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Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

IEEE signal processing LETTERS 2025年 32卷 811-815页

作者： El Hajjar, Dana Ginolhac, Guillaume Yan, Yajing El Korso, Mohammed Nabil Univ Savoie Mont Blanc LISTIC F-74000 Annecy France Univ Paris Saclay Cent Supelec L2S F-91190 Gif Sur Yvette France

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) exploits Synthetic Aperture Radar images time series (SAR-TS) for surface deformation monitoring via phase difference (with respect to a reference image) estimation. Most of the actual state-of-the-art MT-InSAR rely on temporal covariance matrix of the SAR-TS, assuming Gaussian distribution. However, these approaches become computationally expensive when the time series lengthens and new images are added to the data vector. This paper proposes a novel approach to sequentially integrate each newly acquired image using Phase Linking (PL) and Maximum Likelihood Estimation (MLE). The methodology divides the data into blocks, using previous images and estimations as a prior to sequentially estimate the phase of the new image. Actually, this framework allows to consider non Gaussian distributions, such as a mixture of scaled Gaussian distribution, which is particularly important to consider when dealing with urban areas.

关键词： Covariance matrices Coherence Synthetic aperture radar signal processing algorithms Radar polarimetry Maximum likelihood estimation Vectors Time series analysis Radar imaging Gaussian distribution Multi-temporal interferometric synthetic aperture radar maximum likelihood estimation covariance matrix estimation mixture of scaled Gaussian distribution

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An Accelerated Successive Convex Approximation Scheme With Exact Step Sizes for L1-Regression

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 184-193页

作者： Schynol, Lukas Hemsing, Moritz Pesavento, Marius Tech Univ Darmstadt D-64283 Darmstadt Germany

We consider the minimization of $\ell _{1}$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially i... 详细信息

We consider the minimization of $\ell _algorithms$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially in sparse problem instances. This work proposes an acceleration scheme for the successive convex approximation technique with a negligible additional computational cost. We demonstrate this scheme by devising three related accelerated algorithms with provable convergence. The first introduces an additional descent step along the past optimization trajectory in the variable update that is inspired by Nesterov's accelerated gradient method and uses a closed-form step size. The second performs a simultaneous descent step along both the best response and the past trajectory, thereby finding a two-dimensional step size, also in closed-form. The third algorithm combines the previous two approaches. All algorithms are hyperparameter-free. Empirical results confirm that the acceleration approaches improve the convergence rate compared to benchmark algorithms, and that they retain the benefits of successive convex approximation also in non-sparse instances.

关键词： signal processing algorithms Trajectory Vectors Optimization Approximation algorithms Gradient methods Convergence Computational efficiency signal processing Sparse matrices Compressed sensing FISTA LASSO L1-regularized least squares Nesterov's accelerated gradient method optimization successive convex approximation

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HF2TNet: A Hierarchical Fusion Two-Stage Training Network for Infrared and Visible Image Fusion

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IEEE signal processing LETTERS 2024年 31卷 3164-3168页

作者： Lv, Ting Ji, Chuanming Jiang, Hong Liu, Yu Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney 2007 Australia Hefei Univ Technol Dept Biomed Engn Hefei 230009 Peoples R China

In the field of infrared and visible image fusion, current algorithms often focus on complex feature extraction and sophisticated fusion mechanisms, ignoring the issues of information redundancy and feature imbalance. These limit effective information aggregation. To address these issues, this paper proposes a hierarchical fusion strategy with a two-stage training network, abbreviated as HF2TNet, which achieves effective information aggregation in a staged manner. In the initial training stage, a three-stream encoder-decoder architecture is proposed, seamlessly integrating CNN and transformer modules. This architecture extracts both global and local features from visible and infrared images, capturing their shared attributes before the fusion process. Moreover, a multi-shared attention module (MSAM) is proposed to profoundly reconstruct and augment the visible and infrared features, ensuring the preservation and enhancement of details across modalities. In the subsequent stage, HF2TNet utilizes the pre-integrated features as query inputs for the dual MSAMs. These modules interact with the previously reconstructed infrared and visible features to enhance complementary information and ensure a balanced feature fusion. Experimental results indicate HF2TNet's superior performance on standard datasets like MSRS and TNO, especially in complex scenes, demonstrating its potential in multimodal image fusion.

关键词： Feature extraction Training Image reconstruction Image fusion Transformers Decoding Convolution Vectors signal processing algorithms Sensor phenomena and characterization CNN deep learning image fusion transformer

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Reconstructed Variational Bayesian Kalman Filter Under Heavy-Tailed and Skewed Noises

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IEEE signal processing LETTERS 2024年 31卷 2405-2409页

作者： Wang, Ke Wu, Panlong Zhao, Baochen Kong, Lingqi He, Shan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In this letter, a new reconstructed variational Bayesian (RVB) Kalman filter is proposed for handling heavy-tailed and skewed noises. The Student's t distribution is used to model predicted state, and the skewed Gaussian-exponential mixture distribution is used to model measurement noises. In order to more accurately deal with process noises, we model the predicted covariance based on the inverse Wishart distribution. Subsequently, a new RVB framework is proposed to separate the iterations of process and measurement noise related parameters, which achieves decoupling of hidden variables. Under the new RVB framework, the RVB-ST-SGEM-KF algorithm is derived. The superiority of the proposed RVB framework and algorithm are validated through a maneuvering target tracking scenario.

关键词： Noise signal processing algorithms Noise measurement Heavily-tailed distribution Predictive models Adaptation models Target tracking Heavy-tailed and skewed noises Kalman Filter reconstructed variational Bayesian skewed Gaussian-exponential mixture distribution student's t distribution

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High-Speed Energy-Efficient Fixed-Point Signed Multipliers for FPGA-Based DSP Applications

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IEEE EMBEDDED SYSTEMS LETTERS 2024年第4期16卷 417-420页

作者： Nagar, Mitul Sudhirkumar Mathuriya, Aditya Patel, Sohan H. Engineer, Pinalkumar J. Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat 395007 India

Among various platforms for computer vision algorithms, FPGA has gained popularity as a low-power solution. These algorithms involve convolution operation which are extensively performed using signed multipliers. Hence, this work proposes high-speed and energy-efficient signed fixed-point multipliers for digital signal processing (DSP) applications. This work focuses on reducing the combinational path delay (CPD) using LUT-based Booth radix-4 partial product (PP) generation with Bewick's sign extension and Dadda-based concurrent PP reduction with carry save adder (CSA) for Xilinx (now AMD) FPGA. The proposed design eliminates the requirement of a long carry chain for PP reduction. The proposed multiplier reduces CPD by 3%, 4%, and 16% compared to the state-of-the-art (SoA) multiplier for 8x8 , 16x16 , and 32x32 sizes, respectively. We have also analyzed our proposed 32x32 multiplier by pipelining, which offers CPD and EDP reduction by 12.28% and 19.47% at the cost of a 3% and 80% increase in LUTs and flip-flops, respectively, compared to the combinatorial multiplier.

关键词： Table lookup Field programmable gate arrays Adders Vegetation Standards signal processing algorithms Resource management Booth radix-4 digital signal processing (DSP) energy-efficient field-programmable gate arrays signed fixedpoint multiplier

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Robust Elliptic Positioning via Sparse Regularization and ADMM for a Distributed MIMO Radar in the Presence of Outliers

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IEEE signal processing LETTERS 2024年 31卷 2110-2114页

作者： Dang, Xudong Liu, Hongwei Yan, Junkun Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Large-magnitude errors, known as outliers, have a significant impact on the accuracy of elliptic positioning (EP) for a distributed MIMO radar. Most of the existing EP techniques are developed in the least squares sense, susceptible to outliers. In this letter, we propose to leverage a sparse argument to model the outliers and decompose the measurement noise into two components, namely, inliers and outliers. By doing so, the robust EP is formulated as a & ell;(0) -norm minimization problem with non-convex constraints. By relaxing the objective & ell;(0) -norm with the convex & ell;(1) -norm, an alternating direction method of multipliers based algorithm is derived to efficiently solve the relaxed optimization problem. Numerical results illustrate the improved performance of the proposed method compared to the existing methods.

关键词： Noise Pollution measurement Noise measurement MIMO radar Optimization Convex functions signal processing algorithms Alternating direction method of multipliers (ADMM) distributed MIMO radar elliptic positioning outliers sparse regularization

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Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems

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IEEE signal processing LETTERS 2024年 31卷 2895-2899页

作者： Chen, Zhuo Niu, Xiaoming Ding, Jian Wu, Hong Liu, Zhiyang Nankai Univ Coll Elect Informat & Opt Engn Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Optoelect Sensor & Sensing Network Tianjin 300350 Peoples R China China Res Inst Radiowave Propagat Natl Key Lab Electromagnet Environm Qingdao 266107 Peoples R China Nankai Univ Minist Educ Engn Res Ctr Thin Film Optoelect Technol Tianjin 300350 Peoples R China

The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.

关键词： Channel estimation Symbols Bayes methods Delays signal processing algorithms Indexes Doppler shift Vectors Estimation Adaptive systems Adaptive pattern-coupled sparse Bayesian learning block sparse signal recovery channel estimation OTFS

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Joint Learning Spatial-Temporal Attention Correlation Filters for Aerial Tracking

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IEEE signal processing LETTERS 2024年 31卷 686-690页

作者： Zhao, Bo Ma, Sugang Zhao, Zhixian Zhang, Lei Hou, Zhiqiang Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Discriminative correlation filter (DCF)-based UAV tracking algorithms have drawn much attention due to their outstanding robustness and high computational efficiency. However, these algorithms are easily disturbed by background noise and abrupt changes in target appearance, leading to tracking failure. To address the issues above, we propose a real-time UAV object tracking algorithm with adaptive spatial-temporal attention. Specifically, we construct two filters with different roles based on the training sample's target foreground and environmental background. The spatial attention filter is implemented by incorporating a spatial context regularizer into the traditional DCF paradigm, which fully utilizes background environmental information to suppress background environmental noise and effectively distinguish between the target and the background. The temporal attention filter focuses on the continuity of the target samples, modeling only the target patch samples during the training process and introducing a temporal context regularizer, which substantially enhances the tracker's robustness against target occlusions and deformations. The two are jointly optimized by the Alternating Direction Method of Multipliers (ADMM) algorithm, which is mutually constrained during training and complemented during detection. Extensive experiments on three mainstream UAV benchmarks demonstrate the tracking advantages of the proposed algorithm.

关键词： Target tracking Filtering algorithms Information filters Training signal processing algorithms Correlation Autonomous aerial vehicles Discriminative correlation filter unmanned aerial vehicle temporal context regularization spatial context regularization dual regularization

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A Density Clustering-Based CFAR Algorithm for Ship Detection in SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Yang Wang, Zeyu Chen, Hongmeng Li, Yachao Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.

关键词： Clustering algorithms Clutter Marine vehicles Radar polarimetry signal processing algorithms Detectors Noise Constant false alarm rate (CFAR) density clustering ship detection synthetic aperture radar (SAR)

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Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 948-968页

作者： Khan, Aamir Ullah Mint, Saw James Shah, Syed Najaf Haider Schneider, Christian Robert, Joerg Tech Univ Ilmenau Elect Measurements & Signal Proc Grp D-98693 Ilmenau Germany

Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.

关键词： Roads Channel models Radar Integrated sensing and communication Scattering Layout Wireless communication Reflectivity Object detection Radar cross-sections Autonomous driving channel models radar cross-section radar sensing radar clutter signal processing algorithms vehicle-to-vehicle communication

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