Signal Processing最新文献

英文中文

A low complexity method for large scale clutter suppression in passive radar 一种低复杂度的无源雷达大规模杂波抑制方法

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-07-01 Epub Date: 2026-01-20 DOI: 10.1016/j.sigpro.2026.110503

Zhibo Tang, Heyue Huang, Xingpeng Mao

In passive radar systems, the utilized signals are typically not designed for radar purposes, resulting in high ambiguity floors. These ambiguity floors, compounded by strong direct-path and multipath clutter, often obscure weak targets. To enhance the signal-to-clutter ratio (SCR), clutter suppression algorithms are essential. The Extensive Cancellation Algorithm (ECA) and its variants are widely used for this purpose by projecting received signals onto the subspace orthogonal to clutter. However, ECA suffers from high computational cost as clutter space dimensionality increases. Segmented versions like ECA-Batches (ECA-B) and Generalized Subband Cancellation (GSC) reduce complexity by broadening the suppression notch in one domain on the range-Doppler (RD) map, but remain limited when addressing large-area clutter. In this paper, we propose ECA-Batches and Subbands (ECA-BS), which performs segmentation in both time and frequency domains. This dual-domain strategy simultaneously broadens the suppression notch in both delay and Doppler dimensions, significantly reducing the clutter space. Simulation experiments verify that ECA-BS achieves clutter suppression performance comparable to existing segmented methods while significantly reducing computational complexity. Its effectiveness is further confirmed by real-world data experiments, demonstrating strong practical applicability in large scale and complex clutter environments. These results make ECA-BS particularly well-suited for real-time passive radar applications.

在无源雷达系统中，所利用的信号通常不是为雷达目的而设计的，从而导致高模糊层。这些模糊层，加上强大的直接路径和多路径杂波，通常会模糊弱目标。为了提高信杂比，杂波抑制算法是必不可少的。广泛消去算法（ECA）及其变体被广泛用于将接收到的信号投影到与杂波正交的子空间中。但是，随着杂波空间维数的增加，ECA的计算成本较高。分段版本，如eca - batch （ECA-B）和广义子带抵消（GSC）通过扩大距离-多普勒（RD）图上一个域的抑制陷波来降低复杂性，但在处理大面积杂波时仍然有限。在本文中，我们提出了eca - batch和subband (ECA-BS)，它可以在时域和频域进行分割。这种双域策略同时拓宽了延迟和多普勒两个维度的抑制陷波，显著减小了杂波空间。仿真实验证明，ECA-BS在显著降低计算复杂度的同时，实现了与现有分割方法相当的杂波抑制性能。通过实际数据实验进一步验证了该方法的有效性，在大规模复杂杂波环境下具有较强的实用性。这些结果使得ECA-BS特别适合于实时无源雷达应用。

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引用次数: 0

A regularized regression approach to robust state estimation of nonlinear systems with state constraints 带状态约束的非线性系统鲁棒状态估计的正则回归方法

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-07-01 Epub Date: 2026-01-26 DOI: 10.1016/j.sigpro.2026.110515

Yonghong Xiang, Le Yin, Yi Rong, Wenjing Xie

This paper introduces a regularized regression framework for robust state estimation of nonlinear systems. The nonlinear process and measurement functions are first approximated via statistical linearization, after which a Kalman-type estimator is derived through linear regression. The proposed framework generalizes several nonlinear Kalman filters and incorporates robust regularization to explicitly mitigate outlier effects. In particular, sparsity-promoting ℓ₁-norm regularization enables joint estimation of outliers and state variables, thereby reducing the influence of error propagation across correlated components introduced by linearization. Furthermore, an ADMM-based algorithm is developed to naturally incorporate state constraints within the estimation framework. Numerical examples demonstrate that the proposed method achieves superior estimation accuracy compared to existing techniques.

介绍了一种用于非线性系统鲁棒状态估计的正则回归框架。首先通过统计线性化方法对非线性过程和测量函数进行近似，然后通过线性回归得到卡尔曼估计量。提出的框架推广了几种非线性卡尔曼滤波器，并结合鲁棒正则化来显式减轻离群值效应。特别是，稀疏促进的1-范数正则化使异常值和状态变量的联合估计成为可能，从而减少了线性化引入的相关分量间误差传播的影响。此外，开发了一种基于admm的算法来自然地将状态约束合并到估计框架中。数值算例表明，与现有方法相比，该方法具有更高的估计精度。

引用次数: 0

Low-rank enhanced Hammerstein-spline adaptive filter for sparsity-aware nonlinear feedback cancellation in hearing aids 用于助听器稀疏感知非线性反馈消除的低秩增强hammerstein样条自适应滤波器

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.sigpro.2026.110518

Shouharda Ghosh, Tarun Meena, Nithin V. George

Adaptive feedback cancellation (AFC) remains a significant challenge in digital hearing aids due to the correlation between the microphone input and loudspeaker output, leading to biased feedback path estimates. Additionally, loudspeaker-induced non-linearities, such as saturation, further degrade sound quality. This paper proposes an Enhanced Hammerstein-Spline Adaptive Filter (EHSAF) that improves upon the conventional Hammerstein-spline model by modifying the update rule to address convergence issues in sparse feedback paths. The integration of EHSAF within the AFC framework effectively mitigates non-linear distortions, ensuring improved stability and faster convergence. Further performance gains are achieved by incorporating the nearest Kronecker product (NKP) framework, which leverages the low-rank structure of the hearing aid impulse response. Experimental results demonstrate that the proposed EHSAF-based nonlinear AFC (NAFC) and NKP-enhanced EHSAF NAFC algorithms outperform state-of-the-art methods in both accuracy and computational efficiency.

由于麦克风输入和扬声器输出之间存在相关性，导致反馈路径估计存在偏差，因此自适应反馈抵消（AFC）在数字助听器中仍然是一个重大挑战。此外，扬声器引起的非线性，如饱和，进一步降低音质。本文提出了一种增强型hammerstein -样条自适应滤波器（EHSAF），该滤波器在传统hammerstein -样条模型的基础上，通过修改更新规则来解决稀疏反馈路径中的收敛问题。在AFC框架内集成EHSAF有效地减轻了非线性扭曲，确保了更好的稳定性和更快的收敛速度。进一步的性能提升是通过结合最近的Kronecker产品（NKP）框架来实现的，该框架利用了助听器脉冲响应的低阶结构。实验结果表明，本文提出的基于EHSAF的非线性AFC （NAFC）算法和nkp增强的EHSAF NAFC算法在精度和计算效率方面都优于现有方法。

引用次数: 0

Composite anti-disturbance asynchronous control for 2-D semi-Markov jump systems with multiple disturbances: From a mode generation perspective 多扰动二维半马尔可夫跳变系统的复合抗干扰异步控制：从模态生成的角度

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.sigpro.2026.110504

Chen Li , Shuliang Wang , Yunzhe Men , Guoliang Chen

Abstract Two-dimensional (2-D) systems have been extensively investigated due to their effectiveness in modeling practical industrial processes. However, the presence of random mode switching and multiple disturbances may degrade the system performance or even induce instability. Driven by these challenges, this study focuses on a composite anti-disturbance asynchronous control strategy for 2-D semi-Markov jump Roesser systems subject to multiple disturbances. By fully considering the structural features of the Roesser model, a novel global mode generation mechanism is developed to address the issue of mode ambiguity. To counteract the detrimental influence of multiple disturbances, a 2-D disturbance observer is designed to compensate for matched disturbances arising from an exogenous system, while an energy-to-peak control scheme is employed to attenuate mismatched external disturbances. Since exact mode information is often unavailable in practical systems, a hidden Markov model is employed to handle the asynchrony in the controller-system channel. Sufficient conditions are derived to guarantee that the system is almost surely exponentially stable. Finally, the feasibility of the designed control methodology is validated through two simulation examples.

二维（2-D）系统由于其在实际工业过程建模中的有效性而得到了广泛的研究。然而，随机模式切换和多重干扰的存在可能会降低系统的性能，甚至引起不稳定。在这些挑战的驱动下，本研究重点研究了受多重干扰的二维半马尔可夫跳变Roesser系统的复合抗干扰异步控制策略。在充分考虑Roesser模型结构特点的基础上，提出了一种新的全局模态生成机制来解决模态模糊问题。为了抵消多重干扰的不利影响，设计了二维干扰观测器来补偿外源系统产生的匹配干扰，同时采用能量到峰值控制方案来衰减不匹配的外部干扰。由于在实际系统中通常无法获得精确的模式信息，因此采用隐马尔可夫模型来处理控制器-系统通道中的异步性。给出了保证系统几乎肯定是指数稳定的充分条件。最后，通过两个仿真实例验证了所设计控制方法的可行性。

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引用次数: 0

ARKFNet: A neural network-enhanced anomaly-robust Kalman filter 一种神经网络增强的异常鲁棒卡尔曼滤波器

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-07-01 Epub Date: 2026-01-23 DOI: 10.1016/j.sigpro.2026.110512

Shanli Chen , Dongyuan Lin , Peng Cai , Yunfei Zheng , Lei Zhang , Shiyuan Wang

Accurate estimation of latent states from noisy measurements remains a fundamental challenge in signal processing. Neural network-enhanced (NNE) Kalman filters, which integrate neural networks within traditional Kalman filtering frameworks, have emerged as a promising paradigm. However, in the presence of anomalous measurements, existing NNE Kalman filters often suffer from performance degradation. While certain approaches can alleviate this issue by adaptively adjusting the weights of anomalous measurements during the filtering process through end-to-end training, they are typically limited to specific types of anomalies, and their overall effectiveness remains constrained. To overcome these limitations, we propose an anomaly-robust NNE Kalman filter, called ARKFNet, that demonstrates superior performance across different kinds of anomaly scenarios. By integrating two dedicated neural network modules into the extended Kalman filter framework, ARKFNet replaces traditional anomaly-sensitive computations with a data-driven approach, establishing a unified framework for handling diverse anomaly types. To ensure stable training and numerical robustness, ARKFNet employs an alternating optimization strategy and enforces positive-definite constraints on its neural modules’ outputs through eigenvalue decomposition. Simulations demonstrate ARKFNet’s superior capability in addressing a range of anomalies, including false data injection attacks, sensor outliers, data mismatches, and missing data, outperforming existing NNE Kalman filters regarding estimation accuracy and robustness.

从噪声测量中准确估计潜在状态仍然是信号处理中的一个基本挑战。神经网络增强（NNE）卡尔曼滤波器将神经网络集成到传统的卡尔曼滤波框架中，已成为一种有前途的范例。然而，在存在异常测量的情况下，现有的NNE卡尔曼滤波器通常会受到性能下降的影响。虽然某些方法可以通过端到端训练在过滤过程中自适应地调整异常测量的权重来缓解这个问题，但它们通常仅限于特定类型的异常，并且它们的总体有效性仍然受到限制。为了克服这些限制，我们提出了一种异常鲁棒的NNE卡尔曼滤波器，称为ARKFNet，它在不同类型的异常场景中表现出卓越的性能。通过将两个专用神经网络模块集成到扩展的卡尔曼滤波框架中，ARKFNet用数据驱动的方法取代了传统的异常敏感计算，建立了一个统一的框架来处理不同类型的异常。为了确保训练的稳定性和数值鲁棒性，ARKFNet采用交替优化策略，并通过特征值分解对其神经模块的输出施加正定约束。仿真表明，ARKFNet在处理一系列异常方面具有卓越的能力，包括虚假数据注入攻击、传感器异常值、数据不匹配和丢失数据，在估计精度和鲁棒性方面优于现有的NNE卡尔曼滤波器。

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引用次数: 0

A noise-decoupled WLS solution for hybrid AOA-TDOA localization in the presence of sensor position errors 存在传感器位置误差的AOA-TDOA混合定位的噪声解耦WLS方法

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-05 DOI: 10.1016/j.sigpro.2025.110481

Yanbin Zou , Xiaofei Li , Shiru Chen , Yihan Wang , Yimao Sun

This paper addresses the problem of hybrid angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) localization in the presence of sensor position errors. Existing weighted least-squares (WLS) estimators for this scenario often exhibit suboptimal performance because the linearization of TDOA measurements introduces a detrimental cross-coupling between AOA and TDOA noise. To overcome this limitation, a novel WLS estimator is proposed that fundamentally decouples these heterogeneous noise sources through a new linearization procedure for the TDOA equations that is independent of AOA measurements. The proposed estimator is formulated as a WLS problem with a single quadratic constraint, which admits an efficient algebraic solution. Simulation results demonstrate that the proposed algorithm significantly outperforms existing WLS methods, with its estimation accuracy closely approaching the Cramér-Rao Lower Bound (CRLB).

本文研究了存在传感器位置误差时的到达角和到达时差混合定位问题。对于这种情况，现有的加权最小二乘（WLS）估计器通常表现出次优的性能，因为TDOA测量的线性化引入了AOA和TDOA噪声之间有害的交叉耦合。为了克服这一限制，提出了一种新的WLS估计器，通过对与AOA测量无关的TDOA方程的新的线性化过程，从根本上解耦了这些非均匀噪声源。所提出的估计量被表述为一个具有单一二次约束的WLS问题，它允许一个有效的代数解。仿真结果表明，该算法的估计精度接近cram - rao下界（CRLB），显著优于现有的WLS方法。

引用次数: 0

Possibility PMBM filter for robust multi-target tracking 可能性PMBM滤波器用于鲁棒多目标跟踪

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-01 DOI: 10.1016/j.sigpro.2025.110483

Lin Chen , Lin Gao , Yuxuan Xia , Chaoqun Yang , Zijie Shang , Zhicheng Su , Ting Yuan , Ping Wei

This paper considers the multi-target tracking (MTT) problem under epistemic uncertainty, and such a goal is achieved by integrating possibility theory into the Poisson multi-Bernoulli mixture (PMBM) filtering framework. To do so, we first define the possibility PMBM, and then we derive the possibility PMBM filtering recursions. The resulting possibility PMBM filter preserves strong theoretical foundations of PMBM while enhancing robustness to model mismatches. In addition, we present the possibility Poisson multi-Bernoulli (PMB) filter, which is a computationally efficient approximation of the possibility PMBM filter. We also present analytical implementations of the proposed possibility PMBM and possibility PMB filters based on Gaussian mixture representation and their robustness and estimation accuracy have been demonstrated in the simulation studies.

本文研究了认知不确定性下的多目标跟踪问题，通过将可能性理论整合到泊松-伯努利混合滤波框架中来实现多目标跟踪问题。为此，我们首先定义可能性PMBM，然后导出过滤递归的可能性PMBM。得到的可能性PMBM滤波器保留了PMBM的强大理论基础，同时增强了对模型失配的鲁棒性。此外，我们提出了可能性泊松多伯努利（PMB）滤波器，它是可能性泊松多伯努利滤波器的一种计算效率高的近似。我们还提出了基于高斯混合表示的可能性PMBM和可能性PMB滤波器的分析实现，并在仿真研究中证明了它们的鲁棒性和估计精度。

引用次数: 0

Non-fragile H∞ control of uncertain bilinear systems with signal quantization 带有信号量化的不确定双线性系统的非脆弱H∞控制

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.sigpro.2025.110463

Meng-Qi Wang, Xiao-Heng Chang

This paper studies the problem of non-fragile H_∞ control for uncertain bilinear systems with the quantized control input. The research focus lies in designing the controller by fully considering the influence of various uncertainties in the actual system on the system performance, as well as the situation where the system input is quantized, to ensure that the closed-loop control system to have the specified H_∞ performance index. By introducing the Lyapnov function and applying the Linear Matrix Inequality (LMI) method, the complex system stability conditions are transformed into easily solvable LMI problems, and the design conditions of the H_∞ controller to ensure the stability of the continuous-time bilinear system with uncertain are derived. It can be clear seen from the simulation experiments that the designed H_∞ controller can effectively deal with the system uncertainties and signal quantization problems, verifying the effectiveness of the design method proposed in this paper.

研究了具有量化控制输入的不确定双线性系统的非脆弱H∞控制问题。研究重点在于在设计控制器时充分考虑实际系统中各种不确定性对系统性能的影响，以及系统输入被量化的情况，以保证闭环控制系统具有规定的H∞性能指标。通过引入Lyapnov函数并应用线性矩阵不等式（LMI）方法，将复杂的系统稳定性条件转化为易解的LMI问题，推导出具有不确定性的连续双线性系统H∞控制器的稳定性设计条件。通过仿真实验可以清楚地看到，所设计的H∞控制器能够有效地处理系统的不确定性和信号量化问题，验证了本文所提出的设计方法的有效性。

引用次数: 0

2-D DOA and polarization estimation using cylindrical coprime conformal array via cross-covariance tensor reconstruction 基于交叉协方差张量重构的柱质共形阵二维DOA和偏振估计

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.sigpro.2025.110485

Mingcheng Fu , Zhi Zheng , Ping Li , Wen-Qin Wang

In this article, we develop an efficient approach for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation using the cylindrical coprime conformal array. Firstly, we derive the tensor-form coarray output of the cylindrical coprime conformal array and apply virtual array interpolation on the coarray output components. Subsequently, we construct a fourth-order cross-covariance tensor using the interpolated array outputs and recover a low-rank fourth-order augmented tensor by formulating a nuclear norm minimization problem. Using the reconstructed augmented tensor, we estimate the elevation and azimuth angles of sources separately through one-dimensional searching. With the estimated 2-D DOAs, we finally derive the closed-form expressions for the polarization parameter estimates. Compared with the previous techniques, the proposed algorithm can identify more sources and provide offer higher parameter estimation accuracy. Simulation results demonstrate the advantage of our algorithm over several existing techniques.

在本文中，我们开发了一种有效的二维（2-D）到达方向（DOA）和偏振估计的方法，使用圆柱质共形阵列。首先，我们推导出了柱素数共形阵列的张量形式的共阵输出，并对共阵输出分量进行了虚拟阵列插值。随后，我们利用插值数组输出构造一个四阶交叉协方差张量，并通过制定核范数最小化问题恢复一个低秩四阶增广张量。利用重构的增广张量，通过一维搜索分别估计光源的仰角和方位角。利用估计的二维DOAs，我们最后导出了偏振参数估计的封闭表达式。与以往的方法相比，该算法可以识别更多的信号源，并提供更高的参数估计精度。仿真结果表明，该算法优于现有的几种算法。

引用次数: 0

MWNet: Image dehazing network based on multi-scale feature extraction and wavelet feature enhancement MWNet：基于多尺度特征提取和小波特征增强的图像去雾网络

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-07 DOI: 10.1016/j.sigpro.2026.110493

Haixin Jia , Han Wang , Yu Zhang , Guoying Zhang , Zhengfan Li , Hengchen Xu

Atmospheric haze degrades image quality, impairing downstream vision tasks like object detection and segmentation. While wavelet-based deep learning methods are effective by leveraging lossless downsampling and spectral discrepancies, they often suffer from limited multi-scale feature extraction, inadequate frequency-domain enhancement, and a lack of structural priors. To overcome these issues, we propose MWNet, a novel framework integrating structural constraints into a U-Net with wavelet transforms. Our approach introduces dense multi-scale blocks for robust feature extraction, a hierarchical attention mechanism for high-frequency detail enhancement, and a cross-enhancement module for frequency feature interaction. Extensive experiments conducted on four benchmark datasets (SOTS-Indoor, Haze4K, Dense-Haze, NH-Haze) have demonstrated consistent superiority, with MWNet achieving SOTA in quantitative results compared to existing advanced methods (Surpassing the second-best method with average improvements of 0.16 dB in PSNR and 0.0026 in SSIM.), while qualitative results demonstrate enhanced detail preservation and noise suppression. In addition, we conducted generalization tests on three other datasets (RTTS, REAL-NH, CM-Haze), fully verifying the good generalization performance of MWNet.

大气雾霾会降低图像质量，损害下游视觉任务，如物体检测和分割。虽然基于小波的深度学习方法通过利用无损下采样和频谱差异是有效的，但它们往往受到多尺度特征提取有限、频域增强不足和缺乏结构先验的影响。为了克服这些问题，我们提出了MWNet，一种将结构约束与小波变换集成到U-Net中的新框架。我们的方法引入了密集的多尺度块用于鲁棒特征提取，分层关注机制用于高频细节增强，交叉增强模块用于频率特征交互。在四个基准数据集（SOTS-Indoor、Haze4K、Dense-Haze、NH-Haze）上进行的大量实验显示出了一致的优势，与现有的先进方法相比，MWNet在定量结果上达到了SOTA (PSNR平均提高0.16 dB， SSIM平均提高0.0026)，而定性结果显示细节保存和噪声抑制得到了增强。此外，我们还对另外三个数据集（RTTS、REAL-NH、CM-Haze）进行了泛化测试，充分验证了MWNet良好的泛化性能。

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