• Title/Summary/Keyword: multiple target detection

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The Design and Implementation of A Distributed Intrusion Detection System for Multiple Attacks (대규모 네트워크 상의 다중공격에 대비한 분산 침입탐지시스템의 설계 및 구현)

  • 최주영;최은정;김명주
    • Convergence Security Journal
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    • v.1 no.1
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    • pp.21-29
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    • 2001
  • For multiple attacks through large networks e.g., internet, IDS had better be installed over several hosts and collect all the audit data from them with appropriate synthesis. We propose a new distributed intrusion detection system called SPIDER II which is the upgraded version of the previous standalone IDS - SPIDER I. As like the previous version, SPIDER II has been implemented on Linux Accel 6.1 in CNU C. After planting intrusion detection engines over several target hosts as active agents, the administration module of SPIDER II receives all the logs from agents and analyzes hem. For the world-wide standardization on IDS, SPIDER II is compatible with MITRE's CVE(Common Vulnerabilities and Exposures).

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A Dataset of Ground Vehicle Targets from Satellite SAR Images and Its Application to Detection and Instance Segmentation (위성 SAR 영상의 지상차량 표적 데이터 셋 및 탐지와 객체분할로의 적용)

  • Park, Ji-Hoon;Choi, Yeo-Reum;Chae, Dae-Young;Lim, Ho;Yoo, Ji Hee
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.1
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    • pp.30-44
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    • 2022
  • The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

Detection of Breathing Rates in Through-wall UWB Radar Utilizing JTFA

  • Liang, Xiaolin;Jiang, Yongling
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5527-5545
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    • 2019
  • Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

Antipersonnel Landmine Detection Using Ground Penetrating Radar

  • Shrestha, Shanker-Man;Arai, Ikuo;Tomizawa, Yoshiyuki;Gotoh, Shinji
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.1064-1066
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    • 2003
  • In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

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Pre-processing Faded Measurements for Bearing-and-Frequency Target Motion Analysis

  • Lee, Man-Hyung;Moon, Jeong-Hyun;Kim, In-Soo;Kim, Chang-Sup;Choi, Jae-Weon
    • International Journal of Control, Automation, and Systems
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    • v.6 no.3
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    • pp.424-433
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    • 2008
  • An ownship with towed array sonar (TAS) has limited maneuvers due to its dynamic feature, bearing and frequency measurements of a target which are not detected continuously but are often lost in ocean environment. We propose a pre-processing algorithm for the faded bearing and frequency measurements to solve the BFTMA problem of TAS under limited detection conditions. The proposed pre-processing algorithm to restore the faded bearing and frequency measurements is implemented to perform a BFTMA filter even if the measurements of a target are not continuously detected. The Modified Gain Extended Kalman Filter (MGEKF) method based on the Interacting Multiple Model (IMM) structure is applied for a BFTMA filter algorithm to estimate the target. Simulations for the various conditions were carried out to verify the applicability of the proposed algorithms, and confirmed superior estimation performance compared with the existing Bearings-Only TMA (BOTMA).

Integrated Type DNA Chip Array and Gene Detection Using an Indicator-free DNA (집적형 DNA칩 어레이 및 비수식화 DNA를 이용한 유전자 검출)

  • Choi, Yong-Sung;Lee, Kyung-Sup
    • Proceedings of the KIEE Conference
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    • 2006.07c
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    • pp.1322-1323
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    • 2006
  • This research aims to develop the multiple channel electrochemical DNA chip that has the above characteristic and be able to solve the problems. At first, we fabricated a high integration type DNA chip array by lithography technology. It is able to detect a plural genes electrochemically after immobilization of a plural probe DNA and hybridization of non-labeling target DNA on the electrodes simultaneously.

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Separation of passive sonar target signals using frequency domain independent component analysis (주파수영역 독립성분분석을 이용한 수동소나 표적신호 분리)

  • Lee, Hojae;Seo, Iksu;Bae, Keunsung
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.2
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    • pp.110-117
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    • 2016
  • Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

Chopping Frequency Extraction of JEM Signal Using MUSIC Algorithm (MUSIC 알고리즘을 이용한 JEM 신호의 Chopping 주파수 추출)

  • Song, Won-Young;Kim, Hyung-Ju;Kim, Sung-Tai;Shin, In-Seon;Myung, Noh-Hoon
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.30 no.3
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    • pp.252-259
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    • 2019
  • Jet engine modulation(JEM) signals are widely used in the field of target recognition along with high-range resolution profile and inverse synthetic aperture radar because they provide specific information of the jet engine. To obtain the number of blades of the jet engine, the chopping frequency proportional to the number of blades must be extracted. In the conventional chopping frequency extraction method, an initial threshold value is defined and a method of detecting the chopping peak is used. However, this detection method takes time depending on the signal due to repetitive detection. Thus, in this study, we proposed to extract the chopping frequency using MUltiple SIgnal Classification(MUSIC) algorithm. We applied the MUSIC algorithm to a given JEM signal to find the chopping frequency and determine the blade number candidates. We also applied the MUSIC algorithm to other chopping frequency extractions to determine the score of the candidate groups. Unlike the conventional detection algorithm, which requires repetitive frequency detection, MUSIC algorithm quickly detects the accurate chopping frequency and reduces the calculation time.

Manhole Cover Detection from Natural Scene Based on Imaging Environment Perception

  • Liu, Haoting;Yan, Beibei;Wang, Wei;Li, Xin;Guo, Zhenhui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.5095-5111
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    • 2019
  • A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection

  • Wang, Qianghui;Hua, Wenshen;Huang, Fuyu;Zhang, Yan;Yan, Yang
    • Current Optics and Photonics
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    • v.4 no.3
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    • pp.210-220
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    • 2020
  • Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.