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A Large-Scale Diverse GNSS/SINS Dataset: Construction, Publication, and Application 大规模多样化 GNSS/SINS 数据集:构建、出版和应用
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-30 DOI: 10.1109/TIM.2024.3488156
Feng Zhu;Xi Chen;Qinqing Cai;Xiaohong Zhang
High-precision continuous position and attitude determination are the critical modules of mobile mapping and autonomous driving (AD). Research in the integration of Global Navigation Satellite System (GNSS) and strapdown inertial navigation system (SINS) has greatly enhanced the accuracy and robustness of position and attitude in different scenes. However, the complexity and variability of the real scenes are still challenging for the existing models, parameters, strategies, and algorithms (MPSA). It is worth noting that high-quality datasets are key to accelerating the research and development of MPSA, which has been proved in the computer vision (CV) fields represented by the ImageNet dataset. Unfortunately, current public datasets either do not provide the raw observations of GNSS and inertial measurement unit (IMUs) or are not collected in abundant scenes and moving platforms. Therefore, a large-scale diverse GNSS/SINS dataset, named SmartPNT-POS, is presented. This dataset covers rich real-world environments, such as open-sky and complex urban, and multiple moving platforms, such as aircraft, land vehicles, and ships. In addition, different types of IMUs, including those manufactured in Hexagon, iMAR Navigation GmbH, and Honeywell, are contained in SmartPNT-POS as well. Moreover, it provides ground truths in each group of data for users to analyze and evaluate their MPSA. Now, the dataset is publicly available through Kaggle, a data science community, and the website to obtain the dataset is provided in the text. There have been 30 sets of data published on the website up to the present, and comprehensive analyses have been made in this contribution for the position and attitude determination results obtained by different processing modes. More data will be collected for different environments and applications and published on the same website in the future.
高精度连续位置和姿态确定是移动测绘和自动驾驶(AD)的关键模块。全球导航卫星系统(GNSS)和带下惯性导航系统(SINS)的集成研究大大提高了不同场景中位置和姿态的精度和鲁棒性。然而,真实场景的复杂性和多变性对现有的模型、参数、策略和算法(MPSA)仍是一个挑战。值得注意的是,高质量的数据集是加速 MPSA 研究和开发的关键,这一点已在以 ImageNet 数据集为代表的计算机视觉(CV)领域得到证实。遗憾的是,目前的公共数据集要么没有提供全球导航卫星系统和惯性测量单元(IMU)的原始观测数据,要么没有在丰富的场景和移动平台中收集。因此,我们提出了一个名为 SmartPNT-POS 的大规模多样化 GNSS/SINS 数据集。该数据集涵盖了丰富的真实世界环境,如开阔天空和复杂城市,以及多种移动平台,如飞机、陆地车辆和船舶。此外,SmartPNT-POS 还包含不同类型的 IMU,包括 Hexagon、iMAR Navigation GmbH 和 Honeywell 生产的 IMU。此外,SmartPNT-POS 还在每组数据中提供了地面实况,供用户分析和评估其 MPSA。目前,该数据集已通过数据科学社区 Kaggle 公开发布,文中提供了获取该数据集的网站。截至目前,该网站已发布了 30 组数据,本文对不同处理模式下获得的位置和姿态确定结果进行了全面分析。今后还将针对不同环境和应用收集更多数据,并在同一网站上公布。
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引用次数: 0
Compressive-Sensing Reconstruction for Satellite Monitor Data Using a Deep Generative Model 利用深度生成模型对卫星监测数据进行压缩传感重构
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-29 DOI: 10.1109/TIM.2024.3485429
Zeyu Gu;Gang Tang;Jianwei Ma
The mechanical and electrical performance degradation of satellite components has a serious impact on imaging. How to perform high-precision reconstruction of the monitor data from compressive-sensing (CS) data (limited by online computing, storage and transmission) is often the first stage in the fault diagnosis of in-orbit satellites. In this article, a deep generative model named denoising diffusion probabilistic model (DDPM) is applied for the equipment monitor data reconstruction. The priors-assisted reconstruction method is useful for reducing reconstruction error and decreasing measurement/monitor cost. The reconstruction method mainly consists of unconditional generation transition from pre-trained DDPM noise matching network and conditional likelihood correction step toward downsampling data. An inverse time decay technique is embedded into step size strategy of gradient computation to ensure data consistency. As an unsupervised learning paradigm, the learned deep generative priors can be utilized for measurements with different compressive sampling ratio (CSR) like plug-and-play prior. Numerical experiments executed on control moment gyro (CMG) data and reciprocating refrigeration compressor (RRC) data validate the effectiveness of the new method, in comparison with conventional sparse prior methods and advanced deep learning reconstruction methods. Finally, we conduct out-of-distribution (OOD) generalization experiments on fault working condition, which demonstrates the DDPM priors-assisted data reconstruction method are suitable for different operating conditions.
卫星部件的机械和电气性能退化对成像有严重影响。如何从压缩传感(CS)数据(受限于在线计算、存储和传输)中对监测器数据进行高精度重构,往往是在轨卫星故障诊断的第一阶段。本文将一种名为去噪扩散概率模型(DDPM)的深度生成模型用于设备监控数据的重建。先验辅助重构方法有助于减少重构误差和降低测量/监测成本。重建方法主要包括从预训练的 DDPM 噪声匹配网络的无条件生成过渡和向下采样数据的条件似然校正步骤。梯度计算的步长策略中嵌入了反时间衰减技术,以确保数据的一致性。作为一种无监督学习范例,学习到的深度生成先验可用于不同压缩采样率(CSR)的测量,就像即插即用先验一样。在控制力矩陀螺仪(CMG)数据和往复式制冷压缩机(RRC)数据上进行的数值实验验证了新方法的有效性,并与传统的稀疏先验方法和先进的深度学习重构方法进行了比较。最后,我们对故障工况进行了分布外(OOD)泛化实验,证明了 DDPM 先验辅助数据重建方法适用于不同的运行工况。
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引用次数: 0
An Uncertainty Quantification and Calibration Framework for RUL Prediction and Accuracy Improvement 用于 RUL 预测和提高精度的不确定性量化和校准框架
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-29 DOI: 10.1109/TIM.2024.3485392
Ze-Qi Ding;Qiang Qin;Yi-Fan Zhang;Yan-Hui Lin
In prognostic and health management (PHM), predicting remaining useful life (RUL) and quantifying the uncertainties in predictions are necessary. This article proposes a Gaussian process (GP) autoregression-variational autoencoder (GPVAE) framework that can predict RUL based on degradation data, quantify predictive uncertainty, decompose this uncertainty into epistemic and aleatory types, and further quantify epistemic uncertainties on RUL-related features. Subsequently, uncertainty calibration is proposed to ensure that the quantified uncertainty matches the actual error of the model. The calibrated uncertainty is used for out-of-distribution (OOD) detection and active learning for the labeled and unlabeled data, which can improve the RUL prediction accuracy with limited computational resources and limited cost of degradation tests for obtaining RUL labels. The effectiveness of the proposed method is illustrated by the case study on lithium-ion batteries dataset.
在预报和健康管理(PHM)中,预测剩余使用寿命(RUL)和量化预测中的不确定性是必要的。本文提出了一个高斯过程(GP)自回归-变异自编码器(GPVAE)框架,该框架可以根据退化数据预测剩余使用寿命,量化预测的不确定性,将这种不确定性分解为认识型和不确定型,并进一步量化剩余使用寿命相关特征的认识型不确定性。随后,提出了不确定性校准建议,以确保量化的不确定性与模型的实际误差相匹配。校准后的不确定性用于对已标注和未标注数据进行分布外(OOD)检测和主动学习,从而在有限的计算资源和获取 RUL 标签的降解测试成本有限的情况下提高 RUL 预测的准确性。通过对锂离子电池数据集的案例研究,说明了所提方法的有效性。
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引用次数: 0
A High-Accuracy Ultrasonic Gas Flowmeter Based on Scandium-Doped Aluminum Nitride Piezoelectric Micromachined Ultrasonic Transducers 基于掺钪氮化铝压电微机械超声波传感器的高精度超声波气体流量计
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-28 DOI: 10.1109/TIM.2024.3481593
Hanzhe Liu;Xiangyang Wang;Chongbin Liu;Yuzhe Lin;Lujiang Zhang;Hui Zhao;Xiaofei Cui;Jianke Feng;Guoqiang Wu;Jifang Tao
This article presents an ultrasonic gas meter based on a scandium-doped aluminum nitride (Sc0.2Al0.8N) piezoelectric micromachined ultrasonic transducer (PMUT) array, where the characteristic dimension of each PMUT cell is only $600~mu text {m}$ . The ultrasonic time-of-flight (TOF) difference method is employed to measure the gas flow rate, followed by ultrasonic signal processing and subsequent velocity profile and temperature compensation performed by the microcontroller. The cross correlation method is employed to detect the ultrasonic TOF difference and further suppress noise. To conduct a feasibility evaluation of the designed ultrasonic gas meter, the PMUT array, system control circuit, and ultrasonic flow channel are combined and encapsulated in a meter case. Results indicate that the ultrasonic gas meter exhibits outstanding accuracy, repeatability, and temperature adaptability. In the flow range of 0.025–2.8 m3/h, the minimum mean error and minimum repeatability error are 0.11% and 0.13%, respectively. Even when the temperature reaches 323.15 K, the designed device can achieve a mean error of no more than 0.41% with temperature compensation, which is comparable to commercialized ultrasonic gas meters. The highly reliable ultrasonic gas meter presented in this article will provide a feasible solution for advancing portable devices.
本文介绍了一种基于掺钪氮化铝(Sc0.2Al0.8N)压电微机械超声换能器(PMUT)阵列的超声波气体流量计,每个 PMUT 单元的特征尺寸仅为 600~mutext {m}$ 美元。采用超声波飞行时间(TOF)差分法测量气体流速,然后由微控制器进行超声波信号处理和速度曲线及温度补偿。交叉相关法用于检测超声波 TOF 差值并进一步抑制噪声。为了对所设计的超声波气体流量计进行可行性评估,将 PMUT 阵列、系统控制电路和超声波流道组合并封装在流量计外壳中。结果表明,超声波气体流量计具有出色的精度、重复性和温度适应性。在 0.025-2.8 m3/h 的流量范围内,最小平均误差和最小重复性误差分别为 0.11% 和 0.13%。即使温度达到 323.15 K,所设计的装置在进行温度补偿后,平均误差也不超过 0.41%,与商用超声波气体流量计相当。本文介绍的高可靠性超声波气体流量计将为推进便携式设备的发展提供可行的解决方案。
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引用次数: 0
Suppression of Heading Error in Bell-Bloom Atomic Magnetometer by Controlling RF Magnetic Field 通过控制射频磁场抑制钟罩式原子磁力计的方向误差
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-28 DOI: 10.1109/TIM.2024.3485395
Jun Zhu;Liwei Jiang;Jiali Liu;Xin Zhao;Chi Fang;Qi Shao;Yuntian Zou;Zhuo Wang
The atomic magnetometers operated in Earth-scale magnetic field are susceptible to the nonlinear Zeeman (NLZ) effect, resulting in multiple resonance peaks and heading error, which restricts their practical applications. We introduce a spin-locking method based on magnetic field modulation to overcome the NLZ effect and thus suppress the heading error in atomic magnetometers. The suppression effect of spin-locking is proportional to the amplitude of the modulation field. However, an excessively high modulation field amplitude can lead to broadening of the measurement linewidth. A novel model characterizing the linewidth for the amplitude of modulated magnetic field under different environmental magnetic field is established by considering the NLZ effect. From the test results, the novel model can more accurately predict the linewidth under different environmental magnetic fields compared with traditional models. The optimized amplitude of modulated magnetic field is obtained based on the linewidth model, and the heading error is suppressed by about 80% within the magnetic field inclination angle of 28.76°. The theory and method presented here are important for the application of magnetometers in Earth-scale magnetic field, which can suppress the heading error while keeping the linewidth unchanged.
在地球尺度磁场中运行的原子磁强计容易受到非线性泽曼效应(NLZ)的影响,从而产生多个共振峰和方向误差,限制了其实际应用。我们引入了一种基于磁场调制的自旋锁定方法,以克服非线性泽曼效应,从而抑制原子磁强计的航向误差。自旋锁定的抑制效果与调制场的振幅成正比。然而,过高的调制场振幅会导致测量线宽变宽。考虑到 NLZ 效应,我们建立了一个新模型来描述不同环境磁场下调制磁场幅值的线宽。从测试结果来看,与传统模型相比,新模型能更准确地预测不同环境磁场下的线宽。根据线宽模型得到了优化的调制磁场幅值,在 28.76° 的磁场倾角范围内,航向误差被抑制了约 80%。本文提出的理论和方法对于磁强计在地球尺度磁场中的应用具有重要意义,它可以在保持线宽不变的情况下抑制航向误差。
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引用次数: 0
MARFPNet: Multiattention and Adaptive Reparameterized Feature Pyramid Network for Small Target Detection on Water Surfaces MARFPNet:用于水面小目标检测的多注意力和自适应重参数化特征金字塔网络
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-28 DOI: 10.1109/TIM.2024.3485463
Quanbo Ge;Wenjing Da;Mengmeng Wang
The images captured by unmanned aerial vehicles (UAVs) are often limited in scale and feature information, making it challenging for current detection algorithms to learn the features of objects effectively. This limitation hampers accurate identification of small objects on water surfaces. We introduce a multiattention and adaptive reparameterized feature pyramid network for small target detection on water surfaces (MARFPNet) to tackle this issue. First, to address the loss of small object features during extraction, we improved the attention mechanism based on the characteristics of small objects and proposed a multiattention module, integrating it into the feature extraction process. Second, to address the semantic information of small objects being retained mostly in shallow feature maps and not fully utilized, we introduced an adaptive reparameterized generalized feature pyramid network (Adaptive_RepGFPN). This module reorganizes features, expands the fusion scale, and incorporates adaptive weighting in the concat operation. Third, to overcome the challenge of ineffective restoration of feature map information by upsampling, we introduce the Dysample. Finally, to address the problem of the loss function being sensitive to scale changes, we propose the normalized Wasserstein distance (NWD) loss function to reduce the sudden drop in loss due to scale changes. We conducted experiments on VisDrone, SeaDronsSee, and the self-build dataset. MARFPNet showed higher accuracy compared to other detection algorithms. Notably, on the self-build dataset, mAP50 and mAP50:95 improved by 9.1% and 3.5% over the baseline network. This demonstrates MARFPNet’s effectiveness and suitability for detecting small targets in drone aerial photography on water surfaces.
无人驾驶飞行器(UAV)拍摄的图像通常在比例和特征信息方面受到限制,使得目前的检测算法难以有效地学习物体的特征。这种限制阻碍了对水面上小物体的准确识别。为解决这一问题,我们引入了一种用于水面小目标检测的多关注和自适应重参数化特征金字塔网络(MARFPNet)。首先,针对小目标特征提取过程中的损失,我们根据小目标的特点改进了注意机制,提出了多注意模块,并将其集成到特征提取过程中。其次,针对小物体的语义信息大多保留在浅层特征图中而未被充分利用的问题,我们引入了自适应重参数广义特征金字塔网络(Adaptive_RepGFPN)。该模块重组了特征,扩大了融合规模,并在连接操作中加入了自适应加权。第三,为了克服上采样无法有效还原特征图信息的难题,我们引入了 Dysample。最后,为了解决损失函数对尺度变化敏感的问题,我们提出了归一化瓦瑟斯坦距离(NWD)损失函数,以减少尺度变化造成的损失骤降。我们在 VisDrone、SeaDronsSee 和自建数据集上进行了实验。与其他检测算法相比,MARFPNet 显示出更高的准确性。值得注意的是,在自建数据集上,mAP50 和 mAP50:95 比基准网络分别提高了 9.1% 和 3.5%。这证明了 MARFPNet 在检测无人机航拍水面小目标方面的有效性和适用性。
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引用次数: 0
Prototypical Contrastive Domain Adaptation Network for Nonstationary EEG Classification 用于非稳态脑电图分类的原型对比域自适应网络
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-28 DOI: 10.1109/TIM.2024.3476618
Donglin Li;Jiacan Xu;Yuxian Zhang;Dazhong Ma;Jianhui Wang
The identification of electroencephalography (EEG) signals’ cross sessions and subjects remains challenging due to the variability of data caused by extraneous factors and individual differences in EEG signals. Existing domain-adaptive transfer methods using cross-domain labeled samples for classification are too coarse and could lead to negative transfer problems. To solve this problem, we propose a prototypical contrastive domain adaptation (PCDA) network in this article. First, we align the data from different domains to reduce the data distribution differences for supporting the subsequent model construction. Then, a conditional domain adversarial network is used in the feature extraction stage to achieve domain alignment and learn deep feature representations. Second, we propose a scoring method to equivalently quantify the similarity of data from different domains using resting-state data and select similar source domain data to fine-tune the model. Finally, we propose a prototypical contrastive (PC) learning module. In-domain PC learning captures and compares the category-wise semantic structure of the data and the learned representations to enable the clustering of similar features. Cross-domain PC learning encodes and compares the semantic structure in shared embedding space to enable self-supervised feature alignment and reduce negative transfer. The experimental results show that the PCDA network achieves better results on the datasets of brain-computer interface (BCI) Competition IV II-a and II-b, and the ablation experiments validate the efficacy of the method.
由于外在因素和脑电信号的个体差异造成数据的多变性,对跨时段和跨受试者的脑电信号进行识别仍具有挑战性。现有的使用跨领域标记样本进行分类的领域自适应转移方法过于粗糙,可能导致负转移问题。为解决这一问题,我们在本文中提出了一种原型对比域自适应(PCDA)网络。首先,我们对来自不同领域的数据进行对齐,以减少数据分布差异,从而为后续的模型构建提供支持。然后,在特征提取阶段使用条件域对抗网络实现域对齐并学习深度特征表征。其次,我们提出了一种评分方法,利用静息态数据等效量化不同领域数据的相似性,并选择相似的源领域数据对模型进行微调。最后,我们提出了一个原型对比(PC)学习模块。域内 PC 学习捕捉并比较数据的类别语义结构和学习到的表征,以实现相似特征的聚类。跨域 PC 学习对共享嵌入空间中的语义结构进行编码和比较,以实现自我监督的特征对齐并减少负迁移。实验结果表明,PCDA 网络在脑机接口(BCI)竞赛 IV II-a 和 II-b 的数据集上取得了更好的结果,而消融实验则验证了该方法的有效性。
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引用次数: 0
High-Throughput Thermophysical Characterization of Semiconductors 半导体的高通量热物理特性分析
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-25 DOI: 10.1109/TIM.2024.3485440
Shaojie Zhou;Yali Mao;Yunliang Ma;Guoliang Ma;Chao Yuan
Pump–probe thermoreflectance (Pump–probe TR) is a noncontact detection technique that has been widely used for thermal characterization of materials. In the traditional characterization process, spot detection is usually employed to fit unknown thermal property parameters using a nonlinear fitting process. However, when processing a large amount of data in a specified area of a sample, the traditional measurement process appears to be time-consuming and labor-intensive. In this work, we propose a high-throughput method for semiconductor thermophysical characterization. The optical path of the measurement system is combined with automatic control components to realize automatic scanning measurements. Deep learning techniques are utilized for high-throughput data processing. We first demonstrated the entire measuring process with a Au–sapphire sample, whose interlayers are intentionally controlled by coating different interlayers. The validity of the method can be demonstrated by the measurement results of the thermal boundary conductance (TBC) of Au–sapphire and the thermal conductivity (TC) of sapphire in the scanned area. Then, we demonstrated the application of nondestructive scanning measurement in the industrial production of GaN-on-Si samples, comparing the measurement results at different resolutions. We validate the scanning results demonstrating that this method can measure with high accuracy and speed. Meanwhile, the high-resolution scanning measurement can observe the subtle difference in thermal characterization in the area. This method significantly reduces the time and labor required to measure compared to traditional methods and it is particularly efficient for thermophysical characterization detection of high-volume wafers.
泵浦探针热反射(Pump-probe TR)是一种非接触式检测技术,已广泛应用于材料的热特性分析。在传统的表征过程中,通常采用光斑检测,利用非线性拟合过程来拟合未知的热特性参数。然而,在处理样品指定区域内的大量数据时,传统的测量过程显得费时费力。在这项工作中,我们提出了一种用于半导体热物理特性分析的高通量方法。测量系统的光路与自动控制组件相结合,实现了自动扫描测量。深度学习技术被用于高通量数据处理。我们首先用金蓝宝石样品演示了整个测量过程,该样品的夹层是通过涂覆不同的夹层有意控制的。扫描区域内金蓝宝石热边界电导率(TBC)和蓝宝石热导率(TC)的测量结果证明了该方法的有效性。然后,我们展示了无损扫描测量在硅基氮化镓样品工业生产中的应用,并比较了不同分辨率下的测量结果。我们对扫描结果进行了验证,证明这种方法可以进行高精度、高速度的测量。同时,高分辨率扫描测量可以观察到该区域热特性的细微差别。与传统方法相比,这种方法大大减少了测量所需的时间和人力,尤其适用于大批量晶片的热物理特性检测。
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引用次数: 0
Motion-Induced Error Reduction for Motorized Digital Fringe Projection System 减少电动数字边缘投影系统的运动误差
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-25 DOI: 10.1109/TIM.2024.3481542
Sanghoon Jeon;Hyo Geon Lee;Jae Sung Lee;Bo Min Kang;Byung Wook Jeon;Jun Young Yoon;Jae-Sang Hyun
In phase-shifting profilometry (PSP), errors can be introduced by any motion during the acquisition of fringe patterns, as it assumes both the object and the measurement system are stationary. To address this issue, we propose a pixel-wise motion-induced error reduction method when the measurement system is in motion due to a motorized system. Our proposed method introduces a novel motion-attentive phase-shifting algorithm and leverages the motor’s encoder and the pinhole model of the camera and projector. It enables accurate 3-D shape measurement with only three fringe patterns, leveraging the geometric constraints of the digital fringe projection system. We address the mismatch problem due to motion-induced camera pixel disparities and reduce phase shift errors. These processes are easy to implement and require low computational cost. Experimental results demonstrate that the presented method effectively reduces errors even in nonuniform 3-D motion.
在相移轮廓测量法(PSP)中,由于假定物体和测量系统都是静止的,因此在采集条纹图案的过程中,任何运动都可能带来误差。为了解决这个问题,我们提出了一种在电动系统导致测量系统运动时减少像素运动引起的误差的方法。我们提出的方法引入了一种新颖的运动注意移相算法,并利用了电机编码器以及相机和投影仪的针孔模型。它利用数字条纹投影系统的几何约束,只需三个条纹图案就能实现精确的三维形状测量。我们解决了由运动引起的相机像素不匹配问题,并减少了相移误差。这些过程易于实现,计算成本较低。实验结果表明,所提出的方法即使在非均匀三维运动中也能有效减少误差。
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引用次数: 0
Data-Driven Optimization Strategy of Microphone Array Configurations in Vehicle Environments 车辆环境中麦克风阵列配置的数据驱动优化策略
IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-25 DOI: 10.1109/TIM.2024.3485461
Lehai Liu;Fengrong Bi;Jiewei Lin;Tongtong Qi;Xin Li
Microphone array (MA) speech enhancement is a crucial component of vehicle intelligence. However, the complex acoustic environments and the spatial constraints of array layouts present challenges for the design and implementation of MAs in intelligent vehicles. This study proposes a data-driven optimization strategy for constructing the optimal MA configuration in-vehicle environments. We first developed a novel in-vehicle noise model that considers azimuth and elevation angles by defining a search region for microphone elements in a plane. Subsequently, based on the in-vehicle noise model, we conducted sound field modeling to ensure the designed MA is compatible with the complex acoustic environments inside vehicles. Utilizing this sound field model, we formulated a specialized optimization algorithm to devise the optimal configuration of the MA. Finally, the designed array configuration was constructed using an MEMS MA acquisition system, and the array performance was evaluated in real driving environments. Compared to conventional MA configurations, comprehensive experiments indicate that the designed MA enhances performance by increasing the short-time objective intelligibility (STOI) scores by 13.9%, improving the output signal-to-noise ratio (SNR) levels by 53.3%, and ensuring robustness in complex in-vehicle acoustic environments.
麦克风阵列(MA)语音增强是车辆智能的重要组成部分。然而,复杂的声学环境和阵列布局的空间限制给智能车辆中麦克风阵列的设计和实施带来了挑战。本研究提出了一种数据驱动的优化策略,用于构建车内环境中的最佳 MA 配置。我们首先开发了一种新颖的车内噪声模型,通过在平面上定义麦克风元件的搜索区域来考虑方位角和仰角。随后,在车内噪声模型的基础上,我们进行了声场建模,以确保所设计的 MA 与复杂的车内声学环境相兼容。利用该声场模型,我们制定了专门的优化算法来设计 MA 的最佳配置。最后,使用 MEMS MA 采集系统构建了设计的阵列配置,并在实际驾驶环境中对阵列性能进行了评估。综合实验表明,与传统的 MA 配置相比,所设计的 MA 性能提高了 13.9%,输出信噪比(SNR)水平提高了 53.3%,并确保了在复杂的车内声学环境中的鲁棒性。
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引用次数: 0
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IEEE Transactions on Instrumentation and Measurement
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