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Efficient Reduction of Powerline signals in Magnetic data Acquired From a Moving Platform

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第8期59卷 7137-7146页

作者： Kass, M. Andy Christiansen, Anders Vest Auken, Esben Larsen, Jakob Juul Aarhus Univ Hydrogeophys Grp DK-8000 Aarhus Denmark Aarhus Univ Dept Engn DK-8200 Aarhus Denmark

High-bandwidth magnetic data are normally distorted by ubiquitous 50- or 60-Hz noise from powerlines and similar sources. The powerline noise can be orders of magnitude larger than the magnetic signal from targets and must be culled from data sets prior to interpretation. Suppression of the powerline noise by simple filtering can result in artifacts and an unacceptable reduction in resolution of ground-based and unmanned aerial vehicle magnetic surveys. Removal approaches such as those based on Biot-Savart modeling are sensitive to the estimated position of the powerline systems, in addition to their limited applicability due to the requirement of a DC source. Moreover, the powerline noise in data acquired from a moving platform is inherently nonstationary and removal techniques must be specifically developed with this in mind. We propose a model-based method that does not rely on a priori knowledge of the powerline system by fitting and subtracting a set of sinusoids to the data. These sinusoids are computed on small windows of data, tied together with regularization terms within the fitting process to reduce discontinuities between segments. We further incorporate powerline frequency as a nonlinear parameter, allowing for fluctuations in the fundamental frequency as loads on the power grid change. Through synthetic and field examples, we show that periodic noise can be reliably removed automatically without the need for filtering or significant alterations of the frequency content. Powerline noise is reduced by over 98% in the field example.

关键词： Magnetic survey powerline noise removal signal processing

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基于频域多通道图特征感知的海面小目标检测

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电子与信息学报 2023年第5期45卷 1567-1574页

作者：许述文焦银萍白晓惠蒋俊正西安电子科技大学雷达信号处理国家重点实验室西安710071 桂林电子科技大学信息与通信学院桂林541004

海洋物理环境和电磁环境日趋复杂,海杂波背景下的微弱慢速小目标检测始终是一个研究难点和重点。海面小目标的雷达散射截面积小、回波能量低,传统基于能量的检测方法存在性能瓶颈。基于特征的检测方法聚焦于提取纯杂波和目标回波的差异... 详细信息

海洋物理环境和电磁环境日趋复杂,海杂波背景下的微弱慢速小目标检测始终是一个研究难点和重点。海面小目标的雷达散射截面积小、回波能量低,传统基于能量的检测方法存在性能瓶颈。基于特征的检测方法聚焦于提取纯杂波和目标回波的差异性特征来实现目标检测,且有效提升了检测性能。该文利用回波数据间频域中幅度的关联性,将图论的方法引入到特征检测中。首先将实测数据进行块白化处理,对海杂波进行一定的抑制,然后在频域提取各多普勒通道下的数据,借助图的处理方法,构建所提取数据的距离邻接矩阵,再转换为拉普拉斯矩阵。该方法计算不同时间序列下拉普拉斯矩阵的最大特征值,并将其与刻画频域能量信息的相对多普勒峰高进行融合,得到新的检验统计量来区分纯杂波和含有目标的回波。通过全相参的X波段(IPIX)实测数据验证,该文所提方法的检测性能更为优越。

关键词：目标检测海杂波特征检测图特征多普勒通道

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Target-Constrained Interference-Minimized Band Selection for Hyperspectral Target Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第7期59卷 6044-6064页

作者： Shang, Xiaodi Song, Meiping Wang, Yulei Yu, Chunyan Yu, Haoyang Li, Fang Chang, Chein-I Dalian Maritime Univ Ctr Hyperspectral Imaging Remote Sensing CHIRS Informat & Technol Coll Dalian 116026 Peoples R China Univ Maryland Remote Sensing Signal & Image Proc Lab Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Providence Univ Dept Comp Sci & Informat Management Taichung 02912 Taiwan

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBSx002A;) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods.

关键词： Object detection Hyperspectral imaging signal to noise ratio Computer science Interference Band prioritization (BP) band selection (BS) hyperspectral image (HSI) target detection target-constrained interference-minimum filter (TCIMF) virtual dimensionality (VD)

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Shadow-Assisted Moving Target Tracking Based on Multidiscriminant Correlation Filters Network in Video SAR

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Fang, Hui Liao, Guisheng Liu, Yongjun Zeng, Cao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this letter, a shadow-assisted tracking method for moving targets based on a multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a reverse feature pyramid network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised multilevel discriminant correlation filters (MDCFs) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.

关键词： Index Terms- Convolutional neural network (CNN) discrim-inant correlation filters (DCFs) moving target tracking video synthetic aperture radar (video SAR)

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Experimental Investigation for GPR Detection of Buried Objects with Varied Sizes and Orientation

Experimental Investigation for GPR Detection of Buried Objec...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Sujeet Roy Himanshu Govil Sandip Mukherjee Prabhat Diwan Department of Applied Geology National Institute of Technology Raipur India Independent Researcher Indian Institute of Remote Sensing Dehradun India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Ground Penetration Radar (GPR) is a remote sensing technology with immense potential. Its performance and quality of results strongly depends on primarily the composition of the soil and further on other aspects of the subsurface targets like the size, material, shape, depth, and orientation. The central operating frequency of the GPR and the type of antenna on the GPR are selected based on the factors above, to be able to detect your intended target satisfactorily. Limited exploratory research work has been undertaken in this field in India towards detection of small shallow buried objects. The present study attempts to experimentally investigate the combined effect of predetermined size and orientation of small shallow buried objects in conjunction with a suitably selected central frequency of the GPR. The experiment was conducted in Jaisalmer, where the arid dry sandy wastelands are conducive to GPR performance. signal processing methods have been applied to the standard GPR results to derive better output. Apart from the standard effect of material of the targets and the depth buried to, the study mainly provided data indicating a positive correlation between the central operating frequency of the GPR and the quality of detection of the shallow subsurface targets of size corresponding to the wavelength $\lambda$. The positive correlation of polarization of the GPR antenna to the alignment of the buried object was also noted. Further investigations covering more factors affecting GPR performance is advocated by the present study.

关键词： Surveys Correlation Soil Planning data mining Test equipment Buried object detection Standards Remote sensing Antennas

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Dim and small target detection based on their living environment

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DIGITAL signal processing 2022年 120卷 103271-103271页

作者： Zhou, Shugang Gao, Zhisheng Xie, Chunzhi Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China

Dim and small targets detection in a cluttered background requires high discrimination features to separate them from the noisy environment. Previous local contrast measure based methods lack scale invariance and also cannot fully learn the features of target and target's living environment. Semantic segmentation based methods are helpless for very dim and small targets. A 3D-based convolutional network for analyzing and reconstructing dim and small targets is proposed in this paper. To separate targets from their living environment effectively, A 3D tensor is extracted around the target and used as the input of the proposed analytical network. Then, comprehensive features and environmental information of the targets are extracted by a serial of 3D convolution layers. To improve the reconstruction ability of the proposed model, a trick of skip-connection is adopted to fuse the abstract features and detailed features. The block attention module and dilated convolution can extract important and high-level features. After the dim and small targets are reconstructed, a detection method based on the constant false alarm rate (CFAR) is adopted to detect and locate these targets. Experiments on simulated data and real data with diverse information demonstrate that the proposed approach outperforms the state-of-the-art methods. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Reconstruction of dim and small targets 3D convolution Analysis network Infrared image CFAR

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A Lightweight Extraction Method for Human Skeletal Joint Points of Wind Tunnel Pilots Based on GST

A Lightweight Extraction Method for Human Skeletal Joint Poi...

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International Conference on Vision, Image and signal processing (ICVISP)

作者： Wei Zhang Wentao Gong Ying Duan Zhisheng Gao School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Wind tunnel skydiving training, as an essential means of skydiving and aviation training, relies on efficient posture capture to ensure training effectiveness. However, due to factors such as complex wind tunnel backgrounds,pilot body occlusion, small joint targets, and high model complexity, the real-time extraction accuracy of the current model needs to be improved. To this end, we propose a lightweight method based on Gst to extract the critical skeleton points of wind tunnel pilots. This method redesigns the lightweight keypoint extraction model YOLOv8nPose-Gst, an improved version of YOLOv8nPose aimed at improving wind tunnel pilot attitude detection accuracy and *** introducing the CoordAtt attention mechanism,the model is better at focusing on small keypoint targets, and combined with the HeadGst convolutional head design, it effectively reduces computational complexity while maintaining high detection accuracy. In addition, this article adopts the NmPIoU loss function to accelerate training and further improve model performance. Our method achieved significant results across multiple metrics on the wind tunnel pilot dataset. Compared with the baseline model YOLOv8nPose, map@50, and map@50-95, The values reached 0.801 and 0.564, 2.2 percentage points and 3.3 percentage points higher than the original model. Meanwhile, the model complexity GFLOPs is 7.8, a decrease of 1.4; The number of parameters has also decreased from 3.289 to 2.932.

关键词： Training Adaptation models Accuracy Wind tunnels Computational modeling data models Skeleton Real-time systems Computational complexity Context modeling

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GASS: Generalizing Audio Source Separation with Large-Scale data

GASS: Generalizing Audio Source Separation with Large-Scale ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jordi Pons Xiaoyu Liu Santiago Pascual Joan Serrà Dolby Laboratories

Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the potential of universal source separation is limited because most existing works focus on mixes with predominantly sound events, and small training datasets also limit its potential for supervised learning. Here, we study a single general audio source separation (GASS) model trained to separate speech, music, and sound events in a supervised fashion with a large-scale dataset. We assess GASS models on a diverse set of tasks. Our strong in-distribution results show the feasibility of GASS models, and the competitive out-of-distribution performance in sound event and speech separation shows its generalization abilities. Yet, it is challenging for GASS models to generalize for separating out-of-distribution cinematic and music content. We also fine-tune GASS models on each dataset and consistently outperform the ones without pre-training. All fine-tuned models (except the music separation one) obtain state-of-the-art results in their respective benchmarks.

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Joint range, angle and polarization estimation in polarimetric FDA-MIMO radar based on Tucker tensor decomposition

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EURASIP JOURNAL ON ADVANCES IN signal processing 2023年第1期2023卷 39页

作者： Zhang, Qi Jiang, Hong Liu, Yunchang Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is an emerging technology to offer range-angle-dependent beampattern. Polarimetric FDA-MIMO radar can sense additional polarization information to improve target identification capability. In this article, we investigate the problem of joint range, angle and polarization parameter estimation in a monostatic polarimetric FDA-MIMO radar with an FDA at transmitter and a cross-dipole array at receiver. Unlike the conventional methods in which the multidimensional data structure is rearranged into vectors or matrices by stacking operation, we propose a Tucker tensor decomposition-based scheme, which can reserve the original data structure and avoid spoiling the inherent characteristics of interest, especially when the number of snapshots is small. The third-order tensor model of the observed data is constructed. Two approaches named as Tucker covariance reconstruction and Tucker signal subspace are presented using the fourth-order covariance tensor decomposition. The Cramer-Rao bound for range, angle and polarization estimation is also provided. Numerical experiments demonstrate the superiorities of the proposed approaches. Specifically, two targets with identical range and close angles are effectively distinguished.

关键词： FDA-MIMO radar Tucker tensor decomposition Range-angle-polarization estimation Polarization sensitive array signal subspace

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Floating small Target Detection in Sea Clutter Based on Long Short-Term Memory-Transformer Model

SSRN

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SSRN 2024年

作者： Gao, Hongyuan Yuan, Shanliang Zhu, Qinglin Chuai, Jige Ma, Jingya Harbin Engineering University China Harbin Engineering University Heilongjiang China China Research Institute of Radio Wave Propagation Qingdao China

Target detection in sea clutter is critical for both civil and military applications. However, due to the impact of chaotic model prediction accuracy on detection performance and the ease with which target signals can be submerged in sea clutter, detecting small targets in the presence of strong sea clutter poses a significant challenge. To address this issue, a method for detecting small targets in sea clutter using a long short-term memory-transformer (LSTM-Trans) model is proposed. First, phase space reconstruction is used to transform the sea clutter signal and extract high-dimensional information. Next, the LSTM-Trans network, which combines the cell state and hidden state information from the LSTM to fully exploit its memory capabilities, is introduced. The transformer component further enhances and optimizes the fused feature information, effectively capturing complex dependencies within the data. In addition, to preserve the chaotic properties while extracting more information at each time step, the embedding window width of the phase space reconstruction is used as the time step input to the LSTM-Trans. Experimental results, validated with intelligent pixel processing X-band (IPIX) data, show that the proposed method achieves higher prediction accuracy and superior detection performance for sea clutter compared to other recurrent neural networks (RNN). © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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