Search

With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.

Time Domain Coding Metasurface (TDCM) is an emerging technology enabling dynamic modulation of electromagnetic waves. In response to the control characteristics of this technology, this paper presents a radar jamming method based on TDCM intrapulse and interpulse coding optimization. First, optimization models are established in fast and slow time domains. By optimizing intrapulse and interpulse phase coding, the energy redistribution of targets is achieved, thereby generating deceptive interference on the range-Doppler two-dimensional plot. Subsequently, a genetic algorithm is employed to solve this discrete optimization problem. Furthermore, this paper analyzes the effect of various modulation factors on interference effectiveness in terms of TDCM coding strategies, providing guidance for achieving optimal strategies for deceptive interference.

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.

Hyperspectral images (HSI) suffer from limited labeled data and the curse of dimensionality, which makes it difficult to classify imbalanced and small-sized HSI data. To address the mentioned issues, pixel pair features is designed to handle small-size problem. However, the augmented data inherits the class imbalance and even exhibits a larger imbalance ratio. Therefore, the ensemble-based Synthetic Minority Oversampling Technique (SMOTE) is integrated into pixel pair features to deal with imbalanced datasets and strengthen the quality of the augmented datasets. The proposed method is evaluated on five groups of HSI datasets and compared against seven state-of-the-art contrast algorithms, including ensemble-CNN (E-CNN), CNN with pixel pair features, ensemble CNN with pixel pair features, spatial–spectral E-ResNet, E-Transfering-VGGNet, ContextualNet, and CNN with K-means SMOTE. The proposed method demonstrates superior performance across three distinct difficulty levels, as determined by its average performance rank on five HSI datasets. This underscores its effectiveness in mitigating small size and class imbalance challenges.

Abstract This paper proposes a novel variational Bayesian (VB) adaptive Kalman filter with mismatched process noise covariance matrix (PNCM). Firstly, this paper explains the reason why the predicted error covariance matrix (PECM) is chosen for variational inference. Secondly, compared with the earlier VB adaptive Kalman filter (VB‐AKF‐Q), the proposed filter calculate the dynamic model of the PECM with its historical estimation information. Therefore, the proposed filter can overcome the influence of mismatched PNCM on the initial value setting of PECM in the VB‐AKF‐Q. Finally, we use the evidence lower bound for the proposed filter and give the convergence criterion on this basis. Some examples with a target tracking simulation are carried out to demonstrate the superiority of the proposed filter.

Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.

Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classification tasks in recent years. However, simple GA faces challenges, such as complex calculation, poor noise immunity, and slow convergence. This research proposes a two-step ensemble protocol for LULC classification using a grayscale-spatial-based GA model. The first ensemble framework uses fuzzy c-means to classify pixels into those that are difficult to cluster and those that are easy to cluster, which aids in reducing the search space for evolutionary computation. The second ensemble framework uses neighborhood windows as heuristic information to adaptively modify the objective function and mutation probability of the GA, which brings valuable benefits to the discrimination and decision of GA. In this study, three research areas in Dangyang, China, are utilized to validate the effectiveness of the proposed method. The experiments show that the proposed method can effectively maintain the image details, restrain noise, and achieve rapid algorithm convergence. Compared with the reference methods, the best overall accuracy obtained by the proposed algorithm is 88.72%.

Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton’s method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware’s characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program.

With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial–spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity.

Abstract Sparse frequency agile linear frequency modulation‐orthogonal frequency division multiplexing (LFM‐OFDM) radar can improve anti‐interference ability as well as reduce the sampling rate of the radar system. However, the frequency agility makes it difficult to realize coherent processing by using the fast Fourier transform. To overcome this problem, a signal processing scheme for the sparse frequency agile LFM‐OFDM radar is proposed to obtain the high range and Doppler resolution in this study. The subcarriers are synthesised to an LFM signal in the time domain at first. Then, within the framework of the compressed sensing theory, an improved orthogonal matching pursuit algorithm is proposed to obtain the high‐resolution range‐velocity profile while reducing the computation complexity. Finally, several numerical simulations are provided to verify the effectiveness of the proposed method for moving targets.

Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies from the background. Informative bands are very important for the accuracy improvement of the detection technology. However, most of the abnormal targets segmentation methods focus on the usage of all the spectral features, thus are easily affected by redundant bands or feature noise. A hyperspectral anomaly detection algorithm based on subfeature ensemble is proposed in this article. The proposed method consists of the following steps. First, the bands of the original HSI are normalized and randomly divided into several subfeature sets according to different proportions. Second, six methods including the prior-based tensor approximation algorithm (PTA), Reed–Xiaoli method, a low-rank and sparse representation method, a low-rank and sparse matrix decomposition-based Mahalanobis distance method, the graph and total variation regularized low-rank representation-based method, and a method based on tensor principal component analysis are applied to detect anomalies on the original HSI, and the method with the best performance is used to obtain an enhanced feature set. Then, the enhanced features and the subfeatures are ensembled iteratively to construct a new dataset. Finally, the PTA method is operated on the dataset with ensemble features to get the final abnormal target results. Six hyperspectral datasets are used in the experiment. Seven methods are employed as comparisons. The results are analyzed from both qualitative and quantitative perspectives. Extensive experimental results illustrate that the proposed method performs best on all datasets.

With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a you-only-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional GAs remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize the contextual and hierarchical information of image objects in addition to solely using spectral information. In addition, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with the spatial-based GA offers a promising method [ensemble spatial–spectral genetic algorithm (E-nGA)] for automating the process of monitoring forest loss. The research in this article is presented in four parts. First, block-matching and 3-D filtering is performed to suppress noises while enhancing valuable information. The difference image is, then, generated using the image difference method. Afterward, context-based saliency detection and fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently used classification methods, as well as the simple GA, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

For the application of a digital array signal processing system with a high sampling rate and large bandwidth, and considering the constraints of limited resources (performance, volume, power consumption, weight, etc.), this paper proposes a design and implementation method of broadband digital array signal processing system based on radio frequency system on a chip. The system focuses on multi-channel synchronization under a high sampling rate and multi-channel amplitude and phase calibration under large bandwidth to form an all-digital, low-power, multi-channel, high-speed, large bandwidth hardware platform suitable for broadband digital array signal processing. Through the implementation of the Multiple-Input Multiple-Output phased-array digital beamforming algorithm, the test conducted verifies that the system has superior signal processing performance of the broadband digital array.

The existing research proposes an intra-pulse frequency agile radar waveform with “active” anti-jamming characteristics. It uses the discontinuity and periodicity of the interrupted-sampling repeater jamming and combines the anti-jamming algorithm to effectively suppress interrupted-sampling repeater jamming. In order to improve the jamming effectiveness of the interferer for the intra-pulse frequency agile waveform, this paper proposes to jam the intra-pulse frequency agile radar by using a non-uniform interrupted-sampling and forwarding method under parameter constraints. The proposed method first obtains the sub-pulse width of the intra-pulse frequency agile radar waveform by parameter estimation of the intercepted intra-pulse frequency agile radar signal through time–frequency ridge extraction and wavelet transform. Then, we construct non-uniform interrupted-sampling repeater jamming based on sub-pulse width constraint interference parameters. Theoretical analysis and results show that the non-uniform interrupted-sampling forwarding under parameter constraints makes it challenging to suppress interference in multiple domains, such as the time–frequency and pulse compression domain for intra-pulse frequency agile radar, which significantly improves the jamming capability of the jammer for intra-pulse frequency agile radar.

Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming.

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method.