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

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.4
• /
• pp.229-237
• /
• 2017

In this paper we propose an auto tonal detection method which exploits short term stationary when targets located in a detection beam area and then additional methods are proposed in order to reduce the computational complexity of the proposed method. The proposed method is adaptive to input signals and robust against interference caused by multiple targets because it compares an expected value of input signals with a threshold value which are estimated from a single beam while signals are keep stationary. The performances of the proposed methods are evaluated using by simulated data and acquired data from real ocean. The proposed method has shown better performance than conventional CFAR (Constant False Alarm Rate) methods.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.7
• /
• pp.258-266
• /
• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.11 no.1
• /
• pp.66-74
• /
• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.5
• /
• pp.55-63
• /
• 2011

This paper describes new moving target detection technique combining two algorithms to detect targets and reject clutters in video frame images for surveillance system: One obtains the region of moving target using phase correlation method using $N{\times}M$ sub-block images in frequency domain. The other uses adaptive threshold using learning weight for extracting target candidates in subtracted image. The block region with moving target can be obtained using the characteristics that the highest value of phase correlation depends on the movement of largest image in block. This technique can be used in camera motion environment calculating and compensating camera movement using FFT phase correlation between input video frame images. The experimental results show that the proposed algorithm accurately detects target(s) with a low false alarm rate in variety environment using the receiver operating characteristics (ROC) curve.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.2
• /
• pp.175-181
• /
• 2014

In this paper, we propose a robust clutter pre-thresholding method using background adaptive Threshold Map for the clutter rejection in the complex coastal environment. The proposed algorithm is composed of the use of Threshold Map's and method of its calculation. Additionally we also suggest an automatic decision method of Thresold Map's update. Experimental results on some sets of real infrared image sequence show that the proposed method could remove clutters effectively without any loss of detection rate for the aim target and reduce processing time dramatically.

• The Journal of the Acoustical Society of Korea
• /
• v.34 no.2
• /
• pp.157-162
• /
• 2015

In this paper, we propose an audio fingerprinting using robust hash based on the MCLT (Modulated Complex Lapped Transform) peak-pair. In existing methods, the robust audio fingerprinting is not generated if various distortions occurred; time-scaling, pith-shifting and equalization. To solve this problem, we used the spectrum of the MCLT, an adaptive thresholding method for detection of prominent peaks and the novel hash function in the audio fingerprinting. Experimental results show that the proposed method is highly robust in various distorted environments and achieves better identification rates compared to other methods.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.57-65
• /
• 2022

International attempts to smuggle narcotics and explosives using ship or aircraft cargoes are on the rise. With the recent increase in the number of detection cases of narcotics and explosives in Korea, it is important to detect dangerous material (narcotics and explosives) through container searches at ports and airports, which are the main routes. This paper proposes a technique to detect dangerous material in cargo containers using the sampled output signal of ion mobility spectroscopy (IMS). The proposed technique estimates parameters such as a threshold, a window length, and a noise level for ion detection of the target dangerous material by using known materials in the initialization stage. The estimated parameters are used to detect the ions of the dangerous target material inside the containers. The proposed technique can be applied when the peak value of the IMS signal and the ion mobility are varying due to container environments.