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Space-time adaptive processing for airborne radar

Space-time adaptive processing for airborne radar

引用

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

作者： J. Ward Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA

Advanced airborne radar systems are required to detect targets in the presence of both clutter and jamming. Ground clutter is extended in both angle and range, and is spread in Doppler frequency because of the platform motion. Space-time adaptive processing (STAP) refers to the simultaneous processing of the signals from an array antenna during a multiple pulse coherent waveform. STAP can provide improved detection of targets obscured by mainlobe clutter, defection of targets obscured by sidelobe clutter, and detection in combined clutter and jamming environments. Fully adaptive STAP is impractical for reasons of computational complexity and estimation with limited data, so partially adaptive approaches are required. The paper presents a taxonomy of partially adaptive STAP approaches that are classified according to the type of preprocessor, or equivalently, by the domain in which adaptive weighting occurs. Analysis of the rank of the clutter covariance matrix in each domain provides insight and conditions for preprocessor design.

关键词： Airborne radar Clutter Jamming Adaptive arrays Radar detection Frequency signal processing Antenna arrays Computational complexity Taxonomy

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Accelerated nonlinear Gaussianization for feature extraction

Accelerated nonlinear Gaussianization for feature extraction

引用

2nd International conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Condurache, Alexandru Paul Mertins, Alfred Institute for Signal Processing University of Luebeck Ratzeburger Allee 160 D-23562 Luebeck Germany

ISBN: (纸本)9789898565419

In a multi-class classification setup, the Gaussianization represents a nonlinear feature extraction transform with the purpose of achieving Gaussian class-conditional densities in the transformed space. The computational complexity of such a transformation increases with the dimension of the processed feature space in such a way that only relatively small dimensions can be processed. In this contribution we describe how to reduce the computational burden with the help of an adaptive grid. Thus, the Gaussianization transform is able to also handle feature spaces of higher dimensionality, improving upon its practical usability. On both artificially generated and real-application data, we demonstrate a decrease in computation complexity in comparison to the standard Gaussianization, while maintaining the effectiveness.

关键词： Feature extraction

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High-frequency imaging radar for robotic navigation and situational awareness

High-frequency imaging radar for robotic navigation and situ...

引用

conference on Unmanned Systems Technology XIII

作者： Thomas, David J. Luo, Changan Knox, Robert USA TARDEC Warren Michigan MI USA Epsilon Lambda Elect Corp West Chicago IL USA

ISBN: (纸本)9780819486196

With increasingly available high frequency radar components, the practicality of imaging radar for mobile robotic applications is now practical. Navigation, ODOA, situational awareness and safety applications can be supported in small light weight packaging. Radar has the additional advantage of being able sense through aerosols, smoke and dust that can be difficult for many optical systems. The ability to directly measure the range rate of an object is also an advantage in radar applications. This paper will explore the applicability of high frequency imaging radar for mobile robotics and examine a W-band 360 degree imaging radar prototype. Indoor and outdoor performance data will be analyzed and evaluated for applicability to navigation and situational awareness.

关键词： Radar Digital signal processing Robotics Radar imaging Sensors Situational awareness sensors Antennas Target detection Receivers Imaging systems

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

引用

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

作者： Binhua Yu Ping-An Liu Yichen Liu Hongyu Wang Liang Zhang Yanhua Wang Kun Liu Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target’s pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method.

关键词： Training Sensitivity Image resolution Image recognition Target recognition databases Imaging Radar applications signal resolution Dispersion

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Method for estimating target orientation in single snapshot and coherent echo space

Method for estimating target orientation in single snapshot ...

引用

IEEE International conference on signal processing, Communications and Computing (ICSPCC)

作者： Bibo Zhu Guijuan Han Weihua Cong Science and Technology on Sonar Laboratory Hangzhou Applied Acoustics Research Institute Hangzhou China

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172033

The three-dimensional imaging sonar with small physical size and high integration can detect underwater formations and form image results with centimeter-level resolution in both the forward direction and the depth direction. Due to the limitation of physical size and signal wavelength, the horizontal azimuth resolution obtained by conventional beam-form processing has become a short board for 3D high-resolution imaging. The traditional high-resolution algorithms of MVDR and MUSIC cannot adapt to the characteristics of stratum sonar echo of single snapshot, strong coherent source and a small number of array elements. These traditional high-resolution algorithms cannot guarantee the performance and stability of azimuth resolution of targets in the horizontal direction. This paper avoids the traditional operations of estimating the co-variance matrix, counting multiple snapshot data samples, and reducing the coherence characteristics of the echo space. Through the estimation process of the maximum posterior probability criterion and the iterative convergence process of the constraint equation, the method in this paper can obtain the optimal waveform estimation of the source signal in the spatial scanning orientation. The magnitude of the waveform energy represents the spatial distribution of the target bearing. After simulation analysis and experimental data processing, the algorithm can achieve high-resolution imaging of the target orientation under the single snapshot and coherent sonar echo. It has stable processing performance and good technical innovation.

关键词： Azimuth signal resolution Eigenvalues and eigenfunctions Covariance matrices Arrays Mathematical model Imaging

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Resource Allocation for Multiple Target Tracking in Active and Passive Radar Network

Resource Allocation for Multiple Target Tracking in Active a...

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2021 CIE International conference on Radar, Radar 2021

作者： Dai, Jinhui Mo, Xiuci Pu, Wenqiang Yan, Junkun Wang, Penghui Liu, Hongwei Xidian University National Laboratory of Radar Signal Processing Xi'an China Leihua Electronic Technology Research Institution Aviation Industry Corporation of China Wuxi China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665498142

A joint beam selection and power allocation (JBSPA) scheme is proposed for multiple target tracking (MTT) in active and passive radar network. The motivation of JBSPA is to exploit not only the degrees of freedom of power for active radars but also the degrees of freedom of receive beams for passive radars, thereby improving the overall tracking accuracy of multiple targets. The predictive Posterior Cramér-Rao lower bound is utilized to evaluate the impact of each allocation strategy on tracking performance, and then the JBSPA scheme is formulated as a mixed integer nonlinear program problem. To solve the formulation efficiently, an alternating direction method of multipliers is developed for an optimum. Numerical results demonstrate that the JBSPA scheme can achieve a significant improvement of the overall MTT accuracy, compared with conventional uniform allocation. © 2021 IEEE.

关键词： multiple target tracking nonconvex optimization radar network resource allocation

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Sequentially-Shifted Excitation (SSE) Brillouin spectroscopy for recovering signal contaminated with strong scattering, absorption or fluorescence 6

Sequentially-Shifted Excitation (SSE) Brillouin spectroscopy...

引用

conference on Optical Elastography and Tissue Biomechanics VI at SPIE BIOS conference and Photonics West conference

作者： Troyanova-Wood, Maria Yakovlev, Vladislav V. Texas A&M Univ College Stn TX 77843 USA

ISBN: (数字)9781510624030

ISBN: (纸本)9781510624030

The presented work shows a novel approach to correct the undesired background in Brillouin spectra. Specifically, we have developed a Brillouin spectroscopy modification suitable for correction of distortions caused by a molecular filter's absorption, fluorescent background, or ambient room light. Due to the weak intensity of the Brillouin signal, a distortion of the baseline or a partial absorption of Brillouin peak can have strong impact on data analysis. In the worst case, such as investigation of strongly scattering biological material with simultaneous fluorescence measurements, these perturbations can make it impossible to accurately determine the Brillouin shift. A new Sequentially-Shifted Excitation (SSE) Brillouin spectroscopy method has been developed to allow acquisition of quality Brillouin spectra in most challenging of conditions. The idea behind this method is the observation that the Brillouin and elastically scattered light strongly depend on the wavelength of the incident light, and the location of their respective peaks in the final spectrum changes in response to the smallest change in excitation wavelength. On the other hand, the fluorescence background or distortions due to molecular filter absorption remain the same for small, similar to 1 pm, changes in incident wavelength. SSE Brillouin spectroscopy involves acquiring multiple Brillouin spectra using slightly offset excitation wavelengths, and computationally separating the signal and distortion/background components, thus recovering the Brillouin signal. The application of SSE Brillouin spectroscopy in highly-scattering sample is presented experimentally using sample of cream.

关键词： Brillouin spectroscopy elastography signal processing multi-excitation

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Research on Mass Image data Storage Method for data Center 4th

引用

4th International conference on 3D Imaging Technologies- Multidimensional signal processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Pan, Sen Jiang, Jing Qiu, Hongbin Qiao, Junfeng Xu, Menghan State Grid Smart Grid Research Institute Co. Ltd. Nanjing Jiangsu210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Jiangsu Electric Power Co. Ltd. Information and Telecommunication Branch Nanjing China

ISBN: (纸本)9789819912292

With the advancement of technology and the development of the electric power business, power enterprises have generated a large amount of image data, which contains rich potential information, and it is urgent to store massive-scale image data for further mining and analysis. Starting from the storage requirements of power grid image data, this paper expounds on the drawbacks of direct storage of small files according to the characteristics of power grid image files. Then, the architecture design and function design of the grid image small file storage are carried out, and the function implementation is carried out. Finally, the grid image small file storage method based on the SequenceFile file format is briefly summarized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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InSAR phase noise filter in frequency domain

InSAR phase noise filter in frequency domain

引用

IET International Radar conference 2015

作者： Suo, Zhiyong Li, Ming Zhang, Qingjun Zhang, Jinqiang Fang, Chao National Lab. of Radar Signal Processing Xidian Univ Xi'an710071 China Beijing Aerospace Automatic Control Institute Beijing100854 China China Academy of Space Technology Beijing100094 China

ISBN: (纸本)9781849199940

In this paper, we propose an improved InSAR phase filter in frequency domain based on the Goldstein filter. Firstly, we use different window size determined by the coherence to suppress phase noise. Secondly, we use fringe frequencies compensation to eliminate the effect of the terrain topography. Thirdly, we excute the phase filtering procedure pixel by pixel with a new filtering parameter. Another obvious difference is that we use the Fourier transformation of a small window to deal with the power spectrum of InSAR phase patches. The effectiveness of the improved InSAR phase noise filtering method is investigated by both simulated and real data sets.

关键词： Phase noise

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Multiband Radar signal Fusion and Extrapolation Method Based on Transformer Model

Multiband Radar Signal Fusion and Extrapolation Method Based...

引用

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

作者： Chunxu Gong Weidong Li Ruiming Lu Rui Wang Radar Technology Research Institute Beijing Institute of Technology Beijng China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The improvement of range resolution is crucial for enhancing the capability of imaging radars to detect small targets, such as drones. However, due to technical and cost limitations, current radars cannot indefinitely increase hardware bandwidth to enhance range resolution. To address this challenge, the multiband fusion and extrapolation technique has emerged. This method uses multiple discrete sub-band radar signals and fuses them to extrapolate the missing frequency bands, effectively achieving a wideband radar signal. Traditional approaches primarily rely on constructing signal models and estimating model parameters to accomplish fusion and extrapolation. This paper is the first to apply machine learning techniques to the field of multiband fusion and extrapolation, proposing a radar multiband signal fusion and extrapolation method based on a Transformer model. By training the Transformer network, the method enables accurate estimation of unknown frequency band signals. The effectiveness of the approach is validated through electromagnetic simulation data of drone echoes, demonstrating significantly improved estimation accuracy compared to traditional signal fusion and extrapolation methods.

关键词： Extrapolation Accuracy Image resolution Estimation Radar Machine learning Radar imaging Transformers Frequency estimation Electromagnetics

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