Signal Processing最新文献

A saddlepoint approximation for the smoothed periodogram 平滑周期图的鞍点近似

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

Signal Processing

Pub Date : 2024-11-28 DOI: 10.1016/j.sigpro.2024.109758

Dakota Roberson , S. Huzurbazar

Poor variance properties of the periodogram often limit its practical applicability to a wide range of modern spectral estimation and detection applications. The smoothed periodogram, a refined periodogram-based method, is one such nonparametric approach to reducing variance. Neighboring spectral samples are averaged across a spectral window, as opposed to the more common temporal or lag window. Tapered spectral windows and other modifications needed to address the time-bandwidth product and resolution-variance trade-offs complicate the statistical analysis, making it difficult to quantify statistical performance. In addition, approximate distributions for the smoothed periodogram require a priori normalization along with simplifying assumptions to yield computationally tractable results. Here, under mild asymptotic conditions, the distribution derived prior to normalization is shown to be computationally intractable in most cases. First-order statistical approximations are computationally stable but result in sizeable inaccuracies, particularly in the tails. We use a saddlepoint approximation, a second-order asymptotic method, that allows for accurate statistical characterization but is also numerically stable. Monte Carlo simulations are used to validate the results and to illustrate the robustness of the approach. Finally, its utility is demonstrated on a real-world dataset relevant to the side-channel and hardware cybersecurity communities.

周期图较差的方差特性往往限制了其在现代光谱估计和检测应用中的广泛适用性。平滑周期图是一种基于周期图的改进方法，是一种减少方差的非参数方法。相邻的光谱样本在一个光谱窗口上平均，而不是更常见的时间或滞后窗口。为了解决时间带宽乘积和分辨率方差权衡问题，需要锥形光谱窗口和其他修改，这使得统计分析变得复杂，难以量化统计性能。此外，平滑周期图的近似分布需要先验归一化以及简化假设，以产生计算上易于处理的结果。这里，在轻度渐近条件下，在大多数情况下，在归一化之前导出的分布在计算上是难以处理的。一阶统计近似在计算上是稳定的，但会导致相当大的不准确性，特别是在尾部。我们使用鞍点近似，一种二阶渐近方法，它允许精确的统计表征，但也是数值稳定的。蒙特卡罗模拟用于验证结果，并说明该方法的鲁棒性。最后，在与侧信道和硬件网络安全社区相关的真实数据集上展示了其实用性。

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引用次数: 0

Multifunctional adversarial examples: A novel mechanism for authenticatable privacy protection of images 多功能对抗示例：一种用于图像可验证隐私保护的新机制

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

Signal Processing

Pub Date : 2024-11-28 DOI: 10.1016/j.sigpro.2024.109816

Ming Li , Si Wang

With the rapid development of network technology, more and more images containing personal identity characteristics are being released by users on open network platforms. However, these images are easily collected by malicious users, leading to problems such as privacy leakage, infringement, and tampering, thus harming users’ legitimate interests. Recent studies have found that adversarial examples generated by adding tiny perturbations to an image can mislead image classifiers, causing incorrect classifications. Therefore significant privacy protection against deep neural networks is achieved while the visual quality remains indistinguishable to human eyes. However, these methods cannot protect the authenticity and integrity of the image simultaneously, failing to address infringement and tampering issues, which are also neglectable in the open network platforms. To solve this problem, we propose a novel authentication-enabled privacy protection method. The meaningful information used for authentication, instead of the meaningless perturbations, is embedded into the host image to generate adversarial examples, thereby achieving both authentication and privacy protection simultaneously. This scheme combines attention mechanisms with generative adversarial networks to adaptively select and weight features between different channels, achieving significant improvements in both aggressiveness and authentication capability. Experimental results show that our method outperforms recent similar methods in overall performance.

随着网络技术的飞速发展，用户在开放的网络平台上发布的包含个人身份特征的图像越来越多。然而，这些图像很容易被恶意用户收集，导致隐私泄露、侵权、篡改等问题，从而损害用户的合法利益。最近的研究发现，通过在图像中添加微小的扰动产生的对抗性示例可能会误导图像分类器，导致错误的分类。因此，在人眼无法区分视觉质量的同时，实现了对深度神经网络的重要隐私保护。然而，这些方法无法同时保护图像的真实性和完整性，无法解决在开放的网络平台中同样可以忽略的侵权和篡改问题。为了解决这一问题，我们提出了一种新的基于身份验证的隐私保护方法。将用于身份验证的有意义的信息，而不是无意义的扰动，嵌入到宿主图像中，生成对抗样例，从而同时实现身份验证和隐私保护。该方案将注意力机制与生成对抗网络相结合，在不同通道之间自适应地选择和加权特征，显著提高了攻击能力和认证能力。实验结果表明，该方法在整体性能上优于近年来的同类方法。

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引用次数: 0

Progressive Gaussian filtering for nonlinear uncertain systems based on Gaussian process models 基于高斯过程模型的非线性不确定系统的渐进高斯滤波

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109801

Qichao Wang , Xiaolei Zhuge , Xusheng Yang , Wen-An Zhang

This paper studies the problem of progressive Gaussian filtering (PGF) for nonlinear uncertain systems, and a PGF method is proposed by incorporating Gaussian process (GP) models to improve the compensation ability for measurement uncertainties. Firstly, the measurements are classified by the Chi-square test, and an adaptive strategy for controlling the pseudo-duration of progressive measurement update is designed to compensate for measurement uncertainties. Moreover, a conservative upper bound of the pseudo-duration is given to obtain conservative estimates. Secondly, to mitigate the adverse effects caused by measurement uncertainties on state predictions, the GP model is incorporated into the PGF. Finally, the effectiveness and superiority of the proposed method are validated through simulation results.

研究了非线性不确定系统的渐进式高斯滤波问题，提出了一种结合高斯过程模型的渐进式高斯滤波方法，以提高系统对测量不确定性的补偿能力。首先，采用卡方检验对测量数据进行分类，并设计了一种自适应策略来控制逐级测量更新的伪持续时间，以补偿测量的不确定性。此外，给出了伪持续时间的保守上界，得到了保守估计。其次，为了减轻测量不确定性对状态预测的不利影响，将GP模型引入到PGF中。最后，通过仿真结果验证了所提方法的有效性和优越性。

引用次数: 0

A broken-track association method for robust multi-target tracking adopting multi-view Doppler measurement information 一种采用多视角多普勒测量信息的破航迹关联鲁棒多目标跟踪方法

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109815

Jiaqi Zhang , Cao Zeng , Haihong Tao , Yuhong Zhang , Shihua Zhao , Qirui Wu

Due to reasons such as target maneuvering and track crossing, mistaken track association may be caused. When the target is in Doppler blind zone, it often leads to the loss of measurement information, which is reflected in the situation as track breakage. In response to the demand of track repair in the case of track breakage, we propose a robust multi-target broken-track association method that comprehensively utilizes multi-view Doppler measurement information. Firstly, based on the derivation of measurement coordinate transformation bias, the multi-sensor Doppler measurement after error correction is fused to obtain the heading velocity and heading acceleration measurement of the target. Secondly, the multi-scan association cost function based on the heading velocity and heading acceleration measurement is constructed, and the optimal correlation sequence is obtained by minimizing the complexity of the cost function. Then, for the case of missing measurements, the optimal association sequence is used to extrapolate the missing measurements, thereby accomplishing the correlation among the broken track segment, the extrapolated measurement and the optimal correlation sequence. Thirdly, we design a multi-scan GLMB smoother to perform forward prediction and backward smoothing on the above correlation results to improve the smoothness of the track. Simulation and actual experimental results suggest that our proposed method can effectively deal with the track breakage situation of dense targets, particularly in track integrity, track accuracy and robustness compared with the existing approaches.

由于目标机动、航迹交叉等原因，可能造成错误的航迹关联。当目标处于多普勒盲区时，往往会导致测量信息的丢失，表现为航迹断裂。针对轨道破损情况下的轨道修复需求，提出了一种综合利用多视角多普勒测量信息的鲁棒多目标破损轨道关联方法。首先，基于测量坐标变换偏差的推导，将误差修正后的多传感器多普勒测量融合得到目标的航向速度和航向加速度测量值；其次，构建了基于航向速度和航向加速度测量的多扫描关联代价函数，并通过最小化代价函数复杂度得到最优关联序列；然后，对于缺失的测量值，使用最优关联序列外推缺失的测量值，从而实现破碎航段、外推的测量值与最优相关序列之间的关联。第三，我们设计了一个多扫描GLMB平滑器，对上述相关结果进行前向预测和后向平滑，以提高轨迹的平整度。仿真和实际实验结果表明，与现有方法相比，该方法能有效地处理密集目标的航迹破损情况，特别是在航迹完整性、航迹精度和鲁棒性方面。

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引用次数: 0

A privacy-preserving license plate encryption scheme based on an improved YOLOv8 image recognition algorithm 一种基于改进的YOLOv8图像识别算法的车牌保密加密方案

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109811

Chengye Zou , Yunong Liu , Yongwei Yang , Changjun Zhou , Yang Yu , Yubao Shang

With the rapid development of green smart cities, urban intelligence has also brought new challenges, particularly in protecting the privacy information of vehicles within the city. To address this issue, we propose a novel image encryption scheme to ensure the security of image transmission. This method captures vehicle images through roadside surveillance cameras and uses an improved YOLOv8 algorithm to identify sensitive vehicle information in real time, which is then securely transmitted to the city’s traffic management system. To safeguard the data, we employ a combination of the Rabbit Competition scrambling algorithm and a custom diffusion kernel to perform real-time encryption on the identified image regions, protecting privacy-sensitive areas. Experimental results show that our method improves accuracy by 1.53% and average precision by 1.4% on the test set compared to the baseline model, indicating a substantial enhancement in detection accuracy. Additionally, the encryption scheme demonstrates a larger key space, improved robustness, and significantly enhanced anti-attack capabilities, confirming its effectiveness in protecting vehicle information in smart city environments.

随着绿色智慧城市的快速发展，城市智能化也带来了新的挑战，尤其是在保护城市内车辆的隐私信息方面。为了解决这个问题，我们提出了一种新的图像加密方案来保证图像传输的安全性。该方法通过路边监控摄像头捕获车辆图像，并使用改进的YOLOv8算法实时识别敏感车辆信息，然后安全地传输到城市交通管理系统。为了保护数据，我们采用兔子竞争置乱算法和自定义扩散核的组合对识别的图像区域进行实时加密，保护隐私敏感区域。实验结果表明，与基线模型相比，我们的方法在测试集上的准确率提高了1.53%，平均精度提高了1.4%，表明检测精度有了很大的提高。此外，该加密方案具有更大的密钥空间，增强了鲁棒性，显著增强了抗攻击能力，验证了其在智慧城市环境下保护车辆信息的有效性。

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引用次数: 0

Simplified ICF and smart MIR for PAPR reduction in OFDM systems OFDM系统中减少PAPR的简化ICF和智能MIR

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109804

Heng Du, Jiang Xue , Weilin Song, Qihong Duan

One of the biggest problems for orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR), which breaks the orthogonality among subcarriers and leads to the nonlinear distortion of transmitted signals after being processed by the power amplifier (PA). The iterative clipping and filtering (ICF) method is one of the well known and applied existing PAPR reduction techniques at the transmitter and the modified iterative receiver (MIR) is an effective existing method for signal recovery with the ICF method in its iterative process at the receiver. However, the ICF method, as well as the MIR, suffers from the high computational complexity due to the oversampling and high-order inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) operators. Besides, the performance of MIR is limited by the number of iterations. In this paper, to reduce the computational complexity of ICF method, the phase rotation iterative clipping and filtering (PRICF) method is proposed, which performs padding, phase rotation and low-order IFFT/FFT operators. Meanwhile, the computational complexity of MIR is also reduced because the ICF method is replaced by the PRICF method in its iterative process. Furthermore, to accelerate the iteration or improve the performance, the modified iterative network receiver (MIR-Net) is proposed by introducing trainable parameters based on the method of model-driven deep learning. Comparing with the combination of ICF and MIR, the simulation results demonstrate the advantages of our proposed methods, which is the combination of PRICF and MIR-Net, in terms of the computational complexity and performance.

正交频分复用（OFDM）系统的最大问题之一是峰值平均功率比（PAPR）过高，它破坏了子载波间的正交性，导致传输信号经过功率放大器处理后产生非线性失真。迭代裁剪滤波（ICF）方法是目前在发射机处较为知名和应用的PAPR降低技术之一，而改进迭代接收机（MIR）则是利用ICF方法在接收机处的迭代过程进行信号恢复的一种有效方法。然而，由于过采样和高阶逆快速傅里叶变换/快速傅里叶变换（IFFT/FFT）算子，ICF方法以及MIR具有较高的计算复杂度。此外，MIR的性能受到迭代次数的限制。为了降低ICF方法的计算复杂度，本文提出了相位旋转迭代裁剪滤波（PRICF）方法，该方法执行填充、相位旋转和低阶IFFT/FFT算子。同时，由于在迭代过程中将ICF方法替换为PRICF方法，降低了MIR的计算复杂度。此外，为了加速迭代或提高性能，基于模型驱动的深度学习方法，引入可训练参数，提出了改进迭代网络接收器（MIR-Net）。与ICF和MIR的组合方法相比，仿真结果证明了我们提出的PRICF和MIR- net的组合方法在计算复杂度和性能方面的优势。

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引用次数: 0

A dual-based first-order algorithm for ToA asynchronous localization and synchronization 一种基于双阶的ToA异步定位与同步算法

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109814

Eyal Gur , Alon Amar , Shoham Sabach

Joint ToA source localization and synchronization determines the location and time offset of a radiating source using time-of-arrival measurements collected from a time-synchronized array of sensors. Various approaches have been proposed to address this non-convex and non-smooth optimization problem, which usually transform the problem by applying convex relaxations or smooth approximations. In this paper, we focus on the original joint problem and show that it can be expressed as a sum of a quadratic function with multiple non-smooth functions. This type of problems cannot be solved using traditional proximal-based methods, and we develop a tailored dual-based first-order algorithm. We analyze the proposed method, and prove its convergence to critical points of the original problem under mild assumptions. Experimental results showcase advantages of the method in terms of convergence, RMSE, bias, and complexity.

联合ToA源定位和同步利用从时间同步传感器阵列收集的到达时间测量值确定辐射源的位置和时间偏移。已经提出了各种方法来解决这个非凸和非光滑的优化问题，这些方法通常通过应用凸松弛或光滑近似来转换问题。本文主要讨论了原始联合问题，并证明了它可以表示为一个二次函数与多个非光滑函数的和。这类问题不能用传统的基于近端的方法来解决，我们开发了一种定制的基于双一阶算法。我们对所提出的方法进行了分析，并在温和的假设条件下证明了它收敛于原问题的临界点。实验结果表明，该方法在收敛性、均方根误差、偏差和复杂性方面具有优势。

引用次数: 0

Extraction of patterns from images using a model of combined frequency localization spaces 使用组合频率定位空间模型从图像中提取模式

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

Signal Processing

Pub Date : 2024-11-26 DOI: 10.1016/j.sigpro.2024.109810

Djordje Stanković , Cornel Ioana , Irena Orović

An algorithm for image decomposition and separation of superposed stationary contributions is proposed. It is based on the concept of sparse-to-sparse domain representation achieved through a relationship between block-based and full-size discrete cosine transform. The L-statistics is adapted to discard nonstationary components from the frequency domain vectors, leaving just a few coefficients associated with stationary pattern. These fewer stationary components are then used under the compressive sensing framework to reconstruct the stationary pattern. The original image is observed as a nonstationary component, acting as a non-desired part at this stage of the procedure, while the stationary pattern is observed as a “desired part” that should be extracted through the reconstruction process. The problem of interest is formulated as underdetermined system of equations resulting from a relationship between the two considered transformation spaces. Once the stationary pattern is reconstructed, it can be removed entirely from the image. Furthermore, it will be shown that the efficiency of pattern extraction cannot be affected, even when image contains additional nonstationary disturbance (here, the noisy image is observed as nonstationary undesired part). The proposed approach is motivated by challenges in removing Moiré-like patterns from images, enabling some interesting applications, including extraction of hidden sinusoidal signatures.

提出了一种图像分解与分离叠加平稳贡献的算法。它基于稀疏到稀疏域表示的概念，通过基于块的和全尺寸离散余弦变换之间的关系实现。l统计量适用于从频域矢量中丢弃非平稳分量，只留下几个与平稳模式相关的系数。然后在压缩感知框架下使用这些较少的平稳分量来重建平稳模式。原始图像被观察为非平稳成分，在此阶段作为非期望部分，而平稳模式被观察为应该通过重建过程提取的“期望部分”。感兴趣的问题被表述为由两个考虑的变换空间之间的关系产生的待定方程组。一旦固定图案被重建，它就可以完全从图像中移除。此外，将表明，即使图像包含额外的非平稳干扰（在这里，噪声图像被视为非平稳的不需要的部分），也不会影响模式提取的效率。提出的方法的动机来自于从图像中去除moir样模式的挑战，从而实现了一些有趣的应用，包括提取隐藏的正弦特征。

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引用次数: 0

Game-theory-based complexity allocation for 360-degree video coding 基于博弈论的360度视频编码复杂度分配

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

Signal Processing

Pub Date : 2024-11-23 DOI: 10.1016/j.sigpro.2024.109807

Jielian Lin , Kaiying Xing , Yiwen Xu

360-degree video applications with immersive experiences have been well spread in our daily life. However, 360-degree video with high resolution (e.g., 8192

\times 4096, 6144 \times

3072, and 3840 × 1920) leads to high coding computational complexity. To further optimize the complexity allocation and obtain the optimal coding performance, this paper proposes a game-theory-based complexity allocation algorithm for 360-degree video coding. The proposed method first constructs the latitude-level complexity allocation model by introducing game theory. Second, the optimal Lagrange coefficient

λ^{*}

value is obtained by the Newton method, and then, the complexity of the latitude can be further obtained. Finally, the overall complexity allocation algorithm is also designed. Experimental results indicate our method obtains Time Saving (TS) with 18.44%

\sim

67.08% and BDBR performance with 0.10%

\sim

3.11%. The proposed method also achieves the optimal Coding Gain (CG) values for the most global target complexity.

具有沉浸式体验的360度视频应用已经在我们的日常生活中得到了很好的普及。然而，高分辨率的360度视频（例如8192×4096、6144× 3072和3840 × 1920）导致编码计算复杂度很高。为了进一步优化复杂度分配，获得最优的编码性能，本文提出了一种基于博弈论的360度视频编码复杂度分配算法。该方法首先引入博弈论，构建了纬度层次的复杂度分配模型；其次，利用牛顿法求出最优拉格朗日系数λ *值，进而求出纬度的复杂度；最后，设计了总体复杂度分配算法。实验结果表明，我们的方法获得了18.44% ~ 67.08%的时间节省（TS）和0.10% ~ 3.11%的BDBR性能。该方法还能在最大全局目标复杂度下获得最优的编码增益（CG）。

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引用次数: 0

FTAN: Feature Transform and Alignment Network for cross-domain specific emitter identification 跨域特定发射极识别的特征变换与对准网络

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

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109800

Zhiling Xiao, Xiang Zhang, Guomin Sun, Huaizong Shao

Conventional deep learning-based specific emitter identification (SEI) methods are consistently constrained to domain-invariant assumption, leading to a decrease in recognition accuracy when the feature domain changes. To tackle this issue, we propose a novel unsupervised domain adaptation (UDA) framework named feature transform and alignment network (FTAN) for cross-domain SEI. In FTAN, we first apply a weight-shared network to extract the initial features of signals from all domains. Then, we introduce domain-specific modules to individually learn domain-invariant features, which can minimize the distribution discrepancies of source and target signals. Finally, the aligned domain-invariant features are utilized for identification. We evaluate the performance of FTAN on the various signal datasets. The experimental results demonstrate that FTAN significantly mitigates identification performance degradation in cross-domain scenarios and outperforms other state-of-the-art methods.

传统的基于深度学习的特定发射器识别（SEI）方法一直受到域不变假设的约束，导致特征域变化时识别精度下降。为了解决这个问题，我们提出了一种新的无监督域自适应（UDA）框架，称为跨域SEI特征转换和对齐网络（FTAN）。在FTAN中，我们首先应用一个权重共享网络来提取所有域信号的初始特征。然后，我们引入特定领域的模块来单独学习领域不变特征，从而最大限度地减少源信号和目标信号的分布差异。最后，利用对齐后的域不变特征进行识别。我们评估了FTAN在各种信号数据集上的性能。实验结果表明，FTAN显著减轻了跨域场景下的识别性能下降，优于其他最先进的方法。

引用次数: 0