• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.1
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• pp.20-27
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• 2010

In this paper, we presented a signal model of STAP and actual environment of clutter. The novel estimation method of clutter covariance matrix using median value is proposed to overcome serious performance degradation after NHD in nonhomogeneous clutter. Eigen value characteristic is improved through diagonal loading. Target detection ability and SINR loss of the proposed method though MSMI statistic is also compared with conventional method using average value. The simulation results, confirm the proposed method has better performance than others.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.72-79
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• 2009

This paper describes the performance evaluation of NHD(nonhomogeneity detector) for STAP(space-time adaptive processing) airborne radar according to various normalization methods in the nonhomogeneous clutter environment. In practice, the clutter can be characterized as random variation signals, because it sometimes includes signals with very large magnitude like impulsive signal due to the system environment. The received interference signals are composed of homogeneous and nonhomogeneous data. In this situation, NHB is needed to maintain the STAP performance. The normalization using the NHD result is an effective method for removing the nonhomogeneous data. The optimum normalization can be performed by a representative value considered with a characteristic of the given data, so we propose the K-means clustering algorithm. The characteristic of random variation data due to nonhomogeneous clutters can be considered by the number of clusters, and then the representative value for selecting the homogeneous data is determined in the clustering result. In order to reflect a characteristic of the nonstationary interference data, we also investigate the algorithm for a calculation of the proper number of clusters. Through our simulations, we verified that the K-means clustering algorithm has very superior normalization and target detection performances compared with the previous introduced normalization methods.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.92-95
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• 1991

Constant false alarm rate(CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. The well known "cell averging" (CA) CFAR processor is known to yield best performance in homogeneous case, but exhibits severe performance in the presence of an interfering target in the reference window or/and in the region of clutter edges. The "order statistics"(OS) CFAR processor is known to have a good performance above two nonhomogeneous cases. The modified OS-CFAR processor, known as "trimmed mean"(TM) CFAR processor performs somewhat better than the OS-CFAR processor by judiciously trimming the ordered samples. This paper proposes and analyzes the performance of a new CFAR processor called the "maximum trimmed mean"(MX-TM) CFAR processor combining the "greatest of"(GO) CFAR and TM-CFAR processors. The MAX operation is included to control false alarms at clutter edges. Our analyses show that the proposed CFAR processor has similar performance TM- and OS-CFAR processors in homogeneous case and in the precence of interfering targets, but can control the false rate in clutter edges. Simulation results are presented to demonstrate the qualitative effects of various CFAR processors in nonhomogeneous clutter environments.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.1
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• pp.89-97
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• 2011

We analyze the performance of various techniques to overcome degradation of performance of STAP caused by nonhomogeneous clutter. The performance of NHD that used to eliminate outliers from nonhomogeneous clutter is improved by using the projection statistics(PS) that is robust to multiple outliers. The method of clutter covariance matrix estimation using a median value and the conventional method are also investigated and then compared. From the simulation results of STAP, the method of clutter covariance matrix estimation using a median value shows better performance than the conventional method for the calculation of the SINR loss, and MSMI for the single target and the multiple targets regardless of the NHD methods.

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

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.8
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• pp.849-856
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• 2014

This paper proposes a new efficient CFAR algorithm. The structure of the proposed CFAR is relatively simple as compared with the OS-CFAR or ML-CFAR which are considered to deal with the nonhomogeneous environment such as clutter and multiple targets. The proposed algorithm is effectively applied to the radar signal processor with reduced computation burden. The relationship between the threshold and PFA of the proposed CFAR is derived analytically. The CFAR loss of the proposed CFAR algorithm is compared with CA-CFAR and OS-CFAR based on both SNR and ADT(Average Detection Threshold).