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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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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration 6

Small Target Detection Method in Sea Clutter Based on Interf...

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6th International conference on signal and Image processing, ICSIP 2021

作者： Xu, Shuwen Ma, Yitu Bai, Xiaohui Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9780738133737

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods. © 2021 IEEE.

关键词： Radar cross section

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A novel integrated radar sounder simulation technique for modelling large and small-scale surface scattering phenomena 27

A novel integrated radar sounder simulation technique for mo...

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conference on Image and signal processing for Remote Sensing XXVII

作者： Sbalchiero, Elisa Thakur, Sanchari Cortellazzi, Marco Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommar 5 I-38123 Trento Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Radar sounders (RS) play an important role in planetary investigation. However, the complex tasks of predicting performance and interpreting RS data and predicting performance require to perform data simulations. In the literature, there are different methods for RS data simulation, including: i) numerical methods involving the exact solution of Maxwell's equations based on 3D modelling;and ii) methods based on ray-tracing and facet modelling. Among numerical methods, the Finite-Difference Time-Domain (FDTD) technique allows one to precisely model complex nonlinear targets, both geologically and geophysically, at the cost of high computational load. On the contrary, coherent ray-tracing methods reduce the computational cost by triangulating the targets into facets with size comparable to the wavelength. In this work, we combine the advantages of FDTD and ray-tracing methods into a novel integrated simulation technique for modelling scattering phenomena at two scales of wave interaction, i.e. large (facet) scale and small (sub-facet) scale. The coherent facet method is used to simulate facet- scale scattering phenomena, while FDTD is used to evaluate the sub-facet-scale scattering due to roughness on top of the single facets. We investigate the method's effectiveness by generating a one-layer synthetic DEM as a fractional Brownian motion (fBm) process superimposed by different values of RMS slope of the small-scale roughness and by analysing how the received signal changes in terms of signal-to-noise ratio. The results show the effectiveness of the method in representing small-scale roughness realistically.

关键词： Radar sounder simulations FDTD ray-tracing coherent simulator small-scale roughness planetary exploration

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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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Comparison of Soft and Hard Target RNN-T Distillation for Large-Scale ASR

Comparison of Soft and Hard Target RNN-T Distillation for La...

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

作者： Dongseong Hwang Khe Chai Sim Yu Zhang Trevor Strohman Google LLC USA

Knowledge distillation is an effective machine learning technique to transfer knowledge from a teacher model to a smaller student model, especially with unlabeled data. In this paper, we focus on knowledge distillation for the RNN-T model, which is widely used in state-of-the-art (SoTA) automatic speech recognition (ASR). Specifically, we compared using soft and hard target distillation to train large-scale RNN-T models on the LibriSpeech/LibriLight public dataset (60k hours) and our in-house data (600k hours). We found that hard targets are more effective when the teacher and student have different architecture, such as large teacher and small streaming student. On the other hand, soft target distillation works better in self-training scenario like iterative large teacher training. For a large model with 0.6B weights, we achieve a new SoTA word error rate (WER) on LibriSpeech (8% relative improvement on dev-other) using Noisy Student Training with soft target distillation. It also allows our production teacher to adapt new data domain continuously.

关键词： Training Adaptation models Error analysis Production Machine learning signal processing data models

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A Nonlinear Frequency Modulation Agile Waveform Optimization Algorithm for Pulse Doppler Radar

A Nonlinear Frequency Modulation Agile Waveform Optimization...

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signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Qing-Song Deng Yu-Hang Gao Li-Xiang Ren Quan-Hua Liu Hua-Yu Fan School of Information and Electrinics Beijing Institute of Technology Beijing China Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology) Ministry of Education Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Institute of Technology Yangtze Delta Region Academy Jiaxing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Traditional pulse Doppler radar faces limitations such as range ambiguity and clutter folding when detecting long-range slow-moving small targets in strong clutter backgrounds. The agile waveform technique aims to suppress these issues by varying the parameters of the transmitted waveform between pulses within the coherent processing interval (CPI). However, due to the differing range sidelobes produced by the matched filtering output of the agile waveform, range sidelobe modulation (RSM) effect occurs after Doppler processing, resulting in higher sidelobes in the Doppler domain. To address these challenges, a nonlinear frequency modulation (NLFM) agile waveform optimization algorithm for Pulse Doppler radar is proposed in this paper. First, the spectrum of NLFM waveforms is initially mathematically formulated by using a cosine series representation. Then, considering the RSM effect and waveform orthogonality, with the optimization objectives being the similarity and orthogonality of the agile waveforms. Finally, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm is employed to solve the optimization objectives, resulting in a set of NLFM waveforms with improved performance. Simulation results demonstrate that the proposed algorithm can strike a good balance between the RSM suppression and waveform orthogonality. The designed waveforms, when filtered by mismatched filters, effectively reduces the signal-to-noise ratio loss(SNRL) relative to the initial waveforms.

关键词： Frequency modulation Simulation Impedance matching Information filters Doppler radar Doppler effect Particle swarm optimization Clutter Optimization signal to noise ratio

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MTKD-Det: An Efficient SAR Target Detector Based on Multi-Teacher Knowledge Distillation

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Te...

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signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Guang Zhang Zhaocheng Wang Ruonan Wang Hualiang Song Hailong Kang School of Electronics and Information Engineering Hebei University of Technology Tianjin China Hangzhou Institute of Technology Xidian University Hangzhou China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As a critical branch of synthetic aperture radar (SAR) interpretation, SAR target detection has many applications in the military and civil fields. Although traditional deep learning methods achieve some satisfactory performance in target detection tasks, they inherently require significant computational resources and interpretation time. Hence, traditional deep learning methods frequently struggle to maintain a balance between high efficiency and precision. It is undoubtedly a huge challenge for target detection tasks. To address this problem, an efficient SAR target detector based on multi-teacher knowledge distillation (MTKD-Det) is proposed in this paper, which can effectively enhance target detection accuracy while reducing inference time. Specifically, a multiple-teacher knowledge distillation strategy is used in the proposed method, which integrates the experiences and methodologies of multiple teachers to collaboratively guide the training and learning of the student network, ultimately enhancing the robustness of the student network. In this work, a novel teacher network is developed to enhance the detection performance of the proposed method specifically for small targets. Furthermore, an attention mechanism is incorporated into the student network to enable it to focus on more critical information, thereby improving overall detection efficacy. Experimental results obtained from a measured SAR target detection dataset convincingly validate the benefits of the proposed approach.

关键词： Knowledge engineering Deep learning Training Attention mechanisms Accuracy Object detection Detectors Feature extraction Robustness Synthetic aperture radar

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Residual LSTM Radar Target Detection Method Based on Global Channel Attention Mechanism

Residual LSTM Radar Target Detection Method Based on Global ...

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Communication Technology and Information Technology (ICCTIT), International conference on

作者： Chunhao Bao Pengfei He Yong Huang Lei Xu School of Physics and Electronic Information Yantai University Yantai China Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Yantai University Yantai China Naval Aviation University Yantai China Huaneng Shandong Power generation Co. LTD. Yantai power plant Yantai China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

In radar target detection, large targets often mask small targets, and the traditional constant false alarm (CFAR) detection method is difficult to set a suitable threshold and cannot cope with the detection of this type of target well. Aiming at this kind of problem, a residual LSTM detection method based on global channel attention mechanism (Global Channel Attention Mechanism- Long Short Term Memory, GCAMLSTM) is proposed. This method processes the radar echo data, establishes the time-order relationship of the radar signals through the sliding window method, and proposes a new Global Channel Attention Mechanism module according to the characteristics of the time-order signals, so as to extract the target echo features more accurately. The experimental results show that the detection performance of the proposed method is $\mathbf{6. 8 \%}$ higher than that of the traditional CFAR algorithm. It has strong robustness in the above scenario and can achieve effective detection of radar targets.

关键词： Attention mechanisms Time series analysis Radar detection Radar Object detection Feature extraction Radar signal processing Robustness Long short term memory Monitoring

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Extracting Sinusoidal Frequency Modulation signal Signature Using Fourier-Bessel Transform and Short-Time Fraction Fourier Transform

Extracting Sinusoidal Frequency Modulation Signal Signature ...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Xiaomeng Ma Meiguo Gao School of Information and Communication Beijing Institute of Technology Beijing China

A method using Fourier-Bessel transform (FBT) and short-time fraction Fourier transform (ST-FRFT), for accurate and robust parameter estimate and separation of sinusoidally frequency modulated (SFM) nonstationary signal, is presented. The rotation micro-motion coupling with the rigid body's motion provides an approximately sinusoidal phase modulation, which is known as the micro-Doppler (m-D) signal. According to priori research, the SFM signal shows the nearly linear frequency modulation characteristic in the local part and sparsity features in fraction domain. Firstly, in order to extract the slope of nearly FM and the rotation target's angular frequency, here using FBT method can decompose the rotation target's echo into stationary and nonstationary component. Then, using ST-FRFT method separate the target's signal into the fraction domain where through peak search to catch the SFM signal's slope and rotation angular frequency. This function shows enormous potential in solving the problem that nonstationary component always can't be extracted because of weak echo amplitude. The rotation angle frequency is an important characteristic of the rotating target, which shows great significance to the classification of low-altitude and low-speed small targets. This proposed method has been demonstrated by the air target's simulation data which shows effective results of estimating these parameters.

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FEW SHOT LEARNING FOR INFRA-RED OBJECT RECOGNITION USING ANALYTICALLY DESIGNED LOW LEVEL FILTERS FOR data REPRESENTATION

FEW SHOT LEARNING FOR INFRA-RED OBJECT RECOGNITION USING ANA...

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IEEE International conference on Image processing (ICIP)

作者： Arif, Maliha Mahalanobis, Abhijit Univ Cent Florida Ctr Res Comp Vis CRCV Orlando FL 32816 USA

ISBN: (纸本)9781665441155

It is well known that deep convolutional neural networks (CNNs) generalize well over large number of classes when ample training data is available. However, training with smaller datasets does not always achieve robust performance. In such cases, we show that using analytically derived filters in the lowest layer enables a network to achieve better performance than learning from scratch using a relatively small dataset. These class-agnostic filters represent the underlying manifold of the data space, and also generalize to new or unknown classes which may occur on the same manifold. This directly enables new classes to be learned with very few images by simply fine-tuning the final few layers of the network. We illustrate the advantages of our method using the publicly available set of infra-red images of vehicular ground targets. We compare a simple CNN trained using our method with transfer learning performed using the VGG-16 network, and show that when the number of training images is limited, the proposed approach not only achieves better results on the trained classes, but also outperforms a standard network for learning a new object class.

关键词： manifold eigen representation few shot learning sparse learning infra-red datasets

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