• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.93-102
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• 2010

The FMCW(Frequency Modulation Continuous Wave) radar possesses range-velocity ambiguity to identify the correct combination of beat frequencies for each target in the multi-target situation. It can lead to ghost targets and missing targets, and it can reduce the detection probability. In this pap er, we propose an effective identification algorithm for the correct pairs of beat frequencies and the signal processing hardware architecture to effectively support the algorithm. First, using the correlation of the detected up- and down-beat frequencies and Doppler frequencies, the possible combinations are determined. Then, final pairing algorithm is completed with the power spectrum density of the correlated up- and down-beat frequencies. The proposed hardware processor has the basic architecture consisting of beat-frequency registers, pairing table memory, and decision unit. This method will be useful to improve the radar detection probability and reduce the false alarm rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5249-5267
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• 2016

Cognitive radio is an efficient technique to improve spectrum efficiency and relieve the pressure of spectrum resources. In this paper, we investigate the spectrum sensing period in cooperative relay cognitive radio networks; analyze the relationship between the available capacity and the signal-to-noise ratio of the received signal of second users, the target probability of detection and the active probability of primary users. Finally, we derive the closed form expression of the optimal spectrum sensing period in terms of maximum throughput. We simulate the probability of false alarm and available capacity of cognitive radio networks and compare optimal spectrum sensing period scheme with fixed sensing period one in these performance. Simulation results show that the optimal sensing period makes the cognitive networks achieve the higher throughput and better spectrum sensing performance than the fixed sensing period does. Cooperative relay cognitive radio networks with optimal spectrum sensing period can achieve the high capacity and steady probability of false alarm in different target probability of detection. It provides a valuable reference for choosing the optimal spectrum sensing period in cooperative relay cognitive radio networks.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.1
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• pp.13-18
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• 2019

This paper analyzes the signal acquisition performance of the legacy GNSS spreading codes and a polyphase code. The code length and chip rate of a polyphase code are assumed to be same as those of the GPS L1 C/A and Galileo E1C codes. The autocorrelation and cross correlation characteristics are analyzed. In addition, a way to calculate a more accurate probability of false alarm for a code with sidelobe non-zero auto-correlation function is proposed. Finally, we estimate the probability of detection and the mean acquisition time for a given signal strength and the probability of false alarm.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.4
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• pp.173-180
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• 2015

In this study, an algorithm that detects a spoofing signal for a GPS L1 signal was proposed, and the performance was verified through RF spoofing signal simulation. The proposed algorithm determines the reception of a spoofing signal by detecting a correlation distortion of GPS L1 C/A code caused by the spoofing signal. To detect the correlation distortion, a detection criterion of a spoofing signal was derived from the relationship among the Early, Prompt, and Late tap correlation values of a receiver correlator; and a detection threshold was calculated from the false alarm probability of spoofing signal detection. In this study, an RF spoofing environment was built using the GSS 8000 simulator (Spirent). For the RF spoofing signal generated from the simulator, the RF spoofing environment was verified using the commercial receiver DL-V3 (Novatel Inc.). To verify the performance of the proposed algorithm, the RF signal was stored as IF band data using a USRP signal collector (NI) so that the data could be processed by a CNU software receiver (software defined radio). For the performance of the proposed algorithm, results were obtained using the correlation value of the software receiver, and the performance was verified through the detection of a spoofing signal and the detection time of a spoofing signal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.5
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• pp.632-639
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• 2012

Ad-hoc networks can be used various environment, which it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio(CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In conventional CR based ad-hoc network, it uses constant threshold value to detect primary user signal, so the results become not reliable. In this paper, to solve this problem, we apply adaptive threshold value to the CR based ad-hoc network, and adaptive threshold is immediately changed by SNR(Signal to Noise Ratio). From the simulation results, we confirmed that proposed algorithm has the greatly better detection probabilities than conventional CR based ad-hoc network.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.97-103
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• 2012

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. Global speech absence probability (GSAP) derived from likelihood ratio (LR) based on the statistical model is widely used as the feature parameter for VAD. However, the feature parameter based on conventional GSAP is not sufficient to distinguish speech from noise at low SNRs (signal-to-noise ratios). The presented VAD algorithm utilizes GSAP based on Teager energy (TE) as the feature parameter to provide the improved performance of decision for speech segments in noisy environment. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.977-983
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• 2014

A repeat-back jamming signal is an intentionally re-broadcasted GNSS (Global Navigation Satellite System) interference. In this paper, a novel repeat-back jamming detection scheme is proposed. The proposed scheme uses a combined pseudo random noise signal (C-PRN) and is available for a generic GNSS receiver with a single antenna. The C-PRN signal is made by combining several received pseudo random noise signals that had been transmitted from the visible GNSS satellites. Through a Monte-Carlo simulation, the detection probability of a repeat-back jamming signal detected with the proposed scheme is presented.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.228-229
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Proceedings of the KIEE Conference
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• 2008.04b
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• pp.171-173
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.