• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.200-207
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• 2007

The passive direction finding cross localization method is widely adopted in passive tracking, therefore there will exist masses of false intersection points. Eliminating these false intersection points correctly and quickly is a key technique in passive localization. A new method is proposed for passive locating and tracking multi-jammer target in this paper. It not only solves the difficulty of determining the number of targets when masses of false intersection points existing, but also solves the initialization problem of elastic network. Thus this method solves the problem of multi-jammer target correlation and the elimination of static false intersection points. The method which dynamically establishes multiple hypothesis trajectory trees solves the problem of eliminating the remaining false intersection points. Simulation results show that computational burden of the method is lower, the elastic network can more quickly find all or most of the targets and have a more probability of locking the real targets. This method can eliminate more false intersection points.

• ETRI Journal
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• v.35 no.1
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• pp.80-88
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• 2013

A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.2
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• pp.140-146
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• 2015

We have studied an efficient threshold level for desired target detection in radar system in the paper. A desired target searching detection method detects desired target according to changing for false alarm probability. This time, false alarm probability is close relation to threshold level. Low threshold level can improve detection for desired target, but detect noise signal. Therefor, This method is not good one. In this paper, we propose efficient threshold level method in order to estimation for desired target. Through simulation, we are analysis and performance to compare general method with proposal method. We show that proposed method is more good proof than general method.

• ETRI Journal
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• v.15 no.2
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• pp.1-9
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• 1993

Nitzberg has analyzed the detection performance of the clutter map constant false alarm rate (CFAR) detector using single pulse. In this paper, we extend the detection analysis to the clutter map CFAR detector that employs M-pulse noncoherent integration. Detection and false alarm probabilities for Swerling target models are derived. The analytical results show that the larger the number of integrated pulses M, the higher the detection probability. On the other hand, the analytical results for Swerling target models show that the detection performance of the completely decorrelated target signal is better than that of the completely correlated target.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.7
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• pp.587-594
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• 2005

In this paper, a robust correlation-based video tracking is proposed to track a moving object in correlated image sequences. A correlation-based video tracking algorithm seeks to align the incoming target image with the reference target block image, but has critical problems, so called a false-peak problem and a drift phenomenon (correlator walk-off. The false-peak problem is generally caused by highly correlated background pixels with similar intensity of a moving target and the drift phenomenon occurs when tracking errors accumulate from frame to frame because of the nature of the correlation process. At first, the false-peaks problem for the ordinary correlation-based video tracking is investigated using a simple mathematical analysis. And, we will suggest a robust selective-attention correlation measure with a gradient preprocessor combined by a drift removal compensator to overcome the walk-off problem. The drift compensator adaptively controls the template block size according to the target size of interest. The robustness of the proposed method for practical application is demonstrated by simulating two real-image sequences.

• The Journal of the Acoustical Society of Korea
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• v.34 no.2
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• pp.98-107
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• 2015

It is investigated that there exists the possibility of the false target signals induced by reverberation in an active sonar system due to the internal waves in shallow water. The rays down-refracted from the internal waves may generate strong bottom-reverberation signals, which can result in false target signals. Sound waves emitted from a source propagate 3-dimensionally. Therefore, the study of internal waves on the reverberation should be studied for azimuthal direction as well as 2-dimensional (r-z) plane. Internal-wave modelling was conducted, based on solitons which were predicted with the various conditions such as, the range of source-soliton, horizontal widths of soliton. Variable depth sonar (VDS) was assumed as a source, of which the depth was located in the minimum sound speed layer in a simulation environment. Finally, the simulation on the reverberation level with time was made based on ray-based reverberation model, and the results implied that several false-target signals could be displayed on the PPI(Plan Position Indicator) scope simultaneously with range from source to soliton, and the horizontal width of soliton.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.1
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• pp.100-105
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• 2011

Generally, because received signals from radar are very bulky, the data are divided into manageable size called section, and sections are distributed into several digital signal processors. And then, target detection algorithms are applied simultaneously in each processor. CFAR(Constant False Alarm Rate) algorithm, which is the most popular target detection algorithm, can estimate accurate threshold values to determine which signals are targets or noises within center-cut of section allocated to each processor. However, its estimation precision is diminished in section edge data because of insufficient surrounding data to be referred. Especially this edge problem of CFAR is too serious if we have many sections to be processed, because it causes many false alarms in most every section edges. This paper describes false alarm issues on MCA(Minimum Cell Average)-CFAR, and proposes a false alarm elimination method by changing section size alternatively. Real received data from multi-function radar were used to evaluate a proposed method, and we show that our method drastically decreases false alarms without missing real targets, and improves detection performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.476-483
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• 2009

Tracking and detecting targets by the microwave seeker is affected by the clutter reflecting from the earth's surface. In order to detect retreating targets in look-down scenario, which appear in the sidelobe clutter (SLC) region, in the microwave seeker of high pulse repetition frequency (HPRF) mode, it is necessary to understand statistical characteristics of the surface SLC. Statistical analysis of SLC has been conducted for several kinds of the surface using data obtained by the captive flight test of the microwave seeker in the HPRF mode. The probability density function (PDF) fitting is conducted for several kinds and conditions of the surface. PDFs and PDF parameters, which best describe statistical distribution of the SLC power, are estimated. By using the estimated PDFs and PDF parameters, analyses for setting the target-detection thresholds, which give a desired level of target-detection false alarm probability, are made. These analysis materials for statistical characteristics of SLC power and the target-detection threshold can be used in various fields, such as development of a target-detection method, the constant false alarm rate processing.

• The Plant Pathology Journal
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• v.37 no.3
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• pp.291-298
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• 2021

False smut caused by Ustilaginoidea virens is an important rice fungal disease that significantly decreases its production. In the recent past, conventional methods have been developed for its detection that is time-consuming and need high-cost equipments. The research and development in nanotechnology have made it possible to assemble efficient recognition interfaces in biosensors. In this study, we present a simple, sensitive, and selective oxidized graphene-based geno-biosensor for the detection of rice false smut. The biosensor has been developed using a probe DNA as a biological recognition element on paper electrodes, and oxidized graphene to enhance the limit of detection and sensitivity of the sensor. Probe single-stranded DNA (ssDNA) and target ssDNA hybridization on the interface surface has been quantitatively measured with the electrochemical analysis tools namely, cyclic voltammetry, and linear sweep voltammetry. To confirm the selectivity of the device, probe hybridization with non-complementary ssDNA target has been studied. In our study, the developed sensor was able to detect up to 10 fM of target ssDNA. The paper electrodes were employed to produce an effective and cost-effective platform for the immobilization of the DNA and can be extended to design low-cost biosensors for the detection of the other plant pathogens.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.