Signal Processing最新文献

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Transmit beamforming design for area surveillance and multi-target tracking in colocated MIMO radar 多址MIMO雷达区域监视和多目标跟踪的发射波束形成设计

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

Signal Processing

Pub Date : 2026-01-05 DOI: 10.1016/j.sigpro.2026.110491

Chengxin Yang , Benoit Champagne , Wei Yi

This paper addresses the optimization problem of transmit beamforming design for area surveillance and multi-target tracking (MTT) in a colocated multiple-input multiple-output (C-MIMO) radar system. We first establish the relationship between the detection probability and the predictive Cramér-Rao lower bound (PCRLB) as performance metrics, and the transmit signal correlation matrix as the design variable. The surveillance area, defined as a circular sector bounded by a polar angle and the intersecting arc, is divided into independent smaller sectors, each corresponding to a different illumination direction of the C-MIMO radar. To maximize the efficient utilization of power resources, we then aim to maximize the number of simultaneously illuminated sectors while achieving desired detection probability and target tracking accuracy. Given that the formulated optimization problem is an intractable non-convex mixed-integer nonlinear problem, we propose a beamforming algorithm based on Quality of Service (QoS) to solve it efficiently. Simulation results indicate that the proposed algorithm is capable of effectively maximizing the illuminated area while consistently meeting the specified detection probability and MTT accuracy requirements.

研究了一种多输入多输出（C-MIMO）雷达系统中用于区域监视和多目标跟踪（MTT）的发射波束形成优化设计问题。我们首先建立了检测概率与预测cramsamr - rao下界（PCRLB）之间的关系作为性能指标，并将发射信号相关矩阵作为设计变量。监视区域定义为一个圆形扇区，以极角和相交弧为界，分为独立的较小扇区，每个扇区对应C-MIMO雷达的不同照明方向。为了最大限度地有效利用电力资源，我们的目标是最大限度地同时照亮扇区的数量，同时达到所需的检测概率和目标跟踪精度。针对该优化问题是一个棘手的非凸混合整数非线性问题，提出了一种基于服务质量（QoS）的波束形成算法。仿真结果表明，该算法能够在满足指定检测概率和MTT精度要求的同时，有效地实现光照面积最大化。

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引用次数: 0

Design of Low-Rank differential beamformers with constrained directivity or robustness 具有约束指向性或鲁棒性的低阶差分波束形成器设计

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

Signal Processing

Pub Date : 2026-01-03 DOI: 10.1016/j.sigpro.2026.110487

Kunlong Zhao , Jilu Jin , Xueqin Luo , Gongping Huang , Jingdong Chen , Jacob Benesty

Differential microphone arrays (DMAs) are recognized for their highly directive broadband beampatterns and have attracted significant interest in the design of compact microphone arrays. It has been shown that increasing the number of microphones in a DMA can improve array performance. However, when applying DMAs to embedded systems, this creates challenges due to the increased number of parameters, higher computational complexity, and the need to maintain the array’s robustness. To address these challenges, this paper presents a method for designing robust low-rank (LR) differential beamformers. Initially, we extend traditional differential beamforming by introducing an LR differential beamforming framework, which represents a long filter as the Kronecker product of two sets of shorter filters, significantly reducing both the number of parameters and computational complexity. Next, we derive robust designs for the two sets of shorter filters by maximizing the directivity factor (DF) subject to a white noise gain (WNG) constraint, or by maximizing the WNG subject to a DF constraint. This results in two types of LR differential beamformers that achieve the desired DF or WNG levels. The optimization problems are formulated and transformed into quadratic eigenvalue problems (QEPs), leading to closed-form solutions for both the WNG-constrained and DF-constrained LR differential beamformers. Simulation results demonstrate the effectiveness of the proposed method, confirming its robustness and enhanced computational efficiency.

差分传声器阵列（DMAs）以其高度定向的宽带波束模式而闻名，并在紧凑型传声器阵列的设计中引起了极大的兴趣。研究表明，在DMA中增加麦克风的数量可以提高阵列的性能。然而，当将dma应用于嵌入式系统时，由于参数数量增加、计算复杂性增加以及需要保持阵列的鲁棒性，这带来了挑战。为了解决这些问题，本文提出了一种设计鲁棒低阶差分波束形成器的方法。首先，我们通过引入LR差分波束形成框架扩展了传统的差分波束形成，该框架将长滤波器表示为两组较短滤波器的Kronecker积，从而显着减少了参数数量和计算复杂度。接下来，我们通过最大化受白噪声增益（WNG）约束的指向性因子（DF），或通过最大化受DF约束的WNG，推导出两组较短滤波器的鲁棒设计。这导致两种类型的LR差分波束形成器达到所需的DF或WNG水平。将优化问题转化为二次特征值问题（QEPs），得到wng约束和df约束的LR差分波束形成器的闭合解。仿真结果验证了该方法的有效性，验证了该方法的鲁棒性和提高的计算效率。

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引用次数: 0

Federated learning: A stochastic approximation approach 联邦学习：一种随机逼近方法

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

Signal Processing

Pub Date : 2026-01-03 DOI: 10.1016/j.sigpro.2025.110479

Srihari P V, Anik Kumar Paul, Bharath Bhikkaji

This paper considers the Federated learning (FL) in a stochastic approximation (SA) framework. Here, each client i trains a local model using its dataset

D^{(i)}

and periodically transmits the model parameters

w_{n}^{(i)}

to a central server, where they are aggregated into a global model parameter

{\bar{w}}_{n}

and sent back. The clients continue their training by re-initializing their local models with the global model parameters.

Prior works typically assumed constant (and often identical) step sizes (learning rates) across clients for model training. As a consequence the aggregated model converges only in expectation. In this work, client-specific tapering step sizes

a_{n}^{(i)}

are used. The global model is shown to track an ODE with a forcing function equal to the weighted sum of the negative gradients of the individual clients. The weights being the limiting ratios

p^{(i)} = \lim_{n \to \infty} \frac{a_{n}^{(i)}}{a_{n}^{(1)}}

of the step sizes, where

a_{n}^{(1)} \geq a_{n}^{(i)}, \forall n

. Unlike the constant step sizes, the convergence here is with probability one.

In this framework, the clients with the larger p⁽ⁱ⁾ exert a greater influence on the global model than those with smaller p⁽ⁱ⁾, which can be used to favor clients that have rare and uncommon data. Numerical experiments were conducted to validate the convergence and demonstrate the choice of step-sizes for regulating the influence of the clients.

本文研究了随机逼近框架下的联邦学习问题。在这里，每个客户端i使用其数据集D(i)训练一个本地模型，并定期将模型参数wn(i)传输到中央服务器，在那里它们被聚合成一个全局模型参数w¯n并发送回来。客户通过使用全局模型参数重新初始化他们的局部模型来继续他们的训练。先前的工作通常假设客户端之间的步长（学习率）是恒定的（通常是相同的），用于模型训练。因此，聚合模型只在期望中收敛。在这项工作中，使用了特定于客户端的逐渐变细步长和(i)。全局模型显示跟踪一个ODE，其强迫函数等于单个客户端的负梯度的加权和。权值是阶跃大小的极限比p(i)=limn→∞和(i)和(1)，其中an(1)≥an(i),∀n。不像常数步长，这里的收敛概率是1。在该框架中，p(i)较大的客户比p(i)较小的客户对全局模型的影响更大，这可以用于支持具有稀有和不常见数据的客户。数值实验验证了该算法的收敛性，并证明了步长的选择可以调节客户端的影响。

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引用次数: 0

Adaptive joint-metric detection algorithm for efficient spectrum sensing: A deep-water case study 高效频谱感知的自适应联合度量检测算法：深水案例研究

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

Signal Processing

Pub Date : 2026-01-03 DOI: 10.1016/j.sigpro.2026.110490

Khadija Omar Mohammed, Liping Du, Yueyun Chen

Effective spectrum sensing in fading environments faces challenges due to correlated noise, strong multipath effects, and complex non-linear dependencies among received signals. Traditional eigenvalue-based detectors often assume independence or capture only limited forms of dependence, which reduces reliability in realistic conditions. This study proposes an Adaptive Joint Metric Detection Algorithm (AJMDA) that integrates both independent and dependency eigenvalue statistics into a unified framework. The independent metric represents the signal energy through the sum of eigenvalues, while the dependency metric captures the statistical structure using copula modeling with the Cramér–von Mises (CVM) goodness-of-fit test. An adaptive weighting factor balances these two metrics, and a generalized extreme value (GEV) model provides analytical threshold estimation. Simulation results under Rayleigh fading show that AJMDA significantly improves detection performance over classical energy detectors, eigenvalue-based GOF tests, and copula-only methods. At –15 dB SNR, the proposed detectors achieve a 45–50% higher detection probability, and at –10 dB SNR, they maintain a 20–60% gain, depending on the baseline. In ROC analysis, AJMDA achieves 10–25% higher performance

P_{d}

at low-to-moderate false-alarm levels, approaching the ideal vertical ROC curve.

衰落环境下的有效频谱感知面临着相关噪声、强多径效应和接收信号之间复杂的非线性依赖关系的挑战。传统的基于特征值的检测器通常假设独立性或只捕获有限形式的依赖性，这降低了现实条件下的可靠性。本文提出了一种自适应联合度量检测算法（AJMDA），该算法将独立和依赖特征值统计集成到一个统一的框架中。独立度量通过特征值的和表示信号能量，而依赖度量使用与cram - von Mises （CVM）拟合优度检验的copula建模来捕获统计结构。自适应加权因子平衡这两个度量，广义极值（GEV）模型提供分析阈值估计。Rayleigh衰落下的仿真结果表明，与经典能量检测器、基于特征值的GOF测试和纯copula方法相比，AJMDA检测性能有显著提高。在-15 dB信噪比下，所提出的检测器实现了45-50%的高检测概率，在-10 dB信噪比下，它们保持了20-60%的增益，具体取决于基线。在ROC分析中，AJMDA在中低虚警水平下的性能提高了10-25%，接近理想的垂直ROC曲线。

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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 : 2026-01-02 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

Distributed recursive linear fusion estimation for multi-sensor multi-rate systems with non-Gaussian noises 非高斯噪声下多传感器多速率系统的分布式递归线性融合估计

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

Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.sigpro.2026.110488

Zehua Sun, Shuli Sun

This paper is focused on the issue of distributed recursive linear fusion estimation in multi-sensor multi-rate linear discrete-time stochastic systems with non-Gaussian noises. The asynchronous sampling system is transformed into a synchronous sampling system through the pseudo-observation method. First, local filter in the minimum error entropy criterion is obtained at each sensor. Then, a distributed recursive linear fusion filter without feedback in the linear unbiased minimum variance criterion is presented based on local filters from all sensors. Estimation error cross-covariance matrices between local filters are derived. The proposed fusion filter is more accurate than the matrix-weighted fusion filter from local filters. Finally, to further improve the estimation accuracy, a distributed recursive linear fusion filter with feedback is presented, which avoids calculating cross-covariance matrices. The effectiveness of fusion algorithms is verified by simulations.

研究了具有非高斯噪声的多传感器多速率线性离散随机系统的分布式递归线性融合估计问题。通过伪观测方法将异步采样系统转化为同步采样系统。首先，在每个传感器处获得最小误差熵准则下的局部滤波器；然后，基于所有传感器的局部滤波器，提出了一种基于线性无偏最小方差准则的无反馈分布式递归线性融合滤波器。导出了局部滤波器间的估计误差交叉协方差矩阵。该融合滤波器比基于局部滤波器的矩阵加权融合滤波器精度更高。最后，为了进一步提高估计精度，提出了一种带反馈的分布式递归线性融合滤波器，避免了交叉协方差矩阵的计算。仿真结果验证了融合算法的有效性。

引用次数: 0

Constrained least total logistic distance metric algorithm for unanticipated signal truncation 非预期信号截断的约束最小总逻辑距离度量算法

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

Signal Processing

Pub Date : 2026-01-02 DOI: 10.1016/j.sigpro.2025.110478

Pengwei Wen , Botao Jin , Boyang Qu , Sheng Zhang , Xuzhao Chai

In practical engineering settings, operating conditions are seldom ideal: input signals are corrupted by noise, desired signals suffer interference, and measurements can be unanticipated truncated. These nonidealities reduce the effectiveness of standard adaptive algorithms and can lead to biased or unstable results. To address these challenges, this paper proposes a robust method called the unanticipated truncation-constrained least total logistic distance metric (UT-CLTLDM). The method combines a maximum likelihood approach with an expectation-maximization framework and a least total squares strategy to handle both input noise and signal truncation effectively. Simulation results show that the proposed algorithm achieves superior estimation accuracy and faster convergence compared to existing methods. Its effectiveness is further validated using chaotic input signals from Chua’s circuit model.

在实际的工程环境中，工作条件很少是理想的：输入信号被噪声破坏，期望的信号受到干扰，测量结果可能会意外截断。这些非理想性降低了标准自适应算法的有效性，并可能导致有偏差或不稳定的结果。为了解决这些挑战，本文提出了一种鲁棒方法，称为非预期截断约束最小总逻辑距离度量（UT-CLTLDM）。该方法将极大似然法与期望最大化框架和最小总二乘策略相结合，有效地处理了输入噪声和信号截断。仿真结果表明，与现有方法相比，该算法具有更高的估计精度和更快的收敛速度。利用蔡氏电路模型的混沌输入信号进一步验证了该方法的有效性。

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 : 2026-01-01 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

Simultaneous multiple high-precision beam scheduling for multitarget tracking 多目标同步高精度波束调度

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

Signal Processing

Pub Date : 2026-01-01 DOI: 10.1016/j.sigpro.2025.110486

Honghao Guang, Ratnasingham Tharmarasa, Thia Kirubarajan

Due to the enhanced beam flexibility and improved tracking performance, the colocated multiple-input multiple-output (MIMO) radar technology has been increasingly adopted in multifunction radar (MFR) systems. The use of narrow beamwidths in high-precision radar systems increases the likelihood of missed illuminations, violating conventional tracking assumptions and challenging existing beam scheduling methods. In order to address the narrow-beam problem, a Gaussian mixture (GM) filtering method is proposed, which refines the target state distribution using the information obtained from missed detections. Based on the proposed filter, a beam steering strategy is introduced to enable rapid target localization. To predict the tracking performance for multitarget tracking (MTT) with narrow beamwidths, the multiple hypothesis posterior Cramér-Rao lower bound (MH-PCRLB) is derived. Taking advantage of the proposed MH-PCRLB, the narrow-beam scheduling problem is formulated as a mathematical optimization. Simulation results demonstrate the superior performance of the proposed filtering method, beam steering strategy and the beam scheduling approach.

多输入多输出（MIMO）雷达技术由于具有增强的波束灵活性和改进的跟踪性能，在多功能雷达（MFR）系统中得到了越来越多的应用。在高精度雷达系统中使用窄波束宽度增加了错过照明的可能性，违反了传统的跟踪假设，并挑战了现有的波束调度方法。为了解决窄波束问题，提出了一种高斯混合滤波方法，该方法利用漏检得到的信息对目标状态分布进行细化。在此基础上，引入了波束导向策略，实现了目标的快速定位。为了预测窄波束多目标跟踪（MTT）的跟踪性能，推导了多假设后置cram - rao下界（MH-PCRLB）。利用所提出的MH-PCRLB，将窄波束调度问题表述为数学优化问题。仿真结果表明，所提出的滤波方法、波束转向策略和波束调度方法具有较好的性能。

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引用次数: 0

Tensor block-block terms decomposition for matrix-valued imaging applications 矩阵值成像应用的张量分块项分解

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

Signal Processing

Pub Date : 2026-01-01 DOI: 10.1016/j.sigpro.2025.110482

Saulo Cardoso Barreto, Julien Flamant, Sebastian Miron, David Brie

Matrix-valued images appear in many applications, ranging from polarimetric remote sensing to medical imaging. Such images can be represented as 4th-order tensors, where the first two dimensions correspond to spatial variables and the last two encode the matrix feature in each pixel. To efficiently analyze, decompose, and process these images, this paper considers the block-block terms decomposition (2BTD), a versatile low-rank tensor decomposition model that extends bilinear matrix factorization to 4th-order tensors by representing the latter as the sum of outer products of low-rank matrix blocks. Low-rank assumptions allow for a significantly reduced number of parameters to be estimated and enable the enforcement of key physical constraints on matrix sources. We establish both necessary and sufficient conditions for the uniqueness of the 2BTD model. To enable the use of 2BTD in covariance matrix-valued imaging, we develop an optimization framework that allows efficient handling of non-negativity and symmetry constraints together with low-rank assumptions on matrix blocks. Numerical experiments on synthetic and real data from Diffusion Tensor Imaging (DTI) illustrate the potential of the 2BTD model in matrix-valued imaging, as well as its effectiveness in practical settings.

矩阵值图像出现在许多应用中，从偏振遥感到医学成像。这样的图像可以表示为四阶张量，其中前两个维度对应于空间变量，后两个维度编码每个像素中的矩阵特征。为了有效地分析、分解和处理这些图像，本文考虑了块项分解（2BTD），这是一种通用的低秩张量分解模型，通过将双线性矩阵分解表示为低秩矩阵块的外积和，将双线性矩阵分解扩展到4阶张量。低秩假设允许大大减少需要估计的参数数量，并使对矩阵源的关键物理约束得以实施。建立了2BTD模型唯一性的充分必要条件。为了在协方差矩阵值成像中使用2BTD，我们开发了一个优化框架，该框架允许有效处理非负性和对称约束以及矩阵块上的低秩假设。利用扩散张量成像（DTI）的合成数据和真实数据进行的数值实验表明，2BTD模型在矩阵值成像中的潜力及其在实际环境中的有效性。

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引用次数: 0

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