Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (117)

Search Parameters:
Keywords = adaptive dictionary

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 4567 KiB  
Article
Compressed Adaptive-Sampling-Rate Image Sensing Based on Overcomplete Dictionary
by Jianming Wang, Dingpeng Li, Qingqing Yang and Yi Peng
Entropy 2025, 27(7), 709; https://doi.org/10.3390/e27070709 - 30 Jun 2025
Viewed by 199
Abstract
In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates [...] Read more.
In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates to different types of blocks. The efficient, sparse representation of images is achieved by using an overcomplete ridgelet dictionary; at the same time, a reasonable dictionary-partitioning method is designed, which effectively reduces the number of candidate dictionary atoms and greatly improves the speed of classification. Unlike existing methods, the proposed method does not rely on the original signal, and computation is simple, making it particularly suitable for scenarios where a device’s computing power is limited. At the same time, the proposed method can accurately identify smooth image blocks and more reasonably allocate sampling rates to obtain a reconstructed image with better quality. The experimental results show that our method’s image reconstruction quality is superior to that of existing ARCS methods and still maintains low computational complexity. Full article
(This article belongs to the Section Signal and Data Analysis)
Show Figures

Figure 1

17 pages, 2287 KiB  
Article
A Self-Adaptive K-SVD Denoising Algorithm for Fiber Bragg Grating Spectral Signals
by Hang Gao, Xiaojia Liu, Da Qiu, Jingyi Liu, Kai Qian, Zhipeng Sun, Song Liu, Shiqiang Chen, Tingting Zhang and Yang Long
Symmetry 2025, 17(7), 991; https://doi.org/10.3390/sym17070991 - 23 Jun 2025
Viewed by 238
Abstract
In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study [...] Read more.
In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study proposes a self-adaptive K-SVD (SAK-SVD) denoising algorithm based on adaptive window parameter optimization, establishing a closed-loop iterative feedback mechanism through dual iterations between dictionary learning and parameter adjustment. This approach achieves a synergistic enhancement of noise suppression and signal fidelity. First, a dictionary learning framework based on K-SVD is constructed for initial denoising, and the peak feature region is extracted by differentiating the denoised signals. By constructing statistics on the number of sign changes, an adaptive adjustment model for the window size is established. This model dynamically tunes the window parameters in dictionary learning for iterative denoising, establishing a closed-loop architecture that integrates denoising evaluation with parameter optimization. The performance of SAK-SVD is evaluated through three experimental scenarios, demonstrating that SAK-SVD overcomes the rigid parameter limitations of traditional K-SVD in FBG spectral processing, enhances denoising performance, and thereby improves wavelength demodulation accuracy. For denoising undistorted waveforms, the optimal mean absolute error (MAE) decreases to 0.300 pm, representing a 25% reduction compared to the next-best method. For distorted waveforms, the optimal MAE drops to 3.9 pm, achieving a 63.38% reduction compared to the next-best method. This study provides both theoretical and technical support for high-precision fiber-optic sensing under complex working conditions. Crucially, the SAK-SVD framework establishes a universal, adaptive denoising paradigm for fiber Bragg grating (FBG) sensing. This paradigm has direct applicability to Raman spectroscopy, industrial ultrasound-based non-destructive testing, and biomedical signal enhancement (e.g., ECG artefact removal), thereby advancing high-precision measurement capabilities across photonics and engineering domains. Full article
(This article belongs to the Section Computer)
Show Figures

Figure 1

18 pages, 2250 KiB  
Article
Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning
by Zhiqi Gao, Na Yang, Pingping Huang, Wei Xu, Weixian Tan and Zhixia Wu
Electronics 2025, 14(12), 2350; https://doi.org/10.3390/electronics14122350 - 8 Jun 2025
Viewed by 328
Abstract
Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering [...] Read more.
Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The clutter plus noise covariance matrix is estimated via SBL combined with the updated dictionary. The experimental results demonstrate that the proposed algorithm can effectively suppress the clutter ridge expansion caused by the off-grid problem while reducing the computation burden significantly compared with the existing methods. Full article
Show Figures

Figure 1

18 pages, 1221 KiB  
Technical Note
swmm_api: A Python Package for Automation, Customization, and Visualization in SWMM-Based Urban Drainage Modeling
by Markus Pichler
Water 2025, 17(9), 1373; https://doi.org/10.3390/w17091373 - 1 May 2025
Viewed by 1152
Abstract
The Python package swmm_api addresses a critical gap in urban drainage modeling by providing a flexible, script-based tool for managing SWMM models. Recognizing the limitations of existing solutions, this study developed a Python-based approach that seamlessly integrates SWMM model creation, editing, analysis, and [...] Read more.
The Python package swmm_api addresses a critical gap in urban drainage modeling by providing a flexible, script-based tool for managing SWMM models. Recognizing the limitations of existing solutions, this study developed a Python-based approach that seamlessly integrates SWMM model creation, editing, analysis, and visualization within Python’s extensive ecosystem. The package offers intuitive, dictionary-like interactions with model components, enabling manipulation of input files and extraction of results as structured data. It supports advanced GIS integration, sensitivity analysis, calibration, and uncertainty estimation through libraries like GeoPandas, SALib, and SPOTPY. Results demonstrate significant efficiency improvements in repetitive tasks, including batch simulations, sensitivity analyses, and automated GIS data processing, exemplified by practical applications such as model updates for municipal sewer systems. The package significantly enhances reproducibility and facilitates transparent sharing of scientific workflows. Overall, swmm_api provides researchers and practitioners with a robust, adaptable solution for streamlined urban drainage modeling. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
Show Figures

Graphical abstract

17 pages, 2787 KiB  
Article
Improved Variational Bayes for Space-Time Adaptive Processing
by Kun Li, Jinyang Luo, Peng Li, Guisheng Liao, Zhixiang Huang and Lixia Yang
Entropy 2025, 27(3), 242; https://doi.org/10.3390/e27030242 - 26 Feb 2025
Viewed by 637
Abstract
To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in [...] Read more.
To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields. Full article
(This article belongs to the Section Signal and Data Analysis)
Show Figures

Figure 1

34 pages, 4479 KiB  
Article
Development of a Children’s Educational Dictionary for a Low-Resource Language Using AI Tools
by Diana Rakhimova, Aidana Karibayeva, Vladislav Karyukin, Assem Turarbek, Zhansaya Duisenbekkyzy and Rashid Aliyev
Computers 2024, 13(10), 253; https://doi.org/10.3390/computers13100253 - 2 Oct 2024
Cited by 4 | Viewed by 2569
Abstract
Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, [...] Read more.
Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, Mongolian, and others, due to the lack of qualitative and accessible resources, morphological complexity, and the semantics of agglutinative languages. This article presents research on early childhood learning resources for the low-resource Kazakh language. Generally, a dictionary for children differs from classical educational dictionaries. The difference between dictionaries for children and adults lies in their purpose and methods of presenting information. A themed dictionary will make learning and remembering new words easier for children because they will be presented in a specific context. This article discusses developing an approach to creating a thematic children’s dictionary of the low-resource Kazakh language using artificial intelligence. The proposed approach is based on several important stages: the initial formation of a list of English words with the use of ChatGPT; identification of their semantic weights; generation of phrases and sentences with the use of the list of semantically related words; translation of obtained phrases and sentences from English to Kazakh, dividing them into bigrams and trigrams; and processing with Kazakh language POS pattern tag templates to adapt them for children. When the dictionary was formed, the semantic proximity of words and phrases to the given theme and age restrictions for children were taken into account. The formed dictionary phrases were evaluated using the cosine similarity, Euclidean similarity, and Manhattan distance metrics. Moreover, the dictionary was extended with video and audio data by implementing models like DALL-E 3, Midjourney, and Stable Diffusion to illustrate the dictionary data and TTS (Text to Speech) technology for the Kazakh language for voice synthesis. The developed thematic dictionary approach was tested, and a SUS (System Usability Scale) assessment of the application was conducted. The experimental results demonstrate the proposed approach’s high efficiency and its potential for wide use in educational purposes. Full article
(This article belongs to the Special Issue Smart Learning Environments)
Show Figures

Figure 1

18 pages, 609 KiB  
Article
Few-Shot Classification Based on Sparse Dictionary Meta-Learning
by Zuo Jiang, Yuan Wang and Yi Tang
Mathematics 2024, 12(19), 2992; https://doi.org/10.3390/math12192992 - 26 Sep 2024
Viewed by 1219
Abstract
In the field of Meta-Learning, traditional methods for addressing few-shot learning problems often rely on leveraging prior knowledge for rapid adaptation. However, when faced with insufficient data, meta-learning models frequently encounter challenges such as overfitting and limited feature extraction capabilities. To overcome these [...] Read more.
In the field of Meta-Learning, traditional methods for addressing few-shot learning problems often rely on leveraging prior knowledge for rapid adaptation. However, when faced with insufficient data, meta-learning models frequently encounter challenges such as overfitting and limited feature extraction capabilities. To overcome these challenges, an innovative meta-learning approach based on Sparse Dictionary and Consistency Learning (SDCL) is proposed. The distinctive feature of SDCL is the integration of sparse representation and consistency regularization, designed to acquire both broadly applicable general knowledge and task-specific meta-knowledge. Through sparse dictionary learning, SDCL constructs compact and efficient models, enabling the accurate transfer of knowledge from the source domain to the target domain, thereby enhancing the effectiveness of knowledge transfer. Simultaneously, consistency regularization generates synthetic data similar to existing samples, expanding the training dataset and alleviating data scarcity issues. The core advantage of SDCL lies in its ability to preserve key features while ensuring stronger generalization and robustness. Experimental results demonstrate that the proposed meta-learning algorithm significantly improves model performance under limited training data conditions, particularly excelling in complex cross-domain tasks. On average, the algorithm improves accuracy by 3%. Full article
Show Figures

Figure 1

24 pages, 3548 KiB  
Article
Adapting CLIP for Action Recognition via Dual Semantic Supervision and Temporal Prompt Reparameterization
by Lujuan Deng, Jieqing Tan and Fangmei Liu
Electronics 2024, 13(16), 3348; https://doi.org/10.3390/electronics13163348 - 22 Aug 2024
Viewed by 1612
Abstract
The contrastive vision–language pre-trained model CLIP, driven by large-scale open-vocabulary image–text pairs, has recently demonstrated remarkable zero-shot generalization capabilities in diverse downstream image tasks, which has made numerous models dominated by the “image pre-training followed by fine-tuning” paradigm exhibit promising results on standard [...] Read more.
The contrastive vision–language pre-trained model CLIP, driven by large-scale open-vocabulary image–text pairs, has recently demonstrated remarkable zero-shot generalization capabilities in diverse downstream image tasks, which has made numerous models dominated by the “image pre-training followed by fine-tuning” paradigm exhibit promising results on standard video benchmarks. However, as models scale up, full fine-tuning adaptive strategy for specific tasks becomes difficult in terms of training and storage. In this work, we propose a novel method that adapts CLIP to the video domain for efficient recognition without destroying the original pre-trained parameters. Specifically, we introduce temporal prompts to realize the object of reasoning about the dynamic content of videos for pre-trained models that lack temporal cues. Then, by replacing the direct learning style of prompt vectors with a lightweight reparameterization encoder, the model can be adapted to domain-specific adjustment to learn more generalizable representations. Furthermore, we predefine a Chinese label dictionary to enhance video representation by co-supervision of Chinese and English semantics. Extensive experiments on video action recognition benchmarks show that our method achieves competitive or even better performance than most existing methods with fewer trainable parameters in both general and few-shot recognition scenarios. Full article
Show Figures

Figure 1

15 pages, 2921 KiB  
Article
Dynamic User Tourism Interest Modeling through Domain Information Integration: A Hierarchical Approach
by Hiroyoshi Todo, Xiliang Zhang, Zhongguo Zhang and Yuki Todo
Electronics 2024, 13(15), 2970; https://doi.org/10.3390/electronics13152970 - 27 Jul 2024
Cited by 1 | Viewed by 1259
Abstract
With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods [...] Read more.
With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to constructing a user tourism interest model. Unlike existing methods, which primarily rely on flat or simplistic representations of user data, our approach leverages the hierarchical structure inherent in tourism domain information modeling. By harnessing big data within the tourism domain, we construct hierarchical tourism attributes and apply a conditional random field model along with an affective dictionary to facilitate the hierarchical mining of user travel interest information. This culminates in the establishment of a comprehensive user travel interest model using advanced information modeling techniques. Building upon this foundation, we further propose a dynamic user travel interest model, showcasing its adaptability and responsiveness to changing user preferences. Finally, we validate the accuracy and effectiveness of our model through simulation experiments within a user travel recommendation system, demonstrating significant improvements over traditional methods. Full article
Show Figures

Figure 1

28 pages, 6703 KiB  
Article
An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework
by Kun Liu, Tong Wang and Weijun Huang
Remote Sens. 2024, 16(14), 2534; https://doi.org/10.3390/rs16142534 - 10 Jul 2024
Cited by 2 | Viewed by 1234
Abstract
The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars [...] Read more.
The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method. Full article
Show Figures

Graphical abstract

20 pages, 24161 KiB  
Article
Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach
by Jing Lu, Jingjun Jiang and Yidan Bai
Mathematics 2024, 12(14), 2162; https://doi.org/10.3390/math12142162 - 10 Jul 2024
Cited by 2 | Viewed by 2414
Abstract
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the [...] Read more.
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning with Applications, 2nd Edition)
Show Figures

Figure 1

23 pages, 4446 KiB  
Article
Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery
by Shiyuan Zhang, Xingyu Lu, Ke Tan, Huabin Yan, Jianchao Yang, Zheng Dai and Hong Gu
Remote Sens. 2024, 16(13), 2325; https://doi.org/10.3390/rs16132325 - 26 Jun 2024
Cited by 3 | Viewed by 1555
Abstract
The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, [...] Read more.
The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results. Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications)
Show Figures

Figure 1

26 pages, 8482 KiB  
Article
Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection
by Lifeng Yang, Xiaorui Song, Bin Bai and Zhuo Chen
Remote Sens. 2024, 16(12), 2245; https://doi.org/10.3390/rs16122245 - 20 Jun 2024
Viewed by 1360
Abstract
Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual [...] Read more.
Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
Show Figures

Graphical abstract

16 pages, 3656 KiB  
Article
Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction
by Zhiqi Gao, Wei Deng, Pingping Huang, Wei Xu and Weixian Tan
Electronics 2024, 13(11), 2187; https://doi.org/10.3390/electronics13112187 - 4 Jun 2024
Cited by 1 | Viewed by 1025
Abstract
Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary [...] Read more.
Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance. Full article
Show Figures

Figure 1

25 pages, 25911 KiB  
Article
Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary
by Xi Cheng, Ruiqi Mu, Sheng Lin, Min Zhang and Hai Wang
Remote Sens. 2024, 16(11), 1837; https://doi.org/10.3390/rs16111837 - 21 May 2024
Cited by 6 | Viewed by 2260
Abstract
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a [...] Read more.
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method. Full article
Show Figures

Graphical abstract

Back to TopTop