• Journal of electromagnetic engineering and science
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• v.13 no.3
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• pp.151-157
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• 2013

Marine radars are affected by sea and rain clutters, which can make target discrimination difficult. The clutter standard deviation and improvement factor are applied using multiple parameters-moving speed of radar, antenna speed, angle, etc. When a radar signal is processed, a Data Matrix Bank (DMB) filter can be applied to remove sea clutters. This filter allows detection of a target, and since it is not affected by changes in adjacent clutters resulting from a multi- target signal, sea state clutters can be removed. In this paper, we study the level for clutter removal and the method for target detection. In addition, we design a signal processing algorithm for marine radars, analyze the performance of the DMB filter algorithm, and provide a DMB filter algorithm design. We also perform a DMB filter algorithm analysis and simulation, and then apply this to the DMB filter and cell-average constant false alarm rate design to show comparative results.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1073-1080
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• 2011

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.

• 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.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.485-488
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• 2003

A conventional correlation target tracker is analysed with a simple mathematical approach. And, we will propose a correlation measure with selective attentional property in order to overcome the false-peak problem of the conventional methods. Various experimental results show that the proposed correlation measure is able to reduce considerably the probability of false-peaks degraded by the correlation between background images of a reference block and a distorted and noisy sensor input image.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.853-864
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• 2017

This paper proposes a false alarm rate reduction technique for detection of small targets in a terrestrial environment. CFAR algorithm is useful in homogeneous background, but it is not easy to detect targets in non-homogeneous background. In particular, when the clutter power is not significantly different from the target signal, it is difficult to detect the target due to high false alarm rate. To solve these difficulties, this study presents the false alarm rate reduction technique based on CFAR algorithm, matched filter and binary integration technique. The parameters are studied through the theoretical analysis and the validity of the proposed study is examined by the test results.

• 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.

• Journal of electromagnetic engineering and science
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• v.19 no.2
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• pp.107-114
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• 2019

Alert-confirm detection is a highly efficient method to improve phased array radar search performance. It comprises sequential detection in two steps: alert detection, in which a target is detected at a low detection threshold, and confirm detection, which is triggered by alert detection with a longer dwell time to minimize false alarms. This paper provides a design method for applying the alert-confirm detection to multifunctional radars. We find optimum dwell times and false alarm probabilities for each alert detection and confirm detection under the dual constraints of total false alarm probability and maximum allowable dwell time per position. These optimum values are expressed as a function of the mean new target appearance rate. The proposed alert-confirm detection increases the maximum detection range even with a shorter frame time than that of uniform scanning.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1839-1845
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• 2016

Detecting remote targets is important to active protection system (APS) or infrared search and track (IRST) applications. In normal situation, the well-known constant false alarm rate (CFAR) detector works properly. However, decoys in APS or closely spaced targets in IRST degrade the detection capability by increasing background noise level in the CFAR detector. This paper presents a context aware CFAR detector by the intensity sorting and selection of background region to reduce the effect of neighboring targets that lead to incorrect estimation of background statistics. The existence of neighboring targets can be recognized by intensity sorting where neighboring targets usually show highest ranks. The proposed background statistics (mean, standard deviation) estimation method from median local pixels can be aware of the background context and reduce the effects of the neighboring targets, which increase the signal-to-clutter ratio. The experimental results on the synthetic APS sequence, real adjacent target sequence, and remote pedestrian sequence validated that the proposed method produced an enhanced detection rate with the same false alarm rate compared with the hysteresis-CFAR (H-CFAR) detection.

• 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.

• ETRI Journal
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• v.25 no.3
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• pp.171-178
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• 2003

To improve the detection performance of surveillance radars with polarization diversity, we developed an adaptive polarimetric processor and compared it with other polarimetric processors. We derived our adaptive polarimetric processor, called the polarization discontinuity detector (PDD), from the generalized likelihood ratio (GLR) test principle for the unspecified target component. We derived closed-form expressions of its probabilities of detection and false alarm, and compared its performance to that of the adaptive polarization canceller (APC) and Kelly's GLR processor. The PDD had a performance similar to Kelly's GLR in Gaussian clutter, and both the PDD and Kelly's GLR, which have embedded constant false alarm rates (CFARs), outperformed the APC, especially when the target polarization state was close to the clutter's polarization state. The important difference is that the PDD is much simpler than Kelly's GLR for hardware/software implementation, because the PDD does not require a costly two-parameter filter bank to cover the unknown target polarization state as Kelly's GLR does.