Digital Signal Processing最新文献

{"title":"Constrained state estimation for underwater target tracking problem","authors":"Shreya Das ,&nbsp;Shovan Bhaumik","doi":"10.1016/j.dsp.2025.105394","DOIUrl":"10.1016/j.dsp.2025.105394","url":null,"abstract":"<div><div>To enhance estimation accuracy, range, and velocity limits are used to perform constrained state estimation. The range limits are determined using machine learning techniques with the help of bearing angle, Doppler-shifted frequency, and intensity of acoustic signals received at the observer sonar as inputs. The Doppler-shifted frequency from the target can be used to determine its velocity limits. These range and velocity upper and lower limits are used as constraints while performing state estimation. The optimization problem is solved using the Lagrange multiplier. The proposed method is implemented on a bearings-only tracking problem and a Doppler-bearing tracking problem using a moderately nonlinear and a highly nonlinear scenario. The proposed estimation method is observed to have more estimation accuracy than the state-of-the-art and traditional filters in terms of root mean square error, average normalized estimation error squared, bias norm, track loss percentage, and relative execution time.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105394"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271601","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}

{"title":"Robust cubature Kalman filter based on generalized hyperbolic distribution for SLAM under colored heavy-tailed measurement noise","authors":"Jiaxiang Zhao ,&nbsp;Guoqing Wang","doi":"10.1016/j.dsp.2025.105390","DOIUrl":"10.1016/j.dsp.2025.105390","url":null,"abstract":"<div><div>The effectiveness of existing filter-based simultaneous localization and mapping (SLAM) algorithms will deteriorate under non-Gaussian measurement noise, especially when dealing with the colored heavy-tailed characteristics. In this paper, we present a novel robust cubature Kalman filter based on the generalized hyperbolic distribution for SLAM under colored heavy-tailed measurement noise. Within the proposed algorithm, the measurement differencing method is adopted to whiten the colored noise, and subsequently, the additive heavy-tailed noise is modeled by the generalized hyperbolic distribution, which contains several typical heavy-tailed distributions as special cases. We utilize the variational Bayesian inference method to jointly estimate the system state together with the measurement noise parameters, and the one-step smoothing estimation method is utilized to estimate the state vector at the previous moment to enhance the estimation accuracy. To address the varying number of landmark points observed at different moments, we perform sequential processing during the measurement update process, and this method also reduces the computational complexity. The proposed algorithm creates a flexible framework for the state estimation of nonlinear systems under colored and variable heavy-tailed noise, with several existing algorithms serving as special cases. The superior performance of the proposed algorithm in terms of estimation accuracy and robustness is validated through simulations and experiments.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105390"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240153","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}

{"title":"Towards non-uniform shadow removal using shadow adaptive normalization","authors":"Xinjie Li ,&nbsp;Yang Zhao ,&nbsp;Yuan Chen ,&nbsp;Dong Wang ,&nbsp;Li Cao ,&nbsp;Xiaoping Liu","doi":"10.1016/j.dsp.2025.105364","DOIUrl":"10.1016/j.dsp.2025.105364","url":null,"abstract":"<div><div>Image shadow removal has received increasing attention in recent years. Existing deep learning-based shadow removal methods usually rely on the assumption of globally uniform illumination or employ simplified multiplicative illumination models to estimate parameters related to shadow-free image reconstruction. However, the shading process often contains non-uniform, diverse, and complex shadow patterns, substantially reducing the robustness of current shadow removal techniques and limiting their performance. To address this issue, this paper revisits the traditional shadow degradation model and introduces a pixel-wise adaptive non-uniform illumination model. Building upon this model, a shadow adaptive normalization (SAN) module is designed to estimate the parameter maps of the illumination model and rectify the shadow features within the latent space. The proposed SAN dynamically performs attentive normalization on shadow region features, which can align the statistical distributions of shadow and non-shadow regions. To enhance the efficiency of the SAN module, this paper introduces an intra-module complexity reduction strategy to reduce computational complexity while improving the stability of the training process. In addition, to mitigate the color deviation between paired training data, this paper introduces a Poisson-function-based loss to achieve color robustness. Extensive experiments on image shadow triplet dataset (ISTD), adjusted image shadow triplet dataset (ISTD+), and shadow removal dataset (SRD) validate the superiority of the proposed method over other state-of-the-art (SOTA) approaches.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105364"},"PeriodicalIF":2.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263123","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}

{"title":"CMRG-CycleGAN: Color-line module and retinex guided CycleGAN for underwater image enhancement","authors":"Xiuman Liang,&nbsp;Jincheng Wang,&nbsp;Haifeng Yu,&nbsp;Zhendong Liu","doi":"10.1016/j.dsp.2025.105372","DOIUrl":"10.1016/j.dsp.2025.105372","url":null,"abstract":"<div><div>The complex underwater environment leads to light attenuation and scattering, resulting in color distortion, haze, blurring, and low-light conditions in images, which hinder underwater operations. Some existing deep learning methods rely too heavily on the quality of training data, and some reference images in the datasets do not conform to the principles of optical imaging; thus, the results often fail to meet expectations. This paper presents an underwater image enhancement algorithm, named CMRG-CycleGAN, which combines a color line model with a Retinex-guided CycleGAN to address these challenges. A multi-scale color line model (MCLM) is designed to endow the enhancement branch with physical modeling capabilities, improving the enhancement quality. Additionally, a reverse Retinex model (RRM) is designed in the degradation branch. A new joint optimization model is employed to process the illumination and reflection components to degrade the intermediate image, resulting in a reconstructed image that aligns more closely with the principles of underwater imaging. Each branch utilizes a twin-network generator to independently encode the detailed subnetwork and the color line subnetwork or Retinex subnetwork, improving the physical modeling capabilities of the network. Finally, the training of the discriminator is constrained using a relative adversarial loss, which further improves the autonomy of the network. Subjective and objective analyses on benchmark datasets demonstrate that the proposed CMRG-CycleGAN achieves strong performance in both visual quality and evaluation metrics.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105372"},"PeriodicalIF":2.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221697","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}

{"title":"Parameter tracking method for polarization-sensitive arrays based on the generalized labeled multi-Bernoulli filter","authors":"Jingchao You ,&nbsp;Zhikun Chen ,&nbsp;Xue Liu ,&nbsp;Zhibin Chen","doi":"10.1016/j.dsp.2025.105361","DOIUrl":"10.1016/j.dsp.2025.105361","url":null,"abstract":"<div><div>Current methods for the joint estimation of direction of arrival (DOA) and polarization parameters are generally optimized for stationary scenarios. In dynamic environments where signal sources move rapidly, these methods frequently encounter estimation errors. To overcome this challenge, we introduce a novel approach for the joint tracking of DOA and polarization parameters in dynamic scenarios. This method utilizes a polarization-sensitive array to capture incoming wave signals and employs the generalized labeled multi-Bernoulli (GLMB) framework to update the DOA and polarization parameters. Initially, an enhanced MUSIC function is deployed as a pseudo-likelihood function to improve particle distribution in areas of high-likelihood. Subsequently, a novel measurement separation (NMSS) strategy is developed to create a one-to-one correspondence between measurements and signal sources. The implementation of this algorithm through sequential Monte Carlo (SMC) techniques aims to approximate the posterior density accurately. Simulation results indicate that our proposed method surpasses existing algorithms, particularly in environments characterized by low signal-to-noise ratios (SNR) and limited snapshots.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105361"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203933","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}

{"title":"Quantum-enhanced signal processing via VQE for improved biomechanical feedback control","authors":"Javier Villalba-Díez ,&nbsp;Joaquín Ordieres-Meré","doi":"10.1016/j.dsp.2025.105357","DOIUrl":"10.1016/j.dsp.2025.105357","url":null,"abstract":"<div><div>The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm that has demonstrated significant potential for solving quantum chemistry problems, particularly in determining the ground state energy of small molecules like H₂. In this paper, we extend the application of VQE beyond quantum chemistry by utilizing it to analyze sensor data from engineered socks equipped with an accelerometer, and gyroscope sensors. Our goal is to explore the sensitivity of accelerometer and gyroscope signals to specific motion frequencies by encoding their data into the quantum states of the H₂ molecule's qubits. We introduce an automatic control mechanism based on a classical feedback loop, where the output of the VQE is compared to the desired input, and corrective actions are applied using a constant <em>K</em> to ensure the output follows the input closely. This feedback loop is designed to assist the algorithm in managing local minima, noise, and computational challenges. Using this feedback-controlled VQE system, we optimize sensor signal analysis and determine which sensor exhibits higher sensitivity to specific biomechanical frequencies. Our experimental results indicate the potential flexibility of VQE in analyzing specific biomechanical data, providing preliminary insights into the broader applications of quantum algorithms in wearable technology. As quantum hardware advances, VQE may offer applications in complex systems and diverse fields, including personalized healthcare and motion capture.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105357"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221696","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}

{"title":"An improved keystone transform implementation and its application in an S-band LFMCW Doppler radar","authors":"Liang Zhang,&nbsp;Qinglei Du,&nbsp;Weijian Liu,&nbsp;Hui Chen,&nbsp;Yongliang Wang","doi":"10.1016/j.dsp.2025.105396","DOIUrl":"10.1016/j.dsp.2025.105396","url":null,"abstract":"<div><div>Keystone transform (KT) is a radar signal processing technology, and commonly used in long-time integration to correct target range migration. At present, there are several implementation methods of KT, among which the method based the chirp-z transform (CZT) and inverse fast Fourier transform (IFFT) is the most popular, because of low computational cost and relatively good performance for the simulated data. However, the performance is not the case for the measured data used in this paper, where the datasets are the observations of the vehicles on A13 highway in The Netherlands by an S-band LFMCW radar. As to other implementations, the performance is even worse. For this problem, this paper proposes an improved KT implementation, in which the Mellin transform (MT), an integral transform commonly used in digital image processing, is employed in radar returns to remove the coupling of fast-time and slow-time, and obtains better performance over the existing methods. The computational cost of the proposed method is not very high, because a fast algorithm is used in MT computation. Based on the datasets with more than 60,000 pulses, the performance of the proposed method is fully verified.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105396"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231361","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}

{"title":"Optimizing Penalty Parameter Selection of Alternating Direction Methods of Multipliers for an Improved Joint Radar-Communication Waveform Design","authors":"Ahmed Saleem,&nbsp;Abdul Basit,&nbsp;Muhammad Fahad Munir,&nbsp;Wasim Khan,&nbsp;Aqdas Naveed Malik","doi":"10.1016/j.dsp.2025.105354","DOIUrl":"10.1016/j.dsp.2025.105354","url":null,"abstract":"<div><div>In this paper, we propose an improved waveform design strategy for multiple-input multiple-output (MIMO) radar-communication systems. At first, we formulate the waveform design as an optimization problem incorporating constraints on waveform similarity and constant power. Next, we utilize a well-known alternating direction method of multipliers (ADMM) algorithm as our chosen solution strategy for addressing this problem. More importantly, we introduce a novel method of choosing the penalty parameters for achieving an improved performance in terms of desired beampattern approximation. Furthermore, we prioritize the power of the main beams in the desired directions of the radar target and communication receiver, while simultaneously minimizing sidelobes. Finally, these waveforms effectively synthesize the desired signals for joint radar and communication systems. The simulation results support and validate the effectiveness of the proposed methodology.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105354"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231359","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}

{"title":"Robust hyperspectral image recovery from low-resolution quantized incomplete observations","authors":"Yuhong He ,&nbsp;Xinling Liu ,&nbsp;Bochuan Zheng ,&nbsp;Jingyao Hou ,&nbsp;Jianjun Wang","doi":"10.1016/j.dsp.2025.105371","DOIUrl":"10.1016/j.dsp.2025.105371","url":null,"abstract":"<div><div>Quantization is fundamental to hyperspectral imaging (HSI) systems but low-bit quantization introduces strong nonlinear distortions that impede accurate recovery. Real-world HSIs also suffer from missing entries and mixed noise: dense sub-Gaussian noise from sensors and sparse outliers due to environment or hardware faults. To address these challenges, we inject random dithering – a uniform, zero-mean perturbation applied before quantization – to linearize the otherwise nonlinear quantizer in expectation. This physical insight lets us recover the original signal with a unified framework: an <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> loss to suppress dense noise (leveraging the dithered quantizer's unbiasedness) and an <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> penalty to remove sparse outliers. Furthermore, we introduce a Representative Coefficient Total Variation (RCTV) regularizer, which mirrors the piecewise-smooth nature of HSI spectra and spatial textures and can also capture low-rank structure via matrix factorization. RCTV not only provides a clear physical basis (contiguous spectral bands and spatial regions change gradually) but also reduces computation by focusing on representative coefficients. Empirical results on real HSI datasets demonstrate that our method substantially outperforms existing techniques in both reconstruction fidelity and runtime under low-bit quantization with missing data and mixed noise. The code of our algorithm is released at <span><span>https://github.com/Yuhong163/textqrctv</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105371"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203932","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}

{"title":"CSI fingerprint positioning method based on PD array in VLP systems with signal blockage","authors":"Kaiyao Wang,&nbsp;Jiacheng Feng,&nbsp;Zhiyong Hong","doi":"10.1016/j.dsp.2025.105374","DOIUrl":"10.1016/j.dsp.2025.105374","url":null,"abstract":"<div><div>In visible light fingerprint positioning, the line of sight (LOS) signal between the photodetector (PD) and the LED may be blocked by randomly moving people or objects, resulting in degradation of positioning accuracy. To solve this problem, this paper studies a fingerprint positioning method based on PD arrays and channel state information (CSI). The proposed method leverages the spatial arrangement of the PD array to constrain multiple CSI fingerprint matching operations, rather than relying on a single PD for fingerprint matching. Two algorithms are proposed: the PD array minimum matching error (PAMME) algorithm and the PD array LOS path selection (PALS) algorithm. The PAMME algorithm leverages the spatial relationship between multiple PDs to perform multi-point matching, calculating cumulative matching errors to mitigate the limitations of single PDs in fingerprint matching. Building on PAMME, the PALS algorithm estimates the LOS signal, selecting signal combinations with the smallest matching error and removing interference from reflection paths, further improving positioning accuracy. To reduce computational complexity in multi-PD fingerprint matching, the particle swarm optimization (PSO) algorithm is integrated into the method. A segmented search strategy with nonlinear variation factors and Gaussian perturbation is introduced to avoid local optima. In a 4 m × 4 m × 3 m indoor multi-path simulation environment, where two LOS signals are randomly blocked, the PAMME and PALS methods achieve average positioning errors of 0.5 cm and 0.21 cm, respectively. This represents error reductions of 64% and 85% compared to single PD-based CSI fingerprint positioning. Additionally, the proposed PSO strategy optimization reduces the time complexity of PAMME by 94% and PALS by 50%, with minimal increases in positioning error. The simulation results demonstrate that the proposed multi-PD fingerprint positioning method achieves excellent positioning performance with a moderate increase in computational complexity. This highlights the method’s potential and advantages, offering new insights and approaches for indoor fingerprint positioning research.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"166 ","pages":"Article 105374"},"PeriodicalIF":2.9,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231360","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}