chirp modulation is proposed in this paper. After alternately transmitting up- and down-chirp modulation signals, echoes of the jamming and the protected area are recorded in the same location within the echo-receiving window and are related to different chirp modulations. To remove the jamming echoes, de-chirping and frequency filtering are adopted after echo data segmentation. With jamming interference removal using frequency notch filtering, parts of the spectra corresponding to the desired echoes of the imaged swath are simultaneously removed. To recover the unwanted removed range spectra, linear prediction is introduced to improve the focusing quality. Finally, simulation results on both point and distributed targets validate the proposed anti-maneuvering repeater jamming method by using up- and down-chirp modulation. Full article

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance. Full article

Radar emitter signal deinterleaving based on pulse description words (PDWs) is a challenging task in the field of electronic warfare because of the parameter sparsity and uncertainty of PDWs. In this paper, a modulation-hypothesis-augmented Transformer model is proposed to identify emitters from a single PDW with an end-to-end manner. Firstly, the pulse features are enriched by the modulation hypothesis mechanism to generate I/Q complex signals from PDWs. Secondly, a multiple-parameter embedding method is proposed to expand the signal discriminative features and to enhance the identification capability of emitters. Moreover, a novel Transformer deep learning model, named PulseFormer and composed of spectral convolution, multi-layer perceptron, and self-attention based basic blocks, is proposed for discriminative feature extraction, emitter identification, and signal deinterleaving. Experimental results on synthesized PDW dataset show that the proposed method performs better on emitter signal deinterleaving in complex environments without relying on the pulse repetition interval (PRI). Compared with other deep learning methods, the PulseFormer performs better in noisy environments. Full article

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a $5-way5-shot$ setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments. Full article

Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic Support (ES) systems. Existing recognition methods focus more on algorithmic designs, such as neural network structure designs, to improve recognition performance. However, in open electromagnetic environments with increased flexibility in radar transmission, these methods would suffer performance degradation due to domain shifts between training and testing datasets. To address this issue, this study proposes a robust radar inter-pulse modulation feature extraction and recognition method based on disentangled representation learning. At first, inspired by the Representation Learning Theory (RLT), the received radar pulse sequences can be disentangled into three explanatory factors related to (i) modulation types, (ii) modulation parameters, and (iii) measurement characteristics, such as measurement noise. Then, an explainable radar pulse sequence disentanglement network is proposed based on auto-encoding variational Bayes. The features extracted through the proposed method can effectively represent the key latent factors related to recognition tasks and maintain performance under domain shift conditions. Experiments on both ideal and non-ideal situations demonstrate the effectiveness, robustness, and superiority of the proposed method in comparison with other methods. Full article

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios. Full article

The modulation classification technology for radar intra-pulse signals is important in the electronic countermeasures field. As the high quality labeled radar signals are difficult to be captured in the real applications, the signal modulation classification base on the limited number of labeled samples is playing a more and more important role. To relieve the requirement of the labeled samples, many self-supervised learning (SeSL) models exist. However, as they cannot fully explore the information of the labeled samples and rely significantly on the unlabeled samples, highly time-consuming processing of the pseudo-labels of the unlabeled samples is caused. To solve these problems, a supervised learning (SL) model, using the contrastive learning (CL) method (SL-CL), is proposed in this paper, which achieves a high classification accuracy, even adopting limited number of labeled training samples. The SL-CL model uses a two-stage training structure, in which the CL method is used in the first stage to effectively capture the features of samples, then the multilayer perceptron is applied in the second stage for the classification. Especially, the supervised contrastive loss is constructed to fully exploring the label information, which efficiently increases the classification accuracy. In the experiments, the SL-CL outperforms the comparison models in the situation of limited number of labeled samples available, which reaches 94% classification accuracy using 50 samples per class at 5 dB SNR. Full article

In the paper, a new design of a quasi-isotropic antenna for high-power electromagnetic (EM) field measurement is presented, along with its investigation into suitability. The measuring probe is intended for assessing pulsed microwaves, which cannot be measured by available meters due to the high value of electric field strength and short pulse duration. The measurement of such a strong field is required according to guidelines for protecting people against microwave fields, especially those emitted by radars. The proposed probe is based on the concept of dipole–diode detection. To enable high-power measurement, the receiving antenna is electrically “small,” allowing diode detection within the diode square-law characteristics range. Additionally, the shortened dipole length minimizes the spatial integration error, which can be significant in the case of microwave measurement. To obtain the desired antenna polarization, a new dipole geometry was proposed. Fulfilling the requirement of measuring all incident EM field polarizations, the receiving antenna was based on three dipoles arranged within a specific “magic” angular arrangement, ensuring a suitable quasi-isotropic radiation pattern. The proposed probe can operate in a frequency range from 1 GHz to 12 GHz. Full article

Azimuth resolution and swath width are two crucial parameters in spaceborne synthetic aperture radar (SAR) systems. However, it is difficult for conventional spaceborne SAR to simultaneously achieve high-resolution wide-swath (HRWS) due to the minimum antenna area constraint. To mitigate this limitation, some representative HRWS SAR imaging techniques have been investigated, e.g., the azimuth multichannel technique, digital beamforming (DBF) technique, and pulse repetition interval (PRI) variation technique. This paper focus on a comprehensive review of the three techniques with respect to their latest developments. First, some key parameters of HRWS SAR are presented and analyzed to help the reader establish the general concept of SAR. Second, three techniques are introduced in detail, roughly following a simple-to-complex approach, i.e., start with the basic concept, then move to the core principles and classic technical details, and finally report the technical challenges and corresponding solutions. Third, some in-depth insights on the comparison among the three techniques are given. The purpose of this paper is to provide a review and brief perspective on the development of HRWS SAR. Full article

Forward jammers replicate and retransmit radar signals back to generate coherent jamming signals and false targets, making anti-jamming an urgent issue in electronic warfare. Jamming transmitters work at saturation to maximize the retransmission power such that only the phase information of the angular waveform at the designated direction of arrival (DOA) is retained. Therefore, amplitude modulation of MIMO radar angular waveforms offers an advantage in combating forward jamming. We address both the design of unimodular MIMO waveforms and their associated mismatch filters to confront mainlobe jamming in this paper. Firstly, we design the MIMO waveforms to maximize the discrepancy between the retransmitted jamming and the spatially synthesized radar signal. We formulate the problem as unconstrained non-linear optimization and solve it using the conjugate gradient method. Particularly, we introduce fast Fourier transform (FFT) to accelerate the numeric calculation of both the objection function and its gradient. Secondly, we design a mismatch filter to further suppress the filtered jamming through convex optimization in polynomial time. The simulation results show that for an eight-element MIMO radar, we are able to reduce the correlation between the angular waveform and saturated forward jamming to −6.8 dB. Exploiting this difference, the mismatch filter can suppress the jamming peak by 19 dB at the cost of an SNR loss of less than 2 dB. Full article

Synthetic aperture radar (SAR) inter-pulse parameter agility technology involves dynamically adjusting parameters such as the pulse width, chirp rate, carrier frequency, and pulse repetition interval within a certain range; this effectively increases the complexity and uncertainty of radar waveforms, thereby countering active deceptive interference signals from multiple dimensions. With the development of active deceptive interference technology, single-parameter agility can no longer meet the requirements, making multi-parameter joint agility one of the main research directions. However, inter-pulse carrier frequency agility can cause azimuth Doppler chirp rate variation, making azimuth compression difficult and compensation computationally intensive, thus hindering imaging. Additionally, pulse repetition interval (PRI) agility leads to non-uniform azimuth sampling, severely deteriorating image quality. To address these issues, this paper proposes a multi-parameter agile SAR imaging scheme based on traditional frequency domain imaging algorithms. This scheme can handle joint agility of pulse width, chirp rate polarity, carrier frequency, and PRI, with relatively low computational complexity, making it feasible for engineering implementation. By inverting SAR images, the echoes with multi-parameter joint agility are obtained, and active deceptive interference signals are added for processing. The interference-suppressed imaging results verify the effectiveness of the proposed method. Furthermore, simulation results of point targets with multiple parameters under the proposed processing algorithm show that the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by 12 dB and 10 dB, respectively, compared to the traditional fixed waveform scheme. Full article

Accurately classifying the intra-pulse modulations of radar emitter signals is important for radar systems and can provide necessary information for relevant military command strategy and decision making. As strong additional white Gaussian noise (AWGN) leads to a lower signal-to-noise ratio (SNR) of received signals, which results in a poor classification accuracy on the classification models based on deep neural networks (DNNs), in this paper, we propose an effective denoising method based on a denoising diffusion probabilistic model (DDPM) for increasing the quality of signals. Trained with denoised signals, classification models can classify samples denoised by our method with better accuracy. The experiments based on three DNN classification models using different modal input, with undenoised data, data denoised by the convolutional denoising auto-encoder (CDAE), and our method’s denoised data, are conducted with three different conditions. The extensive experimental results indicate that our proposed method could denoise samples with lower values of the SNR, and that it is more effective for increasing the accuracy of DNN classification models for radar emitter signal intra-pulse modulations, where the average accuracy is increased from around 3 to 22 percentage points based on three different conditions. Full article

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the relationship between elevation frequency and range. It uses an elevation oblique subspace projection technique to construct an elevation adaptive filter, which separates clutter from ambiguous regions. Finally, residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments. Full article

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar. Full article