• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.761-767
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• 2009

The detection of target echoes in a sonar image is usually difficult since reverberation is originated by the returns reflected around the boundary and volumes. Under the scenario of the target presence around the reverberation, the detection performance of existing algorithms is degraded. Since they have a similar statistical features. But proposed detector gives improvement existing algorithms Under this scenario. In this paper, 4 segmentation contrast box algorithm using maximum contrast value is proposed based on statistical segmentation, which gives better detection performance in the sense of reducing false alarms. The simulations validate the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.42-46
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• 2010

Energy detection based spectrum sensing compares the energy of a received signal from a primary user with a detection threshold and decides whether it is active or not in the frequency band of interest. Here the detection threshold depends on not only a target false alarm probability but also the level of the noise energy in the band. So, if the noise energy changes, the detection threshold must be adjusted accordingly to maintain the given false alarm probability. Most previous works on energy detection for spectrum sensing are based on the assumption that noise energy is known a priori. In this paper, we present a new energy detection scheme updating its detection threshold under the assumption that the noise is white, and analyze its detection performance. Analytic results show that the proposed scheme can maintain a target false alarm rate without regard to the noise energy level and its spectrum sensing performance gets better as the time bandwidth product of the signal used to estimate the noise energy increases.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.192-198
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• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

• 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 the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.6-14
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• 1996

In this paper we present an algorithm to discriminate an underwater target under track against an acoustic counter-measure(ACM) source, based on a sequential hypothesis test. The ACM source is separated from the target under track and generates, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target-tracking and to help the true target evade a pursuer. The algorithm uses as a test statistic a function of the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting position of the ACM source. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target against an ACM source very fast with a high probability of success.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.92-95
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• 1991

Constant false alarm rate(CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. The well known "cell averging" (CA) CFAR processor is known to yield best performance in homogeneous case, but exhibits severe performance in the presence of an interfering target in the reference window or/and in the region of clutter edges. The "order statistics"(OS) CFAR processor is known to have a good performance above two nonhomogeneous cases. The modified OS-CFAR processor, known as "trimmed mean"(TM) CFAR processor performs somewhat better than the OS-CFAR processor by judiciously trimming the ordered samples. This paper proposes and analyzes the performance of a new CFAR processor called the "maximum trimmed mean"(MX-TM) CFAR processor combining the "greatest of"(GO) CFAR and TM-CFAR processors. The MAX operation is included to control false alarms at clutter edges. Our analyses show that the proposed CFAR processor has similar performance TM- and OS-CFAR processors in homogeneous case and in the precence of interfering targets, but can control the false rate in clutter edges. Simulation results are presented to demonstrate the qualitative effects of various CFAR processors in nonhomogeneous clutter environments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.27-32
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• 2010

77GHz FMCW(Frequency Modulation Continuous Wave) radar system has been used for automotive active safety systems. In typical automotive radar, the moving target detection and clutter cancellation including stationary targets are very important signal processing algorithms. This paper proposed the moving target detection algorithm which improve the detection probability and reduce the false alarm rate. First, the proposed moving target beat-frequency extraction filter is used in order to suppress clutter, and then the data association is applied by using the extracted moving target beat-frequency. Then, the zero-Doppler target is eliminated to remove the rest of clutter.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.2
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• pp.75-82
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• 2002

It is useful to detect the tracking error with an optimal view in the presence of measurement origin uncertainty. In this paper, after the investigation of the targer error dependent on the detection threshold as well as the detection and false alarm probabilities in a clutter environment, a new algorothm that optimizes the threshold of validation region for target trackinf is proposed. The performance of the algorithm is demonstrated through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.424-429
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• 1992

A practical adaptive tracking filter for a maneuvering target is proposed in this paper by combining a modified input estimation technique with pseudo-residuals and a decoupled tracking filter in line-of-sight Cartesian coordinate system. Since the adaptive tracking filter has decoupled structure and computes maneuver input estimates for each axis separately, it requires much less computations compared with the coventional tracking filter with MIE technique without degrading performance. Also, since pseudo-measurement noises in line-of-sight Cartesian coordinate system are much less correlated compared with those of inertial Cartesian coordinate system, the proposed tracking filter produces less false alarms or miss detections to improve the performance.

• 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.