Pub Date : 2025-01-16DOI: 10.1016/j.dsp.2025.105000
Wenbo Zhang, Yuhang Yang, Shenmin Song
The weighted maximum correntropy extended Kalman filtering (WMC-EKF) problem is addressed in this article for a class of stochastic nonlinear systems under cyber attacks, considering the noises are non-Gaussian of system and measurement. A measurement model is established to characterize both denial-of-service (DoS) attacks and false data injection (FDI) attacks, where the false data has a multiplicative effect on the original measurement. Both deterministic and stochastic nonlinear functions are taken into account. Since the standard Kalman filter only utilizes second-order signal information, it may not be optimal in non-Gaussian environments. By leveraging the advantages of correntropy in handling non-Gaussian signals, formulas for calculating the filter gains and upper bound of the filter error covariance are derived using the weighted maximum correntropy criterion, Taylor series expansion, and fixed-point iterative update rule. Finally, two numerical simulations demonstrate the effectiveness of WMC-EKF under hybrid cyber attacks with non-Gaussian process and measurement noises.
{"title":"Maximum correntropy EKF for stochastic nonlinear systems under measurement model with multiplicative false data cyber attacks and non-Gaussian noises","authors":"Wenbo Zhang, Yuhang Yang, Shenmin Song","doi":"10.1016/j.dsp.2025.105000","DOIUrl":"10.1016/j.dsp.2025.105000","url":null,"abstract":"<div><div>The weighted maximum correntropy extended Kalman filtering (WMC-EKF) problem is addressed in this article for a class of stochastic nonlinear systems under cyber attacks, considering the noises are non-Gaussian of system and measurement. A measurement model is established to characterize both denial-of-service (DoS) attacks and false data injection (FDI) attacks, where the false data has a multiplicative effect on the original measurement. Both deterministic and stochastic nonlinear functions are taken into account. Since the standard Kalman filter only utilizes second-order signal information, it may not be optimal in non-Gaussian environments. By leveraging the advantages of correntropy in handling non-Gaussian signals, formulas for calculating the filter gains and upper bound of the filter error covariance are derived using the weighted maximum correntropy criterion, Taylor series expansion, and fixed-point iterative update rule. Finally, two numerical simulations demonstrate the effectiveness of WMC-EKF under hybrid cyber attacks with non-Gaussian process and measurement noises.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105000"},"PeriodicalIF":2.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.dsp.2025.104999
Mohammad Mahdizadeh, Shijie Chen, Peng Ye
Low-light image enhancement aims to improve the visibility and quality of images taken under dim lighting conditions. Most existing approaches in this field often encounter challenges such as low or excessive enhancement, and exaggerated light sources in real nighttime scenes. To address these issues, we present a comprehensive and modular approach that combines several key elements. First, we employ a novel variation of self-supervised retinex-based network to achieve effective enlightening. Second, we utilize an adaptive light source-aware enlightenment module to consider the presence of light sources. Then, our illumination-aware exposure-balanced fusion module integrates the outputs of the two stages. This method significantly improves the quality of low-light and nighttime images by balancing exposure, contrast, and saturation, producing well-exposed results. Comprehensive experiments on two referenced datasets (LOL and EnlightenGAN) and two non-referenced datasets (LIME and ExDark) validate the effectiveness of our approach. Our approach consistently achieves balanced exposure and preserves natural color tones, as reflected in key metrics. Specifically, it demonstrates an average improvement of 8.96% in FID score and 4.43% in LPIPS score for referenced datasets, along with a 0.1186 enhancement in NIQE score for non-referenced datasets. The code and implementation instructions are available at https://github.com/PaulMahdizadeh123/LowLightEnh.
{"title":"Illuminating the night: A light source-aware and exposure-balanced low-light enhancement approach for real nighttime scenes","authors":"Mohammad Mahdizadeh, Shijie Chen, Peng Ye","doi":"10.1016/j.dsp.2025.104999","DOIUrl":"10.1016/j.dsp.2025.104999","url":null,"abstract":"<div><div>Low-light image enhancement aims to improve the visibility and quality of images taken under dim lighting conditions. Most existing approaches in this field often encounter challenges such as low or excessive enhancement, and exaggerated light sources in real nighttime scenes. To address these issues, we present a comprehensive and modular approach that combines several key elements. First, we employ a novel variation of self-supervised retinex-based network to achieve effective enlightening. Second, we utilize an adaptive light source-aware enlightenment module to consider the presence of light sources. Then, our illumination-aware exposure-balanced fusion module integrates the outputs of the two stages. This method significantly improves the quality of low-light and nighttime images by balancing exposure, contrast, and saturation, producing well-exposed results. Comprehensive experiments on two referenced datasets (LOL and EnlightenGAN) and two non-referenced datasets (LIME and ExDark) validate the effectiveness of our approach. Our approach consistently achieves balanced exposure and preserves natural color tones, as reflected in key metrics. Specifically, it demonstrates an average improvement of 8.96% in FID score and 4.43% in LPIPS score for referenced datasets, along with a 0.1186 enhancement in NIQE score for non-referenced datasets. The code and implementation instructions are available at <span><span>https://github.com/PaulMahdizadeh123/LowLightEnh</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104999"},"PeriodicalIF":2.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.dsp.2025.105002
Jianhong Xiang , Tianyi Song , Wei Liu
In recent years, significant progress has been made in image compression sensing (ICS) through deep learning techniques. Deep Unfolding Networks (DUN) transforms the iterative reconfiguration process into an end-to-end deep neural network, improving interpretability and performance. However, traditional algorithms are limited to processing information in pixel space, missing the potential advantages of feature space. Additionally, most DUN are constrained by fixed input-output mirror structures that restrict information flow and lack adaptability due to their use of a fixed threshold for soft shrinkage operations. To address these limitations, we propose a novel feature space-based compression-aware adaptive threshold network (FNAT-Net). The supplementary information (FI) is utilized to enable FNAT-Net to perform fusion processing across both the pixel and feature domains, mapping a two-step approximate gradient descent algorithm from pixel to feature space. Furthermore, this paper introduces an effective enhanced Multi-Layer Perceptron (MLP) adaptive soft-thresholding strategy. This strategy enables FNAT-Net to address L1-regularized neighbourhood mappings with content-aware thresholds. FNAT-Net outperforms state-of-the-art methods, demonstrating superior performance across a wide range of scene changes and noise conditions.
{"title":"FNAT-Net: Feature space-based compression-aware adaptive thresholding network","authors":"Jianhong Xiang , Tianyi Song , Wei Liu","doi":"10.1016/j.dsp.2025.105002","DOIUrl":"10.1016/j.dsp.2025.105002","url":null,"abstract":"<div><div>In recent years, significant progress has been made in image compression sensing (ICS) through deep learning techniques. Deep Unfolding Networks (DUN) transforms the iterative reconfiguration process into an end-to-end deep neural network, improving interpretability and performance. However, traditional algorithms are limited to processing information in pixel space, missing the potential advantages of feature space. Additionally, most DUN are constrained by fixed input-output mirror structures that restrict information flow and lack adaptability due to their use of a fixed threshold for soft shrinkage operations. To address these limitations, we propose a novel feature space-based compression-aware adaptive threshold network (FNAT-Net). The supplementary information (FI) is utilized to enable FNAT-Net to perform fusion processing across both the pixel and feature domains, mapping a two-step approximate gradient descent algorithm from pixel to feature space. Furthermore, this paper introduces an effective enhanced Multi-Layer Perceptron (MLP) adaptive soft-thresholding strategy. This strategy enables FNAT-Net to address L1-regularized neighbourhood mappings with content-aware thresholds. FNAT-Net outperforms state-of-the-art methods, demonstrating superior performance across a wide range of scene changes and noise conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105002"},"PeriodicalIF":2.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.dsp.2025.105001
Yan Wang , Guohong Gao , Chenping Zhao , Xixi Jia , Jianping Wang , Shousheng Luo , Zhiyu Li
Images captured in dark conditions unavoidably suffer from poor visibility issues. Numerous methods addressing these challenges are developed based on the Retinex theory, which decomposes an observed image into illumination and reflection maps, promoting refined processing to enhance image quality. However, most of such methods treat the illumination and reflection components separately, without considering their informational interaction. The proposed method reinforces the collaboration of illumination and reflection with a joint enhancement network named JIRE-Net. We first utilize the powerful feature extraction capability of the convolutional neural network (CNN) to construct a decomposition network. Subsequently, we elaborately designed an Illumination-Driven Transformer-based network structure to reconstruct the normal-light image. Specifically, the Channel Attention Module (IB-CAM) is formulated to promote the features in reflection, which utilize the information of attention weights calculated based on the illumination map. Thereafter, the Illumination-Driven Guidance Block (IDGB) is designed to capture dependencies across input features, cooperatively enhancing the reflection and illumination features. The experimental results on the existing benchmark datasets show that our method obtains better quantitative and qualitative results, achieving a more balanced overall brightness appearance and color quality while preserving finer texture and structural details.
{"title":"JIRE-Net: Low-light image enhancement with joint enhancement network of illumination and reflection maps","authors":"Yan Wang , Guohong Gao , Chenping Zhao , Xixi Jia , Jianping Wang , Shousheng Luo , Zhiyu Li","doi":"10.1016/j.dsp.2025.105001","DOIUrl":"10.1016/j.dsp.2025.105001","url":null,"abstract":"<div><div>Images captured in dark conditions unavoidably suffer from poor visibility issues. Numerous methods addressing these challenges are developed based on the Retinex theory, which decomposes an observed image into illumination and reflection maps, promoting refined processing to enhance image quality. However, most of such methods treat the illumination and reflection components separately, without considering their informational interaction. The proposed method reinforces the collaboration of illumination and reflection with a joint enhancement network named JIRE-Net. We first utilize the powerful feature extraction capability of the convolutional neural network (CNN) to construct a decomposition network. Subsequently, we elaborately designed an Illumination-Driven Transformer-based network structure to reconstruct the normal-light image. Specifically, the Channel Attention Module (IB-CAM) is formulated to promote the features in reflection, which utilize the information of attention weights calculated based on the illumination map. Thereafter, the Illumination-Driven Guidance Block (IDGB) is designed to capture dependencies across input features, cooperatively enhancing the reflection and illumination features. The experimental results on the existing benchmark datasets show that our method obtains better quantitative and qualitative results, achieving a more balanced overall brightness appearance and color quality while preserving finer texture and structural details.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105001"},"PeriodicalIF":2.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Directed acyclic graphs (DAGs) are used for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this setting because the eigendecomposition of the adjacency matrix yields all eigenvalues equal to zero. This inherent property of DAGs results in an inability to differentiate between frequency components of signals on such graphs. This problem can be addressed by alternating the Fourier basis or adding edges in a DAG. However, these approaches change the physics of the considered problem. To address this limitation, we propose a graph zero-padding approach. This approach involves augmenting the original DAG with additional vertices that are connected to the existing structure. The added vertices are characterized by signal values set to zero. The proposed technique enables the spectral evaluation of system outputs on DAGs (in almost all cases), that is the computation of vertex-domain convolution without the adverse effects of aliasing due to changes in a graph structure, with the ultimate goal of preserving the output of the system on a graph as if the changes in the graph structure were not performed.
{"title":"Fourier analysis of signals on directed acyclic graphs (DAG) using graph zero-padding","authors":"Ljubiša Stanković , Miloš Daković , Ali Bagheri Bardi , Miloš Brajović , Isidora Stanković","doi":"10.1016/j.dsp.2025.104995","DOIUrl":"10.1016/j.dsp.2025.104995","url":null,"abstract":"<div><div>Directed acyclic graphs (DAGs) are used for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this setting because the eigendecomposition of the adjacency matrix yields all eigenvalues equal to zero. This inherent property of DAGs results in an inability to differentiate between frequency components of signals on such graphs. This problem can be addressed by alternating the Fourier basis or adding edges in a DAG. However, these approaches change the physics of the considered problem. To address this limitation, we propose a <em>graph zero-padding</em> approach. This approach involves augmenting the original DAG with additional vertices that are connected to the existing structure. The added vertices are characterized by signal values set to zero. The proposed technique enables the spectral evaluation of system outputs on DAGs (in almost all cases), that is the computation of vertex-domain convolution without the adverse effects of aliasing due to changes in a graph structure, with the ultimate goal of preserving the output of the system on a graph as if the changes in the graph structure were not performed.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104995"},"PeriodicalIF":2.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1016/j.dsp.2025.105003
Zaiyu Pan, Jiameng Xu, Shuangtian Jiang, Jun Wang
Currently, multimodal biometric recognition is one of the safest identity recognition technologies. Most existing multimodal biometric recognition algorithms require test samples with complete multimodal data. However, in practical scenarios, it is difficult to obtain complete multimodal biometric data due to factors such as collection equipment and environment. To address this problem, we proposed a Shared-Specific Feature Disentanglement Network (SSFD-Net) for palmprint and palmvein based multimodal biometric recognition with missing modality. Firstly, the multimodal shared-specific feature disentanglement network with inter-modality triplet loss and inter-modality identity consistency loss is proposed to split each modality into shared and specific features. Secondly, the cross-modal feature transformation module is designed to establish the correlation of specific features for different modalities. Subsequently, the features of missing modality are reconstructed by fusing the shared features from available modalities and specific features generated by the cross-modal feature transformation module. Besides, the intra-modality identity consistency loss is presented to enhance the discriminative ability of reconstructed features of missing modality. Experimental results demonstrate that our proposed model outperforms state-of-the-art incomplete multimodal learning models on three multimodal biometric benchmark datasets.
{"title":"SSFD-Net: Shared-Specific Feature Disentanglement Network for Multimodal Biometric Recognition with Missing Modality","authors":"Zaiyu Pan, Jiameng Xu, Shuangtian Jiang, Jun Wang","doi":"10.1016/j.dsp.2025.105003","DOIUrl":"10.1016/j.dsp.2025.105003","url":null,"abstract":"<div><div>Currently, multimodal biometric recognition is one of the safest identity recognition technologies. Most existing multimodal biometric recognition algorithms require test samples with complete multimodal data. However, in practical scenarios, it is difficult to obtain complete multimodal biometric data due to factors such as collection equipment and environment. To address this problem, we proposed a Shared-Specific Feature Disentanglement Network (SSFD-Net) for palmprint and palmvein based multimodal biometric recognition with missing modality. Firstly, the multimodal shared-specific feature disentanglement network with inter-modality triplet loss and inter-modality identity consistency loss is proposed to split each modality into shared and specific features. Secondly, the cross-modal feature transformation module is designed to establish the correlation of specific features for different modalities. Subsequently, the features of missing modality are reconstructed by fusing the shared features from available modalities and specific features generated by the cross-modal feature transformation module. Besides, the intra-modality identity consistency loss is presented to enhance the discriminative ability of reconstructed features of missing modality. Experimental results demonstrate that our proposed model outperforms state-of-the-art incomplete multimodal learning models on three multimodal biometric benchmark datasets.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 105003"},"PeriodicalIF":2.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1016/j.dsp.2025.104996
Shangpo Zheng , Liu Junfeng , Jun Zeng
Multispectral object detection techniques integrate data from various spectral modalities, such as combining thermal images with RGB visible light images, to enhance the precision a-nd robustness of object detection under diverse environmental c-onditions. Although this approach has improved detection capab-ilities, significant challenges remain in fully leveraging the specif-ic detail information of each single modality and accurately capt-uring cross-modality shared features information. To address th-ese challenges, we propose a Multiscale Cross-modality Adaptive Fusion Network (MCAFNet). This network incorporates Cross- modality interactive Transformer (CMIT) module, Multimodal Adaptive Weighted Fusion (MAWF) module, and a 3D-Integrated Attention Feature Enhancement (3D-IAFE) module. These components work together to comprehensively extract complementary feature between modalities and specific detailed feature within each modality, thereby enhancing the accuracy and robustness of multimodal object detection. Extensive experimental validation and in-depth ablation studies confirm the effectiveness of the proposed method, achieving state-of-the-art detection performance on multiple public datasets.
{"title":"MCAFNet: Multiscale cross-modality adaptive fusion network for multispectral object detection","authors":"Shangpo Zheng , Liu Junfeng , Jun Zeng","doi":"10.1016/j.dsp.2025.104996","DOIUrl":"10.1016/j.dsp.2025.104996","url":null,"abstract":"<div><div>Multispectral object detection techniques integrate data from various spectral modalities, such as combining thermal images with RGB visible light images, to enhance the precision a-nd robustness of object detection under diverse environmental c-onditions. Although this approach has improved detection capab-ilities, significant challenges remain in fully leveraging the specif-ic detail information of each single modality and accurately capt-uring cross-modality shared features information. To address th-ese challenges, we propose a Multiscale Cross-modality Adaptive Fusion Network (MCAFNet). This network incorporates Cross- modality interactive Transformer (CMIT) module, Multimodal Adaptive Weighted Fusion (MAWF) module, and a 3D-Integrated Attention Feature Enhancement (3D-IAFE) module. These components work together to comprehensively extract complementary feature between modalities and specific detailed feature within each modality, thereby enhancing the accuracy and robustness of multimodal object detection. Extensive experimental validation and in-depth ablation studies confirm the effectiveness of the proposed method, achieving state-of-the-art detection performance on multiple public datasets.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104996"},"PeriodicalIF":2.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1016/j.dsp.2025.104997
Hong Wang , Hongyu Han , Sheng Zhang , Jinhua Ku
To effectively reduce the kernel conjugate gradient (KCG) algorithm's network size, this paper proposes an improved algorithm based on an adaptive alternating filtering mechanism (AAFM) called AAFM-KCG. The algorithm utilizes a clustering sparse strategy and the orthogonality of nearest instance centroid estimate subspaces to decompose the complex KCG filter into multiple nearly independent sub-filters. By alternately activating only the most relevant sub-filters for updates, it significantly reduces computational complexity and storage requirements while ensuring high filtering accuracy. Then, to establish a fixed-scale network structure, the random Fourier feature (RFF) technique is integrated, yielding the AAFM-RFFCG algorithm. Furthermore, for scenarios with non-Gaussian noise interference, we introduce a truncated generalized exponential hyperbolic tangent (TGEHT) function and embed it into the AAFM framework, refined into the T-AAFM-KCG and T-AAFM-RFFCG algorithms. The simulation results demonstrate that the proposed algorithm achieves excellent computational efficiency and noise robustness in Lorenz chaotic time series prediction, nonlinear system identification, and sunspots time series prediction tasks.
{"title":"An efficient kernel adaptive filtering algorithm with adaptive alternating filtering mechanism","authors":"Hong Wang , Hongyu Han , Sheng Zhang , Jinhua Ku","doi":"10.1016/j.dsp.2025.104997","DOIUrl":"10.1016/j.dsp.2025.104997","url":null,"abstract":"<div><div>To effectively reduce the kernel conjugate gradient (KCG) algorithm's network size, this paper proposes an improved algorithm based on an adaptive alternating filtering mechanism (AAFM) called AAFM-KCG. The algorithm utilizes a clustering sparse strategy and the orthogonality of nearest instance centroid estimate subspaces to decompose the complex KCG filter into multiple nearly independent sub-filters. By alternately activating only the most relevant sub-filters for updates, it significantly reduces computational complexity and storage requirements while ensuring high filtering accuracy. Then, to establish a fixed-scale network structure, the random Fourier feature (RFF) technique is integrated, yielding the AAFM-RFFCG algorithm. Furthermore, for scenarios with non-Gaussian noise interference, we introduce a truncated generalized exponential hyperbolic tangent (TGEHT) function and embed it into the AAFM framework, refined into the T-AAFM-KCG and T-AAFM-RFFCG algorithms. The simulation results demonstrate that the proposed algorithm achieves excellent computational efficiency and noise robustness in Lorenz chaotic time series prediction, nonlinear system identification, and sunspots time series prediction tasks.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104997"},"PeriodicalIF":2.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1016/j.dsp.2025.104989
Seong-Heon Seo
Interpolated discrete Fourier transform (IpDFT) algorithms have been improved for a long time to compensate for frequency estimation bias due to quantization errors in digital signal processing. In this paper, a new interpolation algorithm, named synchro interpolation transform (SIT), is developed based on Morlet continuous wavelet transform (CWT). In Morlet CWT, approximately integer periods of sinusoid are contained within the wavelet. Therefore, the spectral leakage is much smaller than that of the DFT, so a simple interpolation algorithm using only two CWT coefficients can estimate the frequency very accurately. In addition, DFT is only suitable for the frequency analysis of stationary signals. When the frequency varies in time, time-frequency representations (TFRs) such as short time Fourier transform or CWT should be used to estimate instantaneous frequency (IF) of non-stationary signals. The accuracy of the IF measurement of a nonlinear chirp signal depends on the width of the window function used in the TFR. Instead of trying to optimize the window width to get more accurate frequencies, SIT calculates multiple spectrograms as varying the window width, and then interpolates those multiple frequencies estimated from each spectrogram to get the correct IF of the nonlinear chirp signal. In principle, SIT can measure the exact IF for any order nonlinear frequency chirp signals. The performance of SIT is investigated by analyzing simulated signals and bat sounds. A new algorithm, named iterative TFR, is developed to remove interference of multicomponent signals. Multicomponent signals are successfully analyzed by combining iterative TFR and SIT.
{"title":"Instantaneous frequency estimation by interpolating continuous wavelet transform coefficients","authors":"Seong-Heon Seo","doi":"10.1016/j.dsp.2025.104989","DOIUrl":"10.1016/j.dsp.2025.104989","url":null,"abstract":"<div><div>Interpolated discrete Fourier transform (IpDFT) algorithms have been improved for a long time to compensate for frequency estimation bias due to quantization errors in digital signal processing. In this paper, a new interpolation algorithm, named synchro interpolation transform (SIT), is developed based on Morlet continuous wavelet transform (CWT). In Morlet CWT, approximately integer periods of sinusoid are contained within the wavelet. Therefore, the spectral leakage is much smaller than that of the DFT, so a simple interpolation algorithm using only two CWT coefficients can estimate the frequency very accurately. In addition, DFT is only suitable for the frequency analysis of stationary signals. When the frequency varies in time, time-frequency representations (TFRs) such as short time Fourier transform or CWT should be used to estimate instantaneous frequency (IF) of non-stationary signals. The accuracy of the IF measurement of a nonlinear chirp signal depends on the width of the window function used in the TFR. Instead of trying to optimize the window width to get more accurate frequencies, SIT calculates multiple spectrograms as varying the window width, and then interpolates those multiple frequencies estimated from each spectrogram to get the correct IF of the nonlinear chirp signal. In principle, SIT can measure the exact IF for any order nonlinear frequency chirp signals. The performance of SIT is investigated by analyzing simulated signals and bat sounds. A new algorithm, named iterative TFR, is developed to remove interference of multicomponent signals. Multicomponent signals are successfully analyzed by combining iterative TFR and SIT.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104989"},"PeriodicalIF":2.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.dsp.2025.104991
Liyang Sun , Lin Xu , Xue Dong , Muhammad Usman Shoukat , Jia Mi
A novel algorithm, the maneuvering first-order generalized pseudo-Bayesian error-state Kalman filter (MGPB1-ESKF), is proposed in this study for localization in advanced driver assistance system (ADAS) testing platform vehicles. The proposed algorithm substantially enhances the reliability and accuracy of multi-sensor localization in ADAS testing. This improvement is particularly significant in challenging non-line-of-sight (NLOS) environments, which typically degrade the performance of global navigation satellite system (GNSS). By adaptively identifying and isolating faulty signal sources, the MGPB1-ESKF provides a novel approach to achieving robust and reliable localization in the presence of transient sensor failures. Rigorous simulations and experimental results demonstrate that the proposed algorithm effectively mitigates the impact of faulty sensors in challenging environments, outperforming conventional multiple model (MM) algorithm and leading to improved localization accuracy.
{"title":"A maneuvering multi-sensor information fusion algorithm for enhancing localization reliability in ADAS testing","authors":"Liyang Sun , Lin Xu , Xue Dong , Muhammad Usman Shoukat , Jia Mi","doi":"10.1016/j.dsp.2025.104991","DOIUrl":"10.1016/j.dsp.2025.104991","url":null,"abstract":"<div><div>A novel algorithm, the maneuvering first-order generalized pseudo-Bayesian error-state Kalman filter (MGPB1-ESKF), is proposed in this study for localization in advanced driver assistance system (ADAS) testing platform vehicles. The proposed algorithm substantially enhances the reliability and accuracy of multi-sensor localization in ADAS testing. This improvement is particularly significant in challenging non-line-of-sight (NLOS) environments, which typically degrade the performance of global navigation satellite system (GNSS). By adaptively identifying and isolating faulty signal sources, the MGPB1-ESKF provides a novel approach to achieving robust and reliable localization in the presence of transient sensor failures. Rigorous simulations and experimental results demonstrate that the proposed algorithm effectively mitigates the impact of faulty sensors in challenging environments, outperforming conventional multiple model (MM) algorithm and leading to improved localization accuracy.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104991"},"PeriodicalIF":2.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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