Signal Processing最新文献_第4页

A new method for judging thermal image quality with applications 判断热图像质量的新方法及其应用

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.sigpro.2024.109769

Sos Agaian , Hrach Ayunts , Thaweesak Trongtirakul , Sargis Hovhannisyan

Infrared thermal imaging, a non-destructive testing technology, measures the surface temperature of objects. Assessing thermal image quality is crucial for image monitoring, system design, algorithm optimization, and benchmarking. However, developing objective metrics that align with human perception is challenging due to the distinct structure of thermal images, which often feature high background temperatures and minimal variance between objects and the background. Existing methods typically target specific local features or overall image contrast, but new measures are needed to bridge the gap between objective performance and the unique characteristics of thermal images.

We propose a novel image quality assessment (IQA) method inspired by the human vision system, specifically designed for thermal images, harmonizing local and global data. The primary contributions include (1) innovative local, global, and hybrid thermal quality assessment methods that deliver precise image quality predictions without needing reference images, (2) an experimental analysis evaluating the developed blind thermal IQA measure’s applicability to various thermal images, and (3) a comprehensive analysis of traditional IQA measure-based methods applied to publicly accessible thermal databases. Extensive simulations demonstrate our method’s competitive performance and strong alignment with human perception of image quality.

红外热成像是一种无损检测技术，可测量物体的表面温度。评估热图像质量对于图像监控、系统设计、算法优化和基准测试至关重要。然而，由于热图像结构独特，通常背景温度较高，物体与背景之间的差异极小，因此制定符合人类感知的客观指标具有挑战性。现有的方法通常以特定的局部特征或整体图像对比度为目标，但需要新的测量方法来弥补客观性能与热图像独特特征之间的差距。我们提出了一种新颖的图像质量评估（IQA）方法，该方法受人类视觉系统启发，专为热图像设计，协调了局部和整体数据。主要贡献包括：(1) 创新的局部、全局和混合热质量评估方法，无需参考图像即可提供精确的图像质量预测；(2) 实验分析评估所开发的盲热 IQA 测量方法对各种热图像的适用性；(3) 全面分析应用于可公开访问的热数据库的基于 IQA 测量方法的传统方法。大量的模拟证明了我们的方法具有竞争力的性能，并且与人类对图像质量的感知高度一致。

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引用次数: 0

Learning feature-weighted regularization discriminative correlation filters for real-time UAV tracking 学习用于无人飞行器实时跟踪的特征加权正则化判别相关滤波器

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-07 DOI: 10.1016/j.sigpro.2024.109765

Xiumin Wang , Feng Ma , Xuming Wang , Chen Chen

Traditional discriminative correlation filters used for tracking unmanned aerial vehicle objects are often disrupted by concealed noise, resulting in unstable tracking results. Various methods have been developed to search for optimal feature combinations and construct feature weight pools. However, these methods often overlook the significance of different feature channels in tracking frames. Irrespective of the availability of the effective target information, a tracker regards all feature channels similarly. This makes it challenging for the tracker to avoid learning the background noise from such feature combinations. This study proposes a channel-level feature-weighting method called learning feature-weighted regularization discriminative correlation filters (FWRDCF). By introducing feature-weighted regularization (FWR) that automatically adjusts the weights of the feature channels into each frame, the FWRDCF tracker can significantly suppress background noise. Furthermore, the alternating direction method of multipliers is used to obtain the closed-form solution of the model, thereby establishing a robust correlation filter-tracking architecture. Experiments on UAV123@10fps, UAV123, DTB70, and UAVDT demonstrated that the FWRDCF tracker achieved better tracking performance than 15 other state-of-the-art trackers. An integration study of three baselines (AutoTrack, STRCF, and BACF) reveals that the proposed FWR can be integrated with trackers with multi-channel features.

用于跟踪无人飞行器物体的传统判别相关滤波器经常会受到隐藏噪声的干扰，导致跟踪结果不稳定。目前已开发出多种方法来寻找最佳特征组合并构建特征权重池。然而，这些方法往往忽略了不同特征通道在跟踪帧中的重要性。无论是否存在有效的目标信息，跟踪器对所有特征通道的看法都是相似的。这使得跟踪器在避免从这些特征组合中学习背景噪声时面临挑战。本研究提出了一种通道级特征加权方法，称为学习特征加权正则化判别相关滤波器（FWRDCF）。通过引入特征加权正则化（FWR），在每帧中自动调整特征通道的权重，FWRDCF 追踪器可以显著抑制背景噪声。此外，还利用乘法器交替方向法获得了模型的闭式解，从而建立了稳健的相关滤波器跟踪架构。在 UAV123@10fps、UAV123、DTB70 和 UAVDT 上进行的实验表明，FWRDCF 跟踪器的跟踪性能优于其他 15 种最先进的跟踪器。对三种基线（AutoTrack、STRCF 和 BACF）的集成研究表明，所提出的 FWR 可以与具有多通道特征的跟踪器集成。

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引用次数: 0

Recursive-RARE-based three-dimensional parameter estimation of near-field source considering amplitude attenuation 考虑振幅衰减的基于递归-RARE 的近场源三维参数估计

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-06 DOI: 10.1016/j.sigpro.2024.109766

Xinkai Wu , Lu Wang , Hua Chen, Ye Tian, Minghong Zhu, Gang Wang

This paper addresses the issue for near-field (NF) localization considering amplitude attenuation, proposing a recursive rank-reduction (RARE) method for three-dimensional (3-D) parameter estimation of NF sources incident on a symmetrical cross array. The proposed method constructs several one-dimensional (1-D) spectral peak search estimators to obtain two-dimensional angle and range parameters. Initially, using the received data from symmetrical array elements in one axis, a 1-D spectral peak search estimator is constructed. The origins of two types of pseudo peaks within this estimator are analyzed, and then, corresponding pseudo peaks removal methods, namely the initial screening method and the recursive RARE method, are presented to obtain the estimate of the first angle parameter. Subsequently, the estimated results are fed into another 1-D spectral peak search estimator constructed from the original received data to obtain the range parameter. Finally, the same process is applied to the other axis to obtain the second angle and range parameters, followed by a parameter matching operation for 3-D parameters. Compared to existing NF source localization methods, the proposed method more effectively eliminates pseudo peaks, and demonstrates superior parameter estimation performance under conditions of small number of snapshots and low signal-to-noise ratio, as validated by several simulation results.

本文针对考虑振幅衰减的近场（NF）定位问题，提出了一种递归秩还原（RARE）方法，用于对入射到对称十字阵列上的 NF 源进行三维（3-D）参数估计。所提出的方法构建了多个一维（1-D）谱峰搜索估计器，以获得二维角度和范围参数。首先，利用一个轴对称阵元的接收数据，构建一个一维谱峰搜索估计器。分析了该估计器中两类伪峰的起源，然后提出了相应的伪峰去除方法，即初始筛选法和递归 RARE 法，以获得第一个角度参数的估计值。随后，将估算结果输入另一个由原始接收数据构建的一维谱峰搜索估算器，以获得范围参数。最后，将相同的过程应用于另一个轴，以获得第二个角度和范围参数，然后进行参数匹配操作以获得三维参数。与现有的 NF 信号源定位方法相比，所提出的方法能更有效地消除伪峰值，并在快照数量少、信噪比低的条件下表现出更优越的参数估计性能，这一点已通过多个仿真结果得到验证。

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引用次数: 0

Assessing jamming and spoofing impacts on GNSS receivers: Automatic gain control (AGC) 评估干扰和欺骗对全球导航卫星系统接收器的影响：自动增益控制（AGC）

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-05 DOI: 10.1016/j.sigpro.2024.109762

Emile Ghizzo, El-Mehdi Djelloul, Julien Lesouple, Carl Milner, Christophe Macabiau

In modern GNSS receivers, the Automatic Gain Control (AGC) monitors the received signal level to optimize quantization and mitigate interference. This paper characterizes the jamming and spoofing impact on AGC and received signal. It first expresses the AGC gain as a function of the received signal level. Under nominal conditions, the AGC leverages the ergodic properties of the received signal to estimate its level over time. Two physical quantities, namely time-based power and signal distribution, are typically considered. However, in the presence of interference, these ergodic properties are no longer guaranteed, posing challenges in modeling the behavior of these quantities. This paper proposes a probabilistic framework for interpreting temporal estimation and computing time-based power and distribution in order to characterize AGC gain under jamming and spoofing. First, this study models the spoofing impact for both unique and multiple emitted spoofing signals as a function of the re-radiated noise power and the spoofing signals’ characteristics (e.g., number of emitted signals, amplitudes, modulation). Furthermore, it reveals the non-uniformity of the jamming chirp phase, which introduces distortions in power and signal distribution, consequently affecting AGC gain and demonstrates the convergence of the jamming signal toward a continuous wave signal at high frequencies.

在现代全球导航卫星系统接收器中，自动增益控制（AGC）监测接收信号电平，以优化量化和减少干扰。本文描述了干扰和欺骗对 AGC 和接收信号的影响。它首先将 AGC 增益表示为接收信号电平的函数。在标称条件下，AGC 利用接收信号的遍历特性来估算信号随时间变化的电平。通常会考虑两个物理量，即基于时间的功率和信号分布。然而，在存在干扰的情况下，这些遍历特性不再有保证，这给这些量的行为建模带来了挑战。本文提出了一个概率框架，用于解释时间估计和计算基于时间的功率和分布，以描述干扰和欺骗情况下的 AGC 增益。首先，本研究将唯一和多个发射的欺骗信号的欺骗影响建模为再辐射噪声功率和欺骗信号特征（如发射信号数量、振幅、调制）的函数。此外，它还揭示了干扰啁啾相位的不均匀性，这种不均匀性会导致功率和信号分布失真，从而影响自动增益控制增益，并表明干扰信号在高频时会向连续波信号靠拢。

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引用次数: 0

On compressive self-calibration with structural constraints via alternating minimization 通过交替最小化实现具有结构约束的压缩自校准

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-04 DOI: 10.1016/j.sigpro.2024.109757

Ziyi Wang, Heng Qiao

This paper considers the compressive bilinear self-calibration problem with explicit low-dimensional structural constraints. The celebrated proximal alternating linearized minimization (PALM) framework is adapted to simultaneously allow general sub-sampling schemes and structure-promoting regularizers. For the first time in literature, we refine the conditional convergence guarantees of PALM and show that the parameter commonly adopted to remove the scaling ambiguity as well as the structural penalties can ensure the unconditional convergence independent of strict assumptions on the statistical properties of the measurements, subspaces, number of snapshots, or initial iterates. In particular, we impose sparse and small total variation structures on the target signals and provide detailed numerical procedures for efficient computations. The extension to the complex-valued case is also made, and extensive numerical experiments are carried out to corroborate the theoretical claims. Different choices of sub-sampling schemes and compression rates are simulated to support the effectiveness of the proposed algorithm under various settings. We also make comparisons with the state-of-art competing methods, and the superiority of our proposed algorithm is empirically verified.

本文研究了具有明确低维结构约束的压缩双线性自校准问题。我们对著名的近端交替线性化最小化（PALM）框架进行了调整，以同时允许一般的子采样方案和结构促进正则。在文献中，我们首次完善了 PALM 的条件收敛保证，并表明通常用于消除缩放模糊性的参数以及结构惩罚可以确保无条件收敛，而与测量、子空间、快照数量或初始迭代的统计属性的严格假设无关。特别是，我们对目标信号施加了稀疏和较小的总变化结构，并提供了详细的数值计算程序，以实现高效计算。我们还对复值情况进行了扩展，并进行了大量的数值实验来证实理论依据。我们模拟了子采样方案和压缩率的不同选择，以支持所提算法在各种设置下的有效性。我们还与最先进的竞争方法进行了比较，并通过经验验证了我们提出的算法的优越性。

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引用次数: 0

Advancements and challenges in coverless image steganography: A survey 无掩码图像隐写术的进步与挑战：调查

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-04 DOI: 10.1016/j.sigpro.2024.109761

Xuyu Xiang, Yang Tan, Jiaohua Qin, Yun Tan

Coverless image steganography has emerged as a significant research direction in the field of steganography in recent years. Unlike traditional image steganography, it does not require modifying the cover image to achieve information hiding. This review aims to systematically summarize the research progress and challenges in coverless image steganography. Firstly, the paper introduces the basic principles and classification methods of coverless image steganography, including embedding methods based on low-level image features and those combining advanced semantic features from deep learning. Secondly, it discusses key research achievements in this field, such as novel embedding algorithms, efficient extraction methods, and robustness enhancement techniques against various attacks. Additionally, the review highlights major challenges faced by current coverless image steganography, including difficulties in secret information extraction, capacity limitations, and practicality issues, and explores potential solutions and future research directions. Through comprehensive analysis of existing literature, the review aims to provide researchers with a holistic perspective, fostering further development and application of coverless image steganography. The paper includes 124 key contributions, offering a comprehensive overview of coverless image steganography, covering its fundamental principles, research progress, challenges, and solutions.

无掩码图像隐写术是近年来隐写术领域出现的一个重要研究方向。与传统图像隐写术不同，它不需要修改覆盖图像就能实现信息隐藏。本综述旨在系统总结无掩码图像隐写术的研究进展和面临的挑战。首先，本文介绍了无掩码图像隐写术的基本原理和分类方法，包括基于底层图像特征的嵌入方法和结合深度学习高级语义特征的嵌入方法。其次，论文讨论了该领域的主要研究成果，如新型嵌入算法、高效提取方法和针对各种攻击的鲁棒性增强技术。此外，综述还强调了当前无掩码图像隐写术面临的主要挑战，包括秘密信息提取困难、容量限制和实用性问题，并探讨了潜在的解决方案和未来的研究方向。通过对现有文献的全面分析，该综述旨在为研究人员提供一个整体视角，促进无掩码图像隐写术的进一步发展和应用。论文包括 124 篇重要文献，全面概述了无掩码图像隐写术，涵盖了其基本原理、研究进展、挑战和解决方案。

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引用次数: 0

Dual-image reversible data hiding based on pixel value parity and multiple embedding strategy 基于像素值奇偶校验和多重嵌入策略的双图像可逆数据隐藏

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-04 DOI: 10.1016/j.sigpro.2024.109764

Hua Zhang , Zhenghong Peng , Fanli Meng

Dual-image reversible data hiding (DI-RDH) has attracted a lot of attention for its excellent embedding capability. However, the stego image after data embedding is degraded, which limits its practical application. This paper proposes an innovative DI-RDH method based on pixel value parity (PVP), which prevents influences from the image texture features and ensures that the pixel value deviation between the temporary stego image and the original image is −1, 0, or 1, as it can partially offset the difference introduced when generating the cover image. Such characteristics enable us to establish a multiple embedding strategy (MES), which applies the difference image to embed the secret message, decreasing the number of invalid shifting pixels in the difference histogram to enhance embedding capacity and image quality. The average maximum embedding rate of our method combining PVP and two phases of MES is determined to be 1.37 bpp, corresponding to the theoretical results. The average peak signal-to-noise ratio of our method on the UCID database is increased by at least 2.19 dB for a given ER of 0.5 bpp compared with several state-of-the-art methods.

双图像可逆数据隐藏（DI-RDH）因其出色的嵌入能力而备受关注。然而，数据嵌入后的伪图像质量下降，限制了其实际应用。本文提出了一种创新的基于像素值奇偶校验（PVP）的 DI-RDH 方法，它能防止图像纹理特征的影响，并确保临时偷窃图像与原始图像之间的像素值偏差为-1、0 或 1，因为它能部分抵消生成覆盖图像时引入的差异。这种特性使我们能够建立一种多重嵌入策略（MES），利用差分图像嵌入秘密信息，减少差分直方图中无效位移像素的数量，从而提高嵌入能力和图像质量。我们的方法结合了 PVP 和两个阶段的 MES，其平均最大嵌入率为 1.37 bpp，与理论结果相符。与几种最先进的方法相比，我们的方法在 UCID 数据库中的平均峰值信噪比在给定 ER 为 0.5 bpp 的情况下至少提高了 2.19 dB。

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引用次数: 0

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization 利用 TDOA 和 FDOA 测量改进分析解决方案，为 USV/AUV 协同定位提供优化约束条件

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-03 DOI: 10.1016/j.sigpro.2024.109760

Huaxia Zhang , Huigang Wang , Juan Lei , Weina Zhao

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.

本文介绍了一种增强型受限两步加权最小二乘法（WLS）分析定位算法，旨在利用到达时间差和到达频率差测量值，在协作定位系统内确定 AUV 的位置和速度。该算法整合了中间变量和目标运动参数之间的优化约束，根据 WLS 最小化准则生成精确的解析解。所提出的流程不仅通过提供分析解决方案减轻了计算负担，还通过纳入优化约束提高了估计精度。分析评估和统计模拟凸显了所提算法卓越的估计精度，并证明在适当的测量噪声条件下，位置和速度估计精度与克拉梅尔-拉奥下限一致。

引用次数: 0

Reduced-dimension STAP method for conformal array based on sequential convex programming 基于顺序凸编程的共形阵列降维 STAP 方法

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-01 DOI: 10.1016/j.sigpro.2024.109745

Jingxi Shi , Xueqi Yao , Zhihang Wang , Ziyang Cheng , Lei Xie

Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance.

与均匀阵列相比，共形阵列能有效减小雷达截面，提高飞机有限空间的利用率。然而，特殊的阵列结构加剧了杂波的非稳态性，时空自适应处理（STAP）中典型的阻塞矩阵构造方法已不再适用。为解决这些问题，本文提出了一种基于广义侧叶消除结构中惩罚顺序凸编程的降维 STAP 算法。阻塞矩阵、信道选择向量和 STAP 权重可在算法框架中同时优化。为了解决由此产生的非凸问题，我们将原始优化函数表述为准凸形式，并在迭代框架内交替解决这些参数。我们提供了数值模拟来验证所提出的方法，并证明了它的高性能。

引用次数: 0

Shaped pattern synthesis for hybrid analog–digital arrays via manifold optimization-enabled block coordinate descent 通过流形优化块坐标下降实现模拟数字混合阵列的异形图案合成

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-10-31 DOI: 10.1016/j.sigpro.2024.109752

Hongtao Li , Zhoupeng Ding , Shengyao Chen, Qi Feng, Longyao Ran, Zhong Liu

Hybrid analog–digital (HAD) architecture is a promising means to realize large-scale arrays owing to the judicious trade-off between system performance and hardware complexity. This paper investigates the beampattern shaping of HAD arrays through minimizing the beampattern matching error between desired and actual patterns. Due to the nonconvex constraints on analog and digital weights and the nonconvex nonsmooth objective function, the resultant codesign is NP-hard. To address this issue, we create an efficient algorithm by leveraging block coordinate descent (BCD) and Riemannian manifold optimization. We first equivalently represent the objective function as a smooth bi-quadratic function by introducing a uni-modulus auxiliary variable. Subsequently, we alternatingly optimize the scaling factor, digital weights, analog weights and auxiliary variable under the BCD framework, where the simultaneous update of analog weights and auxiliary variable is recast as uni-modular constrained quadratic programming which is efficiently solved by Riemannian Newton method, and the digital weights have a global optimal solution via Lagrangian multiplier method. We also derive an explicit convergence condition of this algorithm. Numerical results demonstrate that the proposed algorithm has superior performance and faster convergence speed than alternative algorithms, and produces nearly the same mainlobe gain as fully digital arrays with significantly fewer radio-frequency chains.

模拟数字混合（HAD）架构在系统性能和硬件复杂性之间进行了明智的权衡，是实现大规模阵列的一种有前途的方法。本文通过最小化期望模式与实际模式之间的信号匹配误差，研究了 HAD 阵列的信号振型。由于模拟和数字权重的非凸约束以及非凸非光滑目标函数，由此产生的编码设计是 NP-困难的。为了解决这个问题，我们利用块坐标下降（BCD）和黎曼流形优化创建了一种高效算法。首先，我们通过引入一个单模辅助变量，将目标函数等效为一个平滑的二次函数。随后，我们在 BCD 框架下交替优化缩放因子、数字权重、模拟权重和辅助变量，其中模拟权重和辅助变量的同步更新被重铸成单模态受限二次方程规划，并通过黎曼牛顿法有效求解，而数字权重则通过拉格朗日乘法得到全局最优解。我们还推导出了该算法的明确收敛条件。数值结果表明，与其他算法相比，所提出的算法性能更优越，收敛速度更快，并能以显著减少的射频链产生与全数字阵列几乎相同的主波增益。

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引用次数: 0