• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.7
• /
• pp.1550-1558
• /
• 1997

In this paper, we first propose a modified OS CFAR detector called the order statistics cell averaging(OSCA) CFAR detector and anlyze its performance for a Rayleigh target in homogeneous backgrounds, clutter edges, and satistics smallest of(OSSO) CFAR detectors for a Rayleigh target to nonhomogeneous environments. Computer simulation results show that the OSCA CFAR detector has superior performance to OS, OSGO, and OSSO CFAR detectors in homogeneous and multiple target environments. And the proposed detector shows its robustness for fast detection because it requires falf the processing time of the OS CFAR detector.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.1
• /
• pp.65-71
• /
• 2012

An OS-CFAR (Ordered Statistics CFAR) based on a sorting algorithm is useful for automotive radar systems in a multi-target situation. However, while the typical cell-averaging CFAR has low computational complexity, the OS-CFAR has much higher computation effort. In this paper, we design the new OS-CFAR architecture with a low computational effort. In the proposed method, since one time sorting processing is performed for the decision of the CFAR threshold, the whole processing effort can be reduced. When the fast sorting technique is employed, the computing time of the proposed OS-CFAR is always much shorter compared with typical OS-CFAR method regardless of the data size. We also present the processing result of proposed architecture using the real radar data.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.5
• /
• pp.123-128
• /
• 2011

In this paper, we design the CA-CFAR processor using a root-square approximation approach and a fixed-point operation to improve hardware complexity and reduce computational effort. We also propose CA-CFAR processor with multi-window, which is capable of concurrent parallel processing. The proposed architecture is synthesized and implemented into the FPGA and the performance is compared with the conventional processor designed by root-square libarary licensed by FPGA corporation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.2
• /
• pp.164-169
• /
• 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 Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.187-193
• /
• 2021

In order to solve the problem of false terrains caused by environmental interferences and tunneling effect in the conventional multi-beam seafloor terrain detection, this paper proposed a seafloor topography detection method based on fast two-dimensional (2D) Censored Mean Level Detector-statistics Constant False Alarm Rate (CMLD-CFAR) method. The proposed method uses s cross-sliding window. The target occlusion phenomenon that occurs in multi-target environments can be eliminated by censoring some of the large cells of the reference cells, while the remaining reference cells are used to calculate the local threshold. The conventional 2D CMLD-CFAR methods need to estimate the background clutter power level for every pixel, thus increasing the computational burden significantly. In order to overcome this limitation, the proposed method uses a fast algorithm to select the Regions of Interest (ROI) based on a global threshold, while the rest pixels are distinguished as clutter directly. The proposed method is verified by experiments with real multi-beam data. The results show that the proposed method can effectively solve the problem of false terrain in a multi-beam terrain survey and achieve a high detection accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.104-113
• /
• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• Proceedings of the IEEK Conference
• /
• 2000.06a
• /
• pp.252-255
• /
• 2000

In this paper, a fast pseudo-noise (PN) code acquisition with novel adaptive architecture is presented in direct-sequence spread- spectrum (DS-SS) systems. Since an existing acquisition system has a fixed correlation tap size and threshold value, this system cannot adapt to various mobile communication environments and results in a low detection probability or a high false alarm rate and long acquisition time. Therefore, if a correlation tap size and a threshold value can be controlled adaptively according to received signals, problems of ail existing system will be solved. The system parameter varies adaptively by using constant false alarm rate (CFAR) algorithm well known in a field of detection and proposed signal-to-noise ratio (SNR) measurement system. By deriving formulas of the proposed system, the performance is analyzed.

• Korean Journal of Remote Sensing
• /
• v.31 no.2
• /
• pp.111-125
• /
• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.