Digital Signal Processing最新文献

英文中文

Main lobe interrupted sampling repeater jamming suppression based on parameter estimation and channel cancellation

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-13 DOI: 10.1016/j.dsp.2025.104985

Zhenhua Liu , Wei Liang , Ning Fu , Liyan Qiao , Jun Zhang

The main lobe interrupted sampling repeater jamming (ISRJ), being coherent with the transmitted signal, is capable of generating both deception and suppression jamming effects. These effects severely limit the capabilities of monopulse radar to detect, track, and identify targets. Furthermore, since the jammer is located within the main lobe of the radar's transmit beam, spatial filtering techniques will cause a serious loss of target energy. This study focuses on the research on main lobe ISRJ suppression for monopulse radar. An anti-jamming scheme based on jamming parameter estimation and channel cancellation is proposed. Firstly, time-frequency (TF) analysis is performed on the received echo to estimate the ISRJ parameters using the distribution characteristics of TF energy. Subsequently, the time unit where only ISRJ signal exists is determined. A function, referred to as the sum-difference channel ratio, is constructed, and its value in the time unit where only ISRJ exists serves as the cancellation coefficient. By applying sum-difference channel cancellation, the jamming in radar's received echo is suppressed while retaining the real target simultaneously. Through numerical simulations, we have validated the effectiveness of the proposed method and conducted a thorough analysis of how different parameters affect its performance. Compared to state-of-the-art methods, the average improvement in the signal-to-jamming-plus-noise ratio improvement factor achieved by our method is approximately 6.4 dB higher.

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引用次数: 0

Infrared small target detection via contrast-enhanced dual-branch network

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-13 DOI: 10.1016/j.dsp.2025.104988

Bolin Xiao, Wenjun Zhou, Tianfei Wang, Quan Zhang, Bo Peng

Infrared small target detection faces challenges including small target size, low contrast, and random image distribution. To address these, this paper presents an innovative approach named Dual-Branch Contrast-Enhanced U-Net (DBCE U-Net). Building on the basic U-Net architecture, DBCE U-Net introduces a dual-branch structure and integrates a novel Contrast Conv Module. The Contrast Conv Module enhances contrast and feature representation via adaptive feature segmentation and convolution operations, while the dual-branch design combines ResNeSt modules for deep feature extraction and feature enhancement modules (RE) for primary visual perception augmentation. In comparison with other methods, DBCE U-Net leverages gradient information to enhance small target features and improves model robustness through a dual-branch structure. Experimental results demonstrate that DBCE U-Net delivers superior detection performance across challenging datasets, particularly on the NUDT-SIRST dataset, with an IoU and Pd of 94.60% and 99.05%, respectively.

引用次数: 0

A novel robust frequency domain widely linear quaternion adaptive filtering algorithm

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-13 DOI: 10.1016/j.dsp.2025.104987

Qianqian Liu , Liulu He

This paper proposes a novel robust frequency-domain widely linear quaternion adaptive filtering algorithm (QGMFD) based on the hyperbolic tangent Geman-McClure function, which is designed to remove outliers from the dataset within the hyperbolic tangent framework. The algorithm significantly reduces the interference of impulsive noise on the system and effectively addresses the performance degradation of traditional frequency-domain widely linear quaternion adaptive filters (FDAF) when processing colored input signals in an impulsive noise environment. Additionally, a theoretical analysis of the proposed QGMFD is provided, and its computational complexity is compared with that of other algorithms. Finally, the simulation results for system identification and prediction demonstrate that the proposed QGMFD outperforms other algorithms in processing colored signals under impulsive noise.

引用次数: 0

A momentum-based stochastic fractional gradient optimizer with U-net model for brain tumor segmentation in MRI

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-10 DOI: 10.1016/j.dsp.2025.104983

Anjali Malik, Ganesh Gopal Devarajan

Brain tumor segmentation from magnetic resonance imaging (MRI) is a critical task in medical image analysis and is essential for diagnosis, treatment planning, and monitoring. This study presents a new approach leveraging the U-Net architecture combined with a newly proposed Stochastic Fractional Moment Gradient Descent (SFM) optimizer. This proposed new hybris combination addresses the issues of convergence speed and segmentation precision. The proposed SFM optimizer introduces a fractional gradient component that provides a more refined update mechanism compared to traditional gradient descent, incorporating momentum to accelerate convergence and avoid local minima. The model was trained and validated on the multiple brain tumor segmentation datasets. The experimental results demonstrate that the U-Net with SFM optimizer outperforms conventional U-Net models using standard optimizers such as Adam and SGD. Specifically, our approach achieved a Dice Similarity Coefficient (DSC) of 0.88, surpassing the baseline U-Net model's DSC of 0.84. Additionally, our method showed a 20% improvement in convergence speed, reducing training time significantly while maintaining high accuracy. Qualitative analysis of segmentation outputs also confirmed that our model effectively delineates tumor boundaries with higher precision, particularly in challenging cases with heterogeneous tumor appearances. These results suggest that the integration of the SFM optimizer with the U-Net architecture provides a robust framework for accurate and efficient brain tumor segmentation in MRI, with potential applications in clinical practice.

{"title":"A momentum-based stochastic fractional gradient optimizer with U-net model for brain tumor segmentation in MRI","authors":"Anjali Malik, Ganesh Gopal Devarajan","doi":"10.1016/j.dsp.2025.104983","DOIUrl":"10.1016/j.dsp.2025.104983","url":null,"abstract":"<div><div>Brain tumor segmentation from magnetic resonance imaging (MRI) is a critical task in medical image analysis and is essential for diagnosis, treatment planning, and monitoring. This study presents a new approach leveraging the U-Net architecture combined with a newly proposed Stochastic Fractional Moment Gradient Descent (SFM) optimizer. This proposed new hybris combination addresses the issues of convergence speed and segmentation precision. The proposed SFM optimizer introduces a fractional gradient component that provides a more refined update mechanism compared to traditional gradient descent, incorporating momentum to accelerate convergence and avoid local minima. The model was trained and validated on the multiple brain tumor segmentation datasets. The experimental results demonstrate that the U-Net with SFM optimizer outperforms conventional U-Net models using standard optimizers such as Adam and SGD. Specifically, our approach achieved a Dice Similarity Coefficient (DSC) of 0.88, surpassing the baseline U-Net model's DSC of 0.84. Additionally, our method showed a 20% improvement in convergence speed, reducing training time significantly while maintaining high accuracy. Qualitative analysis of segmentation outputs also confirmed that our model effectively delineates tumor boundaries with higher precision, particularly in challenging cases with heterogeneous tumor appearances. These results suggest that the integration of the SFM optimizer with the U-Net architecture provides a robust framework for accurate and efficient brain tumor segmentation in MRI, with potential applications in clinical practice.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104983"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143697","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}

引用次数: 0

Adaptive splitting mean online expectation-maximization method-based moving object localization

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-10 DOI: 10.1016/j.dsp.2025.104980

Chee-Hyun Park, Joon-Hyuk Chang

This paper introduces positioning techniques for estimating the location of an emitter using range data affected by outliers. In indoor and densely populated metropolitan environments, the presence of non-line-of-sight (NLOS) signals can significantly degrade estimation performance. To mitigate the adverse effects of NLOS signals, robust localization methods are employed. The proposed technique, referred to as the splitting mean (SM) online expectation-maximization (EM)-based two-step weighted least squares (TSWLS) method, is developed from a Bayesian perspective, specifically utilizing the linear minimum mean squared error (LMMSE) criterion. A key element influencing the performance of the SM algorithm is the smoothing factor. Unlike traditional SM methods that use a fixed smoothing factor, the proposed adaptive splitting mean (ASM) bias estimation method dynamically adjusts this factor. Additionally, a theoretical analysis of the mean squared error (MSE) for the proposed measurement bias estimation algorithms is conducted, demonstrating close alignment with simulation results. Simulations further reveal that the proposed method outperforms existing state-of-the-art techniques in localization accuracy across various NLOS bias distributions, including Gaussian, uniform, and exponential distributions.

{"title":"Adaptive splitting mean online expectation-maximization method-based moving object localization","authors":"Chee-Hyun Park, Joon-Hyuk Chang","doi":"10.1016/j.dsp.2025.104980","DOIUrl":"10.1016/j.dsp.2025.104980","url":null,"abstract":"<div><div>This paper introduces positioning techniques for estimating the location of an emitter using range data affected by outliers. In indoor and densely populated metropolitan environments, the presence of non-line-of-sight (NLOS) signals can significantly degrade estimation performance. To mitigate the adverse effects of NLOS signals, robust localization methods are employed. The proposed technique, referred to as the splitting mean (SM) online expectation-maximization (EM)-based two-step weighted least squares (TSWLS) method, is developed from a Bayesian perspective, specifically utilizing the linear minimum mean squared error (LMMSE) criterion. A key element influencing the performance of the SM algorithm is the smoothing factor. Unlike traditional SM methods that use a fixed smoothing factor, the proposed adaptive splitting mean (ASM) bias estimation method dynamically adjusts this factor. Additionally, a theoretical analysis of the mean squared error (MSE) for the proposed measurement bias estimation algorithms is conducted, demonstrating close alignment with simulation results. Simulations further reveal that the proposed method outperforms existing state-of-the-art techniques in localization accuracy across various NLOS bias distributions, including Gaussian, uniform, and exponential distributions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104980"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143316","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}

引用次数: 0

FDA-based maneuvering target detection with Doppler-spread consideration

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-10 DOI: 10.1016/j.dsp.2025.104990

Mingjie Jia , Bang Huang , Abdul Basit , Wen-Qin Wang

Different from conventional phased array (PA) and multiple-input-multiple-output (MIMO) radars, frequency diverse array (FDA) experiences the additional Doppler-spread and Doppler walk phenomena caused by the coupling among frequency increment, velocity, and acceleration. In this paper, we thoroughly investigate the coherent integration issue for detecting an FDA-based maneuvering target, consisting of range migration, Doppler-spread and Doppler walk phenomena. To address these challenges, the paper presents a novel algorithm. Specifically, we first introduce a new pulse sampling interval into the FDA-based signals to propose the resampling-keystone transform (RKT) stage, which effectively correct range migration and Doppler-spread. After the inter-channel compensation and integration of the resampled signals, the Lv's distribution (LVD) stage is applied to achieve the intra-channel coherent integration of target energy. The proposed algorithm is applicable for both single-target and multi-target scenarios. Finally, several simulation results demonstrate the potential of the proposed algorithm for improved detection performance for FDA radar. Additionally, the results indicate the underlying limitations of frequency increment and acceleration, which is caused by the coupling among frequency increment, acceleration, and quadratic slow time.

{"title":"FDA-based maneuvering target detection with Doppler-spread consideration","authors":"Mingjie Jia , Bang Huang , Abdul Basit , Wen-Qin Wang","doi":"10.1016/j.dsp.2025.104990","DOIUrl":"10.1016/j.dsp.2025.104990","url":null,"abstract":"<div><div>Different from conventional phased array (PA) and multiple-input-multiple-output (MIMO) radars, frequency diverse array (FDA) experiences the additional Doppler-spread and Doppler walk phenomena caused by the coupling among frequency increment, velocity, and acceleration. In this paper, we thoroughly investigate the coherent integration issue for detecting an FDA-based maneuvering target, consisting of range migration, Doppler-spread and Doppler walk phenomena. To address these challenges, the paper presents a novel algorithm. Specifically, we first introduce a new pulse sampling interval into the FDA-based signals to propose the resampling-keystone transform (RKT) stage, which effectively correct range migration and Doppler-spread. After the inter-channel compensation and integration of the resampled signals, the Lv's distribution (LVD) stage is applied to achieve the intra-channel coherent integration of target energy. The proposed algorithm is applicable for both single-target and multi-target scenarios. Finally, several simulation results demonstrate the potential of the proposed algorithm for improved detection performance for FDA radar. Additionally, the results indicate the underlying limitations of frequency increment and acceleration, which is caused by the coupling among frequency increment, acceleration, and quadratic slow time.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104990"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144282","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}

引用次数: 0

Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-09 DOI: 10.1016/j.dsp.2024.104977

Mushtaq Ahmad , Xiaofei Zhang , Farman Ali , Xin Lai

Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.

{"title":"Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method","authors":"Mushtaq Ahmad , Xiaofei Zhang , Farman Ali , Xin Lai","doi":"10.1016/j.dsp.2024.104977","DOIUrl":"10.1016/j.dsp.2024.104977","url":null,"abstract":"<div><div>Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104977"},"PeriodicalIF":2.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144275","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}

引用次数: 0

TDOA-based localization under uniform prior knowledge: Performance bounds and its efficient calculation

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-09 DOI: 10.1016/j.dsp.2025.104981

Iker Sobron , Santiago Mazuelas , Iratxe Landa , Iñaki Eizmendi , Manuel Velez

The emergence of a myriad of location-based services has imposed a key role on wireless localization systems. The accuracy of such systems can be enhanced by using prior information on the target location area, commonly available through a map or wireless system coverage area. In the map-aware localization context, performance limits have been mainly explored for Time-of-Arrival positioning systems. This paper presents performance bounds for Time-Difference-of-Arrival (TDOA) localization using a uniform prior information of the location area. In particular, the paper derives a closed-form approximation of the Ziv-Zakai lower bound (ZZB) and Bayesian Cramer-Rao lower bound (BCRB). The presented bounds are evaluated under different configurations and compared with the maximum a posteriori (MAP) estimator, which incorporates a priori information about the location area, and with the Cramer-Rao lower bound (CRB) and the maximum likelihood (ML) estimator, both without prior information. Numerical results show that the proposed ZZB and BCRB exploit the a priori knowledge to increase the localization accuracy and provide tighter performance lower bounds of a MAP estimator, and are properly matched to the actual limits of practical positioning systems. In addition, the proposed closed-form ZZB approximation allows us to avoid numerical evaluation of integrals needed to compute BCRB and exact ZZB, while maintaining similar accuracy and decreasing the computational complexity.

{"title":"TDOA-based localization under uniform prior knowledge: Performance bounds and its efficient calculation","authors":"Iker Sobron , Santiago Mazuelas , Iratxe Landa , Iñaki Eizmendi , Manuel Velez","doi":"10.1016/j.dsp.2025.104981","DOIUrl":"10.1016/j.dsp.2025.104981","url":null,"abstract":"<div><div>The emergence of a myriad of location-based services has imposed a key role on wireless localization systems. The accuracy of such systems can be enhanced by using prior information on the target location area, commonly available through a map or wireless system coverage area. In the map-aware localization context, performance limits have been mainly explored for Time-of-Arrival positioning systems. This paper presents performance bounds for Time-Difference-of-Arrival (TDOA) localization using a uniform prior information of the location area. In particular, the paper derives a closed-form approximation of the Ziv-Zakai lower bound (ZZB) and Bayesian Cramer-Rao lower bound (BCRB). The presented bounds are evaluated under different configurations and compared with the maximum a posteriori (MAP) estimator, which incorporates a priori information about the location area, and with the Cramer-Rao lower bound (CRB) and the maximum likelihood (ML) estimator, both without prior information. Numerical results show that the proposed ZZB and BCRB exploit the a priori knowledge to increase the localization accuracy and provide tighter performance lower bounds of a MAP estimator, and are properly matched to the actual limits of practical positioning systems. In addition, the proposed closed-form ZZB approximation allows us to avoid numerical evaluation of integrals needed to compute BCRB and exact ZZB, while maintaining similar accuracy and decreasing the computational complexity.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104981"},"PeriodicalIF":2.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144272","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}

引用次数: 0

Direct position determination of multiple sources using a moving virtual interpolation array

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-08 DOI: 10.1016/j.dsp.2024.104973

Zhaobo Wang, Hui Guo, Yingjie Miao, Jun Zhang

Direct position determination (DPD) refers to determining the target position directly without estimating intermediate positioning parameters. Compared to the traditional two-step methods, it avoids parameter correlations and significantly enhances the algorithm's adaptability to low Signal-to-Noise Ratio (SNR) conditions. This paper uses coprime arrays to investigate direct positioning in a motion single-station passive localization system. Addressing issues where current algorithms fail to fully utilize array aperture and perform poorly in low snapshot scenarios, this paper proposes a motion single-station DPD algorithm based on virtual interpolated arrays. The proposed algorithm first uses the

l_{0}

atomic norm to estimate the covariance matrix after filling gaps in the difference co-array. Then, the MVDR (Minimum Variance Distortionless Response) method is applied to fuse covariance estimates for localization. Additionally, we derive the Cramér-Rao lower bound. Numerical simulations validate the algorithm's performance, demonstrating its ability to maximize the degrees of freedom provided by coprime arrays and achieve superior performance in scenarios with short snapshots.

{"title":"Direct position determination of multiple sources using a moving virtual interpolation array","authors":"Zhaobo Wang, Hui Guo, Yingjie Miao, Jun Zhang","doi":"10.1016/j.dsp.2024.104973","DOIUrl":"10.1016/j.dsp.2024.104973","url":null,"abstract":"<div><div>Direct position determination (DPD) refers to determining the target position directly without estimating intermediate positioning parameters. Compared to the traditional two-step methods, it avoids parameter correlations and significantly enhances the algorithm's adaptability to low Signal-to-Noise Ratio (SNR) conditions. This paper uses coprime arrays to investigate direct positioning in a motion single-station passive localization system. Addressing issues where current algorithms fail to fully utilize array aperture and perform poorly in low snapshot scenarios, this paper proposes a motion single-station DPD algorithm based on virtual interpolated arrays. The proposed algorithm first uses the <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> atomic norm to estimate the covariance matrix after filling gaps in the difference co-array. Then, the MVDR (Minimum Variance Distortionless Response) method is applied to fuse covariance estimates for localization. Additionally, we derive the Cramér-Rao lower bound. Numerical simulations validate the algorithm's performance, demonstrating its ability to maximize the degrees of freedom provided by coprime arrays and achieve superior performance in scenarios with short snapshots.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"159 ","pages":"Article 104973"},"PeriodicalIF":2.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144240","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}

引用次数: 0

Kernel generalized affine projection-like algorithms for time-series prediction

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2025-01-08 DOI: 10.1016/j.dsp.2025.104984

Guoliang Li , Ji Zhao , Hongbin Zhang

In reproducing kernel Hilbert space, a novel kernel adaptive filtering algorithm, named kernel generalized affine projection-like algorithm (K-GAPLA), is derived. The cost function is optimized by using the mixed-norm and generalized correntropy methods for the proposed K-GAPLA, which can be treated as an extension of kernel affine projection-like algorithm (APLA) that is based on a correntropy approach. What's more, applying the kernel trick and leaky way to generalized APLA (GAPLA) yields a new kernel leaky GAPLA (KL-GAPLA) in order to improve the performance of K-GAPLA. Furthermore, the variable step-size (VSS) and modified VSS (MVSS) ways are incorporated into KL-GAPLA resulting in VSS-KL-GAPLA and MVSS-KL-GAPLA, respectively. Simulations verify that the proposed kernel algorithms outperform other known kernel affine projection-type algorithms in the context of time-series prediction.

引用次数: 0

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