• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.164-169
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• 2010

Since the range and Doppler resolution of the synthetic aperture radar(SAR) image becomes very high, the target detection accuracy can be significantly increased, but the computational burden is also increased. The conventional single-cell based CFAR detector performs the target detection on every single cell basis, thus it causes the serious increment of the computational load. In this paper, the improved two-step MCA-CFAR detector is proposed for the improvement of the target detection as well as the reduction of computational load: the first step is to use the MCA-CFAR, and the second step is to use the single-cell based CFAR detection in the expected target area for final decision. The performance of the proposed algorithm is compared with the conventional single-cell based CFAR and MCA-CFAR on SAR images.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.1
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• pp.8-16
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• 2005

This paper describes target detection performance of millimeter wave radar that exits on non-stationary target detection schemes in the ground clutter conditions. The comparison of various CFAR process schemes such as CA(Cell-Average)-CFAR, GO(Greatest Of)/SO(Smallest Of)-CFAR, and OS(Order Statistics)-CFAR performance are applied. Using matlab software, we show the performance and loss between target detection probability and signal to noise ratio. This paper concludes the OS-CFAR process performance is better than any others and satisfies the optimal detection probability without loss of detection in the homogeneous clutter, When range bins increase.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.47-52
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• 2011

Constant false alarm rate (CFAR) is an automatic detection algorithm for active sonar system. Among several CFAR algorithms, ordered statistics (OS) CFAR has the best performance over cell averaging (CA), smallest of (SO), greatest of (GO) algorithms at non-homogeneous environments. However, OS CFAR has the disadvantage of bad detection performance in multiple target conditions. We suggest an ordered statistics ratio (OSR) CFAR algorithm that is robust to multiple target environments. The proposed and conventional schemes are compared with computer simulations.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.3
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• pp.37-46
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• 2012

In RADAR and SONAR detection systems, noise environment can be classified into homogeneous and heterogeneous environment. Especially heterogeneous environments are modelled as target masking and clutter edge. Since the variability-index (VI) CFAR, a composed CFAR algorithm, dynamically selects one of the mean-level algorithms based on the VI and the MR (mean ratio) test, it is robust to various environments. However, the VI CFAR still suffers from lowered detection probabilities in heterogeneous environments. To overcome these problems, we propose an improved VI CFAR processor where TM (trimmed mean) CFAR and a sub-windowing technique are introduced to minimize the degradation of the detection probabilities appeared in heterogeneous environments. Computer simulation results show that the proposed method has the better performance in terms of detection probability and false alarm probability compared to the VI CFAR and single CFAR algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.719-729
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• 1992

An improved MXTM (maximum trimmed mean) -CFAR (constant false alarm rate) processor is proposed to reduce false alarm rates In detecting radar targets and Its performance character is ticsare analyzed to be compared with those of other CFAR processors. The proposed MXTM-CFAR processor is obtained by combining the GO (greatest of ) -CFAR processor reducing excessive falsealarm rate at riutter edges with the TM-CFAR processor showing good performances In homo-geneous Jnonhornog eneous background. Performance analyses have been done by computing detection probability, constant false alarm rate and detection thresholds under the homogeneous or multiple target environments and at the clutter edges. Analysis results how that the proposed CFAR processor maintains its performance as good as those of,05(order statistics) and TM-CFAR inhomogeneous and multiple target environments and Can reduce the false alarm rate at clutter edges. Overall computing time hfs been also reduced.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.50-57
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• 1992

This paper proposes and analyzes a new CFAR(Constant False Alarm Rate) detector called the EXGO(Excision Greatest Of)-CFAR. This is the combination of the EXCA(Excision Cell Averaging)-CFAR that shows a good performance under the influence of interferences and the GO(Greatest Of)-CFAR that fights well with clutter edges. For the performance analysis, the formulas for the detection probability and the false alarm probability are derived and computed, and the results are compared with other existing CFAR detectors. Our analysis shows that the proposed EXGO-CFAR considerably improves the false-alarm-rate performance of the EXCA-CFAR at clutter edges while maintaining the high detection probability performance of the EXCA-CFAR in the homogeneous and/or interference noise environment.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.890-895
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• 2004

This paper describes constant alarm rates process of millimeter wave radar that exits on non-stationary target detection schemes in the ground clutter conditions. The comparison of various CFAR processes such as CA(Cell-Average)-CFAR, GO(Greatest Of)/SO(Smallest Of)-CFAR and OS(Order Statistics)-CFAR performance are applied. Using matlab software, we show the performance and loss between detection probability and signal to noise ratio. When rang bins increase, this results show the OS-CFAR process performance is better than any others and satisfies the optimal detection probability without loss of detection in the homogeneous clutter.

• ETRI Journal
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• v.16 no.1
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• pp.17-34
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• 1994

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize $(2^N-1)$ kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.