Signal Processing最新文献_第2页

3D angles-only target tracking in the presence of spatiotemporal bias and sensor position error 存在时空偏差和传感器位置误差的三维纯角度目标跟踪

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

Signal Processing

Pub Date : 2026-01-08 DOI: 10.1016/j.sigpro.2026.110497

Bingyi Ren , Tianyi Jia , Hongwei Liu , Chang Gao , Hongtao Su , Chunlei Zhao

The spatial bias of sensor in the process of target tracking and the temporal bias between the time axis of each sensor and the absolute time axis, if not accounted for, can seriously affect the positioning accuracy. Meanwhile, the sensor position reported by Global Positioning System (GPS) is not accurate. In this paper, the problem of angles-only target motion analysis (TMA) by asynchronous sensors is studied in the presence of spatiotemporal bias and sensor position error. A new target tracking method is proposed by taking the target state, spatiotemporal bias and sensor position as the augmented state vector. Using the filter concept and the minimum mean square error (MMSE) criterion for real-time processing, the augmented state vector can be estimated simultaneously. Simulation results show the superiority of the proposed algorithm for target position estimation, and verify the effectiveness of the proposed in achieving the Posterior Cramér-Rao lower bound (PCRLB) performance under the distance-dependent measurement noise.

传感器在目标跟踪过程中的空间偏差以及各传感器的时间轴与绝对时间轴之间的时间偏差，如果不加以考虑，会严重影响定位精度。同时，全球定位系统（GPS）报告的传感器位置不准确。本文研究了存在时空偏差和传感器位置误差的异步传感器单角度目标运动分析问题。提出了一种以目标状态、时空偏差和传感器位置为增广状态向量的目标跟踪方法。利用滤波概念和最小均方误差（MMSE）准则进行实时处理，可以同时估计增广状态向量。仿真结果表明了该算法在目标位置估计方面的优越性，并验证了该算法在距离相关测量噪声下实现后验cram - rao下界（PCRLB）性能的有效性。

引用次数: 0

Robust angle estimation in MIMO radar under impulsive noise via fast bayesian tensor decomposition with intra-dimension correlation 基于维内相关的快速贝叶斯张量分解的脉冲噪声下MIMO雷达鲁棒角度估计

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

Signal Processing

Pub Date : 2026-01-08 DOI: 10.1016/j.sigpro.2026.110492

Jinli Chen , Yang Song , Hua Shao , Jiaqiang Li

Conventional angle estimation methods are highly sensitive to outliers, causing severe performance degradation under impulsive noise. Although existing tensor-based Bayesian approaches can alleviate the impact of outliers, strongly impulsive noise in multiple-input multiple-output (MIMO) radar often leads to outlier model mismatch, reducing robustness against outliers. To address this, we propose a fast Bayesian method for angle estimation under impulsive noise, which exploits the tensor intra-dimension correlations and incorporates the Vandermonde structure of factor matrices within a Bayesian tensor decomposition framework. Strong outliers in the array measurements are first removed via thresholding to mitigate model mismatch. A hierarchical probabilistic model based on canonical polyadic (CP) decomposition is then developed to capture the correlation structure and the Vandermonde structural prior. Model parameters are efficiently inferred via an expectation–maximization (EM) algorithm, which recovers missing entries caused by thresholding and suppresses residual outliers. Furthermore, a complexity-reduction method is developed to accelerate computation by employing a snapshot-wise stackable strategy and leveraging the sparsity of thresholded entries, enabling efficient estimation of factor matrices across multiple snapshots. Finally, DOAs and DODs are jointly estimated from the decomposed factor matrices. Simulations verify the outlier-robust performance of the proposed method in providing high-accuracy angle estimation under impulsive noise.

传统的角度估计方法对异常值非常敏感，在脉冲噪声下会导致性能严重下降。虽然现有的基于张量的贝叶斯方法可以减轻异常值的影响，但多输入多输出（MIMO）雷达中强烈的脉冲噪声经常导致异常值模型失配，降低了对异常值的鲁棒性。为了解决这个问题，我们提出了一种快速的贝叶斯方法用于脉冲噪声下的角度估计，该方法利用了张量的维内相关性，并在贝叶斯张量分解框架内结合了因子矩阵的Vandermonde结构。阵列测量中的强异常值首先通过阈值去除，以减轻模型不匹配。然后建立了基于正则多进（CP）分解的分层概率模型来捕获相关结构和Vandermonde结构先验。通过期望最大化（EM）算法有效地推断模型参数，该算法恢复阈值导致的缺失条目并抑制残差异常值。此外，开发了一种复杂性降低方法，通过采用快照可堆叠策略和利用阈值条目的稀疏性来加速计算，从而实现跨多个快照对因子矩阵的有效估计。最后，从分解的因子矩阵中联合估计doa和DODs。仿真结果验证了该方法在脉冲噪声下提供高精度角度估计的异常鲁棒性。

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引用次数: 0

From structure to detail: A conditional diffusion framework for extremely low-bitrate image compression 从结构到细节：用于极低比特率图像压缩的条件扩散框架

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

Signal Processing

Pub Date : 2026-01-08 DOI: 10.1016/j.sigpro.2025.110480

Junhui Li, Yiyang Zou, Xingsong Hou, Yutao Zhang, Zhixuan Guo

Although existing diffusion-based methods produce visually rich textures at extremely low bitrates, they often sacrifice structural fidelity, resulting in significant deviations from the original image. To address this fundamental trade-off, we propose Fidelity-Perception Diffusion-based Image Compression (FPD-IC), a two-stage conditional diffusion framework that explicitly separates structure reconstruction and detail restoration. In Stage I, we use a VAE-based compressor to recover structurally faithful conditional images from highly compact bitstreams. In Stage II, a diffusion model, guided by the output from Stage I, generates visually rich details. This conditional approach allows the diffusion model to focus exclusively on perceptual enhancement while preserving the overall structure established in Stage I. Additionally, we introduce a lightweight Fidelity-Perception Tuner Module (FPTM) to combine the outputs of both stages, enabling controllable trade-offs between fidelity and perceptual quality. Extensive experiments on the Kodak and Tecnick datasets demonstrate the effectiveness and robustness of FPD-IC. On the Tecnick dataset, FPD-IC outperforms state-of-the-art diffusion-based methods by 2.24–3.57 dB in PSNR at bitrates below 0.06 bpp, while also achieving superior perceptual quality. Furthermore, FPD-IC shows strong robustness to input noise, consistently maintaining high fidelity and perceptual quality under Gaussian perturbations. The code will be released at https://github.com/mlkk518/FPD-IC.

尽管现有的基于扩散的方法以极低的比特率产生视觉上丰富的纹理，但它们往往会牺牲结构保真度，导致与原始图像的显著偏差。为了解决这一基本问题，我们提出了基于保真度感知扩散的图像压缩（FPD-IC），这是一个明确分离结构重建和细节恢复的两阶段条件扩散框架。在第一阶段，我们使用基于vae的压缩器从高度紧凑的比特流中恢复结构忠实的条件图像。在第二阶段，扩散模型，由第一阶段的输出引导，产生视觉上丰富的细节。这种有条件的方法允许扩散模型专注于感知增强，同时保留阶段i中建立的整体结构。此外，我们引入了一个轻量级的保真度-感知调谐模块（FPTM）来组合两个阶段的输出，从而在保真度和感知质量之间实现可控的权衡。在Kodak和Tecnick数据集上的大量实验证明了FPD-IC的有效性和鲁棒性。在Tecnick数据集上，在比特率低于0.06 bpp的情况下，FPD-IC的PSNR比最先进的基于扩散的方法高出2.24-3.57 dB，同时也实现了卓越的感知质量。此外，FPD-IC对输入噪声具有很强的鲁棒性，在高斯扰动下始终保持高保真度和感知质量。代码将在https://github.com/mlkk518/FPD-IC上发布。

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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 : 2026-01-07 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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引用次数: 0

A weighted coherent integration method for weak target detection based on active-passive radar 一种基于主-被动雷达的弱目标检测加权相干积分方法

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

Signal Processing

Pub Date : 2026-01-07 DOI: 10.1016/j.sigpro.2026.110495

Boyang Jia , Jianwei Zhao , Yaxing Yue , Xuepan Zhang , Jiayi Zhao , Sining Liu , Guisheng Liao

We propose a Weighted Coherent Integration (WCI) algorithm for weak target detection. This method aims to achieve maximum signal-to-noise ratio (SNR) gain by coherently accumulating echoes from active and passive radars. Two key challenges about range-Doppler misalignment and inter-channel differences are addressed by a two-stage framework. First, spatial alignment is achieved by partitioning the surveillance area, and Doppler resolutions are unified via adaptive integration time. Target fluctuations and phase errors between channels are then mitigated via inter-channel coherent integration with phase compensation. Simulation results validate that WCI outperforms conventional monostatic and multistatic non-coherent fusion methods, achieving superior detection capability in both low-SNR and multi-target scenarios.

提出了一种用于弱目标检测的加权相干积分（WCI）算法。该方法旨在通过相干积累主动式和无源雷达回波，实现最大信噪比增益。两阶段框架解决了距离-多普勒失调和信道间差异的两个关键挑战。首先，通过划分监视区域实现空间对准，并通过自适应积分时间统一多普勒分辨率；然后通过带相位补偿的信道间相干积分来减轻信道间的目标波动和相位误差。仿真结果验证了WCI优于传统的单静态和多静态非相干融合方法，在低信噪比和多目标场景下都具有出色的检测能力。

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 : 2026-01-05 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

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