• The Journal of the Acoustical Society of Korea
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• v.33 no.6
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• pp.375-382
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• 2014

In many practical active noise control applications, the primary noise contains multiple closely-spaced harmonics. A narrowband ANC system consists of adaptive filters excited by a composite reference signal, which is the set or sum of sinusoids. This paper analyzes and shows that the convergence speeds of the direct form, parallel form, and simplified parallel form narrowband ANC systems are affected by the fundamental frequency and frequency separation between two adjacent sinusoids in the reference signal. This paper also proposes the new simplified parallel form narrowband ANC system whose convergence speed is independent on the frequency of the reference signal. Computer simulations are conducted to verify the analysis presented in the paper and to compare the proposed narrowband ANC system with the conventional narrowband ANC system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.8 s.111
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• pp.773-787
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• 2006

An airborne radar is an essential aviation electronic system for the aircraft to perform various civil and/or military missions in all weather environments. This paper presents the design, development, and test results of the multi-mode X-band pulsed Doppler radar system test model for helicopter-borne flight test. This radar system consists of 4 LRUs(Line-Replacement Unit), which include antenna unit, transmitter and receiver unit, radar signal & data processing unit and display Unit. The developed core technologies include the planar array antenna, TWTA transmitter, coherent I/Q detector, digital pulse compression, MTI, DSP based Doppler FFT filter, adaptive CFAR, moving clutter compensation, platform motion stabilizer, and tracking capability. The design performance of the developed radar system is verified through various ground fixed and moving vehicle test as well as helicopter-borne field tests including MTD(Moving Target Detector) capability for the Doppler compensation due to the moving platform motion.