Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds

Rangaswamy, Muralidhar; Michels, J.H.; Himed, Braham

doi:10.1109/nrc.2002.999736

Cited by 23 publications

(15 citation statements)

References 27 publications

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“…From (10) it is to known that is CFAR with respect to texture component and only has relationship with speckle component. We have known that the speckle component is modeled as a complex Gaussian random vector with zero mean vector and covariance matrix is a toeplitz matrix form.…”

Section: Fixed Point Maximum Likelihood Covariance Estimation Methods mentioning

confidence: 99%

“…In order to construct an adaptive detector in compund-Gaussian clutter environment, the covariance matrix M of NMF detector was substituted by a proper estimator of covariance matrix and then the detector is called NAMF [10]. It is note that the secondary (or training ) data are free of targets or disturbance and independent identical distribution with the data of cell under test (CUT).…”

Section: High-resolution Radar Recived Model and Namf (Normalized Adamentioning

confidence: 99%

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Covariance matrix estimation method in compound Gaussian sea clutter

Zhang

et al. 2011

Proceedings of 2011 IEEE CIE International Conference on Radar

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As the radar resolution has increased, the statistics of the clutter have no longer been observed to be Gaussian.The performance of traditional detectors was decreased badly in non-Gaussian environment. This paper proposed a covariance matrix estimate algorithm of detector in non-Gaussian sea clutter. The technique is based on the fix-point(FP) estimator that the iterative initalization matrix is a toeplitz matrix. In this paper, the proposed technique estimator performance is compared with two other covariance matrix estimators in real sea clutter data.The simulation results show that the proposed method can enhance the detector performance in real sea clutter.

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Section: Fixed Point Maximum Likelihood Covariance Estimation Methods mentioning

confidence: 99%

Section: High-resolution Radar Recived Model and Namf (Normalized Adamentioning

confidence: 99%

Covariance matrix estimation method in compound Gaussian sea clutter

Zhang

et al. 2011

Proceedings of 2011 IEEE CIE International Conference on Radar

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“…Once the vector Ai in Eq. 2 is assumed to follow K-distribution, its characteristic probability density function which is known to be generalized chidistribution can be written as [12], [13], [14], [15], Therefore, the probability density function of the random variable ai == u; may readily be found as, is written by the product of a non-negative random variable ui(i == 0,1,2, ... , L) and a zero-mean complex Gaussian random vector Zi (i == 0, 1, 2, ... , L) as,…”

Section: System Modelmentioning

confidence: 99%

A performance investigation of mobile cellular radio systems using MRC in K-distribution multipath fading environment

Sriv

Chayawan

Kumwilaisak

2005

2005 1st International Conference on Computers, Communications, &Amp; Signal Processing With Special Track on Biomedical Engine

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Diversity combining is frequently seen to use for an improvement of mobile communication network capacity. in different environments including Gaussian and non-Gaussian multipath fading environments. In this paper, we investigate the problem of modelling the fading process by using maximal ratio combining technique in a spherically invariant multipath fading environment. One of the spherically invarian! random processes which has frequently been used for mod~lhng radar systems, K-distribution, is chosen to model the fading process. New expressions of probability density functions and outage probabilities are derived in terms of integrals for the general case and for the cases of equal mean powers of interferers and different mean powers of interferers and may numerically be evaluated using the Gauss-Hermite, Gauss-Laguerre, and GaussLegendre integrations. Some numerical results were done to show the performance of the proposed systems.

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“…In the past years, numbers of non-homogeneity detection (NHD) algorithms [7][8][9][10] have been proposed, such as generalised inner product (GIP) algorithm [7][8][9] and loaded GIP algorithm [10]. Idealistically, these GIP algorithms can exclude those samples which are heterogeneous with most of the initial training samples.…”

Section: Introductionmentioning

confidence: 99%

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

Wang

et al. 2015

IET Radar, Sonar & Navigation

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In space-time adaptive processing (STAP), the required interference covariance matrix is commonly estimated from homogeneous samples. However, the real interference environments are often heterogeneous, which is induced by the fact that the training samples do not share the same interference property with the cell under test (CUT), and thus the interference covariance matrix estimated from the training samples is mismatched with the real one. To improve the performance of STAP in heterogeneous interference environments, this paper proposes a robust training samples selection algorithm which is based on the spectral similarity of the interference. The spectrums of samples are estimated firstly, which are then utilised to calculate the similarity between the interference of the CUT and those of the training samples. Then, the samples whose interference spectrums are similar to that of the CUT are selected as the final training samples, such that the performance of the interference covariance matrix estimated from these samples can be improved significantly. Besides, to avoid the target self-nulling effect, a simple technique is adopted to exclude the samples which are contaminated by target signal. The proposed method is applied to real radar data, and experimental results demonstrate the effectiveness of the proposed method.

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Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds

Cited by 23 publications

References 27 publications

Covariance matrix estimation method in compound Gaussian sea clutter

Covariance matrix estimation method in compound Gaussian sea clutter

A performance investigation of mobile cellular radio systems using MRC in K-distribution multipath fading environment

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

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