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13th International conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP)

作者： Yang, Jingli Cui, Zheng Jiang, Shouda Harbin Inst Technol Dept Automat Test & Control Xidazhistr 92 Harbin 150080 Heilongjiang Peoples R China

ISBN: (纸本)9783319638560;9783319638553

Aiming at improving the real-time performance of infrared weapon systems, a method based on block compressed sensing is proposed to detect infrared small target, which is also easy to be implemented with hardware. The proposed method can detect and locate small infrared targets by classifying compressed results of blocks. In addition, in order to solve the low detection accuracy caused by using uniform block, the proposed method uses overlapping blocks to reduce the maximum distance between the center of the test sample block and that of the target. Experiments show that the proposed method can effectively improve the detection accuracy of infrared small targets.

关键词： Compressed sensing Infrared target detection Overlapping blocks

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Separation of small targets in Multi-Wavelength Mixtures Based on Statistical Independence 27

Separation of Small Targets in Multi-Wavelength Mixtures Bas...

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conference on signal processing, Sensor/Information Fusion, and Target Recognition XXVII

作者： Mowakeaa, Rami Emge, Darren K. Univ Maryland Baltimore Cty 1000 Hilltop Circle Baltimore MD 21228 USA US Army Edgewood Chem Biol Ctr RDCB DRC P Aberdeen Proving Ground MD 21010 USA

ISBN: (数字)9781510618046

ISBN: (纸本)9781510618046

small target detection is a problem common to a diverse number of fields such as radar, remote sensing, and infrared imaging. In this paper, we consider the application of feature extraction for detection of small hazardous materials in multiwavelength imaging. Since various materials may exist in the area of study each with varying degrees of reflectivity and absortion at different wavelengths of light, flexible, data-driven methods are needed for feature extraction of relevant sources. We propose the use of independent component analysis (ICA), a widely-used blind source separation method based on the statistical independence of the underlying sources. We compare 3 different prominent flavors of ICA on simulated data in a variety of environments. Then, we apply ICA to 2 multi-wavelength imaging datasets with results that suggest that features extracted are useful.

关键词： small-target detection ICA BSS multi-wavelength machine learning

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Phase Compensation Based Multi-frame Coherent Integration for Drone Detection with Radar

Phase Compensation Based Multi-frame Coherent Integration fo...

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IEEE International conference on Unmanned Systems (ICUS)

作者： Rui Huang Xiao-Yong Du Wei-Dong Hu Beijing Institute of Electronic System Engineering Beijing China ATR Lab. National University of Defense Technology Changsha China

ISBN: (纸本)9781728137933

In order to improve the detection ability of small and weak targets such as drones, a multi-frame coherent integration(MFCI) method based on phase compensation is proposed. The phase information of the multi-frame radar echoes is first analyzed and the maximum likelihood estimation(MLE) model of the motion parameters is established. Due to the target motion, the phenomena of migration through range and Doppler cells will occur in the successive frames of target echoes. In order to achieve coherent integration in multiple frames, all the orientations of the multi-frame signal are searched within a certain initial radial velocity and radial acceleration range. The trajectory of each moving target and the corresponding motion parameter values are then obtained, while the phase of the multi-frame signal is located in the same range and the Doppler cell corresponding to a target, thereby improving the detection performance of the weak targets. To further reduce the amount of computation, the influence of radial acceleration on the detection performance of weak targets is studied. Simulation and measured data experiments show that the proposed algorithm can accurately detect drones with less computational complexity.

关键词： Radar Acceleration Maximum likelihood estimation Radar antennas Computational modeling Doppler effect signal processing algorithms

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Long-term testing of acoustic system for tracking low-flying aircraft 10

Long-term testing of acoustic system for tracking low-flying...

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10th International conference on Knowledge and Systems Engineering (KSE)

作者： Sedunov, Alexander Salloum, Hady Sutin, Alexander Sedunov, Nikolay Stevens Inst Technol STAR Ctr Hoboken NJ 07030 USA

ISBN: (纸本)9781538661130

Stevens Institute of Technology conducted a long-term test of an acoustic system designed to track low-flying small aircraft in remote locations. The system consists of 4 nodes located between 1 and 4 km apart in a mountainous terrain. Each node is comprised of a pyramid-shaped volumetric cluster of 5 microphones, an embedded computer, and a pan-tilt-zoom camera steered to detected targets in real time. A communication device was used to transfer data to a centralized location. Each node estimates the direction of arrival toward the sound sources and sends it along to a central processing computer. The central computer combines the data from all nodes to generate tracks and classify targets. The duration and the scale of the deployment allowed to identify and solve many problems, including the effects of propagation delays between nodes on cooperative localization and tracking, the seasonal changes in environmental noise, persistent and transient noise sources, and the diversity of targets of opportunity and their signatures. The propagation delay effects led to the development of separate trackers for review of target trajectories and for immediate action such as automatically steering the camera.

关键词： signal processing passive acoustics low-flying aircraft

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EMPACT 3D: An Advanced EMI Discrimination Sensor for CONUS and OCONUS Applications 23

EMPACT 3D: An Advanced EMI Discrimination Sensor for CONUS a...

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conference on Detection and Sensing of Mines, Explosive Objects, and Obscured targets XXIII

作者： Keranen, Joe Miller, Jonathan S. Schultz, Gregory Sander-Olhoeft, Morgan Laudato, Stephen J. White River Technol 115 Etna Rd Lebanon NH 03766 USA Night Vis & Elect Sensors Directorate 10221 Burbeck Rd Ft Belvoir VA 22060 USA

ISBN: (纸本)9781510617681

We recently developed a new, man-portable, electromagnetic induction (EMI) sensor designed to detect and classify small, unexploded sub-munitions and discriminate them from non-hazardous debris. The ability to distinguish innocuous metal clutter from potentially hazardous unexploded ordnance (UXO) and other explosive remnants of war (ERW) before excavation can significantly accelerate land reclamation efforts by eliminating time spent removing harmless scrap metal. The EMI sensor employs a multi-axis transmitter and receiver configuration to produce data sufficient for anomaly discrimination. A real-time data inversion routine produces intrinsic and extrinsic anomaly features describing the polarizability, location, and orientation of the anomaly under test. We discuss data acquisition and post-processing software development, and results fromlaboratory and field tests demonstrating the discrimination capability of the system. data acquisition and real-time processing emphasize ease-of-use, quality control (QC), and display of discrimination results. Integration of the QC and discrimination methods into the data acquisition software reduces the time required between sensor data collection and the final anomaly discrimination result. The system supports multiple concepts of operations (CONOPs) including: 1) a non-GPS cued configuration in which detected anomalies are discriminated and excavated immediately following the anomaly survey;2) GPS integration to survey multiple anomalies to produce a prioritized dig list with global anomaly locations;and 3) a dynamic mapping configuration supporting detection followed by discrimination and excavation of targets of interest.

关键词： unexploded ordnance explosive remnants of war munitions response signal processing ordnance classification humanitarian demining electromagnetic induction

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Time-Frequency Analysis of Millimeter-Wave Radar Micro-Doppler data from small UAVs 7

Time-Frequency Analysis of Millimeter-Wave Radar Micro-Doppl...

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7th conference of the Sensor signal processing for Defence, SSPD 2017

作者： Rahman, Samiur Robertson, Duncan SUPA School of Physics and Astronomy University of St Andrews St Andrews United Kingdom

ISBN: (纸本)9781538616635

Micro-Doppler feature analysis of small UAVs (sUAVs) is of great importance for identifying such targets. This paper discusses methods that can be used for analyzing various Doppler features present in the radar signal return from sUAVs. A 94 GHz radar is used in both CW and FMCW modes for data collection, and produces high fidelity micro-Doppler returns. Wavelet decomposition method is then used to extract the high frequency components of the signal that correspond to the characteristic fast moving parts of the sUAVs. Finally, time-frequency analysis is performed to visualize and interpret the features. © 2017 IEEE.

关键词： Drones

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Unambiguous Determination of Oscillation Frequency for Multiple Objects using Quadrature Doppler Radar 30

Unambiguous Determination of Oscillation Frequency for Multi...

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Asia-Pacific Microwave conference (APMC)

作者： Whitworth, Avon Ishmael, Khaldoon Yavari, Ehsan Boric-Lubecke, Olga Univ Hawaii Dept Elect Engn Holmes Hall 4832540 Dole St Honolulu HI 96822 USA Adnov LLC 2800 Woodlawn DrSuite 297 Honolulu HI 96822 USA

ISBN: (纸本)9784902339451

Doppler radar can potentially be used to detect respiration and heart rates of human subjects without the inconvenience of attaching transducers to the subjects. We simulated breathing rates of two subjects with two oscillating targets, and recorded the resulting data received when the targets are subject to a continuous wave signal transmitted by a 2.4GHz quadrature channel receiver. Using small changes in the initial center position of one target which passed through the null and optimum positions in the I and Q channels, and then applying a Fast Fourier Transform, we demonstrated that it is possible to remotely distinguish the oscillation frequency of each target.

关键词： Biomedical signal detection biomedical signal processing Doppler radar frequency measurement Fourier transforms

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Three-dimensional Imaging Sonar signal processing System Based on Blade Server

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Journal of Physics: conference Series 2019年第4期1213卷

作者： Xiangrui Zeng Jingyang Liu Bo Gao Science and Technology on Sonar Laboratory Hangzhou Research Institute of Applied Acoustics Hangzhou 310012 China

The three-dimensional high-resolution imaging sonar uses multi-beam technology combined with synthetic aperture technology to achieve centimeter-level imaging of small underwater targets. It has a good imaging effect on underwater anchoring, sinking mine and buried mine target detection and underwater topography, and has a wide range of military and civil engineering applications. The system has a large amount of data transmission and computation, including multiple functional modules such as synthetic aperture, high resolution array processing, image processing, and target recognition. The software architecture is complex and real-time system development faces serious challenges. The blade servers have been widely used in the telecommunications, financial and other big data processing fields as for the high efficiency, stability and autonomy. This paper introduces a cluster 3D imaging signal processing system based on x240m5 blade server. The message queue communication (ZMQ) is used to realize the stable transmission of large data volume. At the same time, the mixed programming technology of C language and Matlab language combines the convenience of real-time and software integration, which greatly shortens the development cycle. The system runs stably and realizes the imaging recognition of the small spheres and their clump weights in the simulated target water.

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Adaptive Kalman Filter for Tracking of Fast Accelerating targets 14

Adaptive Kalman Filter for Tracking of Fast Accelerating Tar...

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14th European Radar conference (EURAD)

作者： Hudec, Premysl Panek, Petr Czech Tech Univ Fac Elect Engn Dept Electromagnet Field Tech 2 Prague 16627 6 Czech Republic Acad Sci Czech Republ Inst Photon & Elect Chaberska 57 Prague 8 Czech Republic

ISBN: (纸本)9782874870491

Active defense (AD) is a new method for protecting military vehicles. Instead of using thick and extremely heavy armors, the actively protected vehicles monitor approaching threatening missiles and de-active them by a suitable active counter-measures. The majority of AD systems is based on radar sensors capable of detecting the concerned missiles and measuring their parameters important for the protective functions. Often, these radar sensors are required to operate in a near-zone (from zero to tens of meters) and find and track relatively small and fast-flying targets. Beside that, it is necessary to take into account the fact that the velocity of the monitored targets can instantaneously vary. This problem especially refers to ignition of rocket engines which can abruptly introduce extremely high accelerations. If the concerned radar sensors employ signal-processing methods based on Kalman filtration, these high-order time-derivatives can cause substantial convergence problems. The main purpose of this article is to present the newly developed adaptable filtering which can reduce these effects. The designed procedures were verified using practically recorded data from flight of RPG-7 missiles.

关键词： Kalman filtration adaptive filtration radar sensor pseudo-noise CW radar

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On the use of deep neural networks for the detection of small vehicles in ortho-images 24

On the use of deep neural networks for the detection of smal...

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24th IEEE International conference on Image processing, ICIP 2017

作者： Terrail, Jean Ogier Du Jurie, Frederic Safran Electronics and Defense Normandie Univ UNICAEN ENSICAEN CNRS France Normandie Univ UNICAEN ENSICAEN CNRS France

ISBN: (纸本)9781509021758

This paper addresses the question of the detection of small targets (vehicles) in ortho-images. This question differs from the general task of detecting objects in images by several aspects. First, the vehicles to be detected are small, typically smaller than 20×20 pixels. Second, due to the multifarious-ness of the landscapes of the earth, several pixel structures similar to that of a vehicle might emerge (roof tops, shadow patterns, rocks, buildings), whereas within the vehicle class the inter-class variability is limited as they all look alike from afar. Finally, the imbalance between the vehicles and the rest of the picture is enormous in most cases. Specifically, this paper is focused on the detection tasks introduced by the VEDAI dataset [1]. This work supports an extensive study of the problems one might face when applying deep neural networks with low resolution and scarce data and proposes some solutions. One of the contributions of this paper is a network severely outperforming the state-of-the-art while being much simpler to implement and a lot faster than competitive approaches. We also list the limitations of this approach and provide several new ideas to further improve our results. © 2017 IEEE.

关键词： Deep neural networks

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