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

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.22-27
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• 2014

In this paper, we analyzed performance of FFT based on pilot sensing scheme for DTV signal in fading environments. In order to detect the present and absent for advanced television system committee digital television (ATSC DTV), a pilot detection scheme based on FFT is employed. Also, these signals are applied to each fading environment. For evaluating the spectrum sensing performance, detection probability is derived. When threshold is decided, we refer to constant false alarm rate (CFAR) scheme. From simulation results, it is confirmed that the spectrum sensing performance is improved as increase of the false alarm rate. The result of this paper can be applied to implement the spectrum sensing part of cognitive radio (CR) systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.67-73
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• 2020

The clustering algorithm is the grouping of similar objects. In radar system, it is mainly used to group adjacent hits using the CFAR algorithm results. However it is difficult to separate adjacent targets by a general clustering method. In this paper, we describe how to separate adjacent targets using double clustering method. First, we execute a range direction clustering. And we find the inflection point and separate it. Next, we execute a doppler direction clustering using range clustering results. This method makes the computation time less change even if the target increases by range-doppler clustering respectively.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.187-193
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• 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.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.3-9
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• 1999

In this paper, the characteristics of LFM and CW signals in active sonar environment is investigated. CA, OS and TM CFAR processors are applied to the received CW/LFM signals which are plotted in the range/doppler domain. The performances of detection are analyzed. Particularly, using the real data, we certified that the results of the experiments are identical with the theoretical performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.482-490
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• 2017

The radar signal processing procedure is divided into the pre-processing such as frequency down converting, down sampling, pulse compression, and etc, and the post-processing such as doppler filtering, extracting target information, detecting, tracking, and etc. The former is generally designed using FPGA because the procedure is relatively simple even though there are large amounts of ADC data to organize very quickly. On the other hand, in general, the latter is parallel processed by multiple DSPs because of complexity, flexibility and real-time processing. This paper presents the radar signal processor design using FPGA which includes not only the pre-processing but also the post-processing such as doppler filtering, bore-sight error, NCI(Non-Coherent Integration), CFAR(Constant False Alarm Rate) and etc.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.22-31
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• 2013

In this paper, we propose a method that estimates the channel response from underwater communication signals with MMSE (Minimun Mean Squared Error) and detects dominant components automatically based on power of response components using CFAR (Constant False Alarm Rate). Statistical characteristics are analyzed with variation of magnitude and phase and time coherence via experimental data obtained by drifting transmitter and receiver. We show that bit error rate has small difference, 1.2 times, compared with the case using every channel information estimated within data period when estimation and equalization is performed with extracted characteristic obtained by the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.10
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• pp.1077-1083
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• 2009

To reject the Doppler frequency spectrum dispersion of clutter caused by high-speed antenna rotation of MTI radar system due to terrain characteristics, signal processing parameters(MTI filter constant, M/N detector ration, K-factor and offset of CFAR) are adjusted for the optimal elimination of the ground clutter. For this investigation, logging equipment is designed and utilized for the collection of classified ground clutter data. Test case is devised through Matlab simulation for the classified analysis and optimization of clutter rejection. Then indoor radar test and outside test in accordance with terrain characteristics are repeatedly performed for the verification of the test. This whole process is through the evolutional development model and repeated for the optimization. Final result is that ground-clutter rejection capability is 5.6 times(7.5 dB) better than that of existing radar system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.83-91
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• 2005

We analyze the performance of an adaptive hybrid search code acquisition algorithm for direct-sequence code division multiple access (DS-CDMA) systems under slowly-moving mobile environments. The code acquisition algorithm is designed to provide the desired feature of constant false alarm rate (CFAR) to cope with nonstationarity of the interference in CDMA forward links. An analytical expression for the mean acquisition time is first derived and the probabilities of detection, miss, and false alarm are then obtained for frequency-selective Rayleigh fading environments. The fading envelope of a received signal is assumed to be constant over the duration of post-detection integration (PDI), considering slow fading environments. Finally, the performance of the designed code acquisition algorithm shall be evaluated numerically to examine the effect of some design parameters such as the sub-window size, size of the PDI, decision threshold, and so on, considering cdma2000 environments.