CFAR detection in clutter with unknown correlation properties

Raghavan, R. S.; Qiu, Haiquan; McLaughlin, D.J.

doi:10.1109/7.381913

Cited by 52 publications

(17 citation statements)

References 6 publications

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“…We assume that the average transmission power at each antenna is the same (34) In this step, the total transmission energy is . The noise vector is characterized statistically as (35) We assume here that the clutter is homogeneous; the noise vector (35) describes both the homogeneous clutter and the additive noise in the received radar returns. We should note that this is a very simplified assumption on the clutter.…”

Section: A Tr-mimo Data Collection and Processingmentioning

confidence: 99%

“…Next, we can compute the detection probability (97) Under simplifying conditions, for example, the radar signal model given in [35] and [36], one can develop closed form expressions for and . In our case, the time reversal radar signal model in (66) becomes, approximately, a nonzero mean, real Gaussian signal immersed in zero-mean complex Gaussian noise.…”

Section: A Tr-mimomentioning

confidence: 99%

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Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

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Abstract-Time reversal explores the rich scattering in a multipath environment to achieve high target detectability. Multiple-input multiple-output (MIMO) radar is an emerging active sensing technology that uses diverse waveforms transmitted from widely spaced antennas to achieve increased target sensitivity when compared to standard phased arrays. In this paper, we combine MIMO radar with time reversal to automatically match waveforms to a scattering channel and further improve the performance of radar detection. We establish a radar target model in multipath rich environments and develop likelihood ratio tests for the proposed time-reversal MIMO radar (TR-MIMO). Numerical simulations demonstrate improved target detectability compared with the commonly used statistical MIMO strategy.

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Section: A Tr-mimo Data Collection and Processingmentioning

confidence: 99%

Section: A Tr-mimomentioning

confidence: 99%

Time Reversal in Multiple-Input Multiple-Output Radar

Jin

Moura

O’Donoughue

2010

IEEE J. Sel. Top. Signal Process.

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“…Recently, Raghavan et al (1995) addressed several interesting aspects of target detection in the present of nonhomogenous interference. In their analysis, the correlation properties of the interference remains unknown.…”

Section: Applications Of Ranking and Selection Theorymentioning

confidence: 99%

Screening among Multivariate Normal Data

Chen

Melvin

Wicks

1999

Journal of Multivariate Analysis

107

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This paper considers the problem of screening k multivariate normal populations (secondary data) with respect to a control population (primary data) in terms of covariance structure. A screening procedure, developed based upon statistical ranking and selection theory, is designed to include in the selected subset those populations which have the same (or similar) covariance structure as the control population, and exclude those populations which differ significantly. Formulas for computing the probability of a correct selection and the least favorable configuration are developed. The sample size required to achieve a specific probability requirement is also developed, with results presented in tabular form. This secondary data selection procedure is illustrated via an example with applications to radar signal processing. Academic PressAMS 1991 subject classifications: 62E15, 62F07, 62H10. Key words and phrases: hypergeometric function in matrix argument, indifference zone approach, eigenvalue, least favorable configuration, multivariate normal, probability of a correct screening, radar signal processing, ranking and selection, subset selection approach.

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“…The statistical properties of the secondary data must be similar to those of the interference in the primary data, to be used in the absence of the knowledge about the covariance matrix. Raghavan, Qiu and McLaughlin in [11] considered the detection of an unknown N-dimensional complex vector whose correlation properties are unknown. They derived the GLR test for the rapid-fluctuating radar signal detection in clutter and noise and showed that their proposed GLR test is equivalent to UMPI [11].…”

Section: Introductionmentioning

confidence: 99%

Rapid-fluctuating Radar Signal Detection with Unknown Arrival Time

Derakhtian

Tadaion

Gazor

et al. 2007

2007 IEEE International Conference on Signal Processing and Communications

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We study the rapid-fluctuating radar signal detection, where the arrival time of the received signal, i.e., the range cell index of the target, as well as the complex amplitude of the signal and the noise are unknown. We show that even in Additive White Gaussian Noise (AWGN) environment, Uniformly Most Powerful Invariant (UMPI) test does not exist. Instead, the UMPI detector in known SNR is used as an upper performance bound for performance evaluation of any invariant detector performance. In addition, we derive Generalized Likelihood Ratio (GLR) detectors for this signal in AWGN with unknown noise variance and also in clutter with unknown covariance matrix. The GLR test statistic for AWGN represents the ratio of the maximum power over all cells to the total power. The GLRT for a clutter environment is also a power ratio which is the maximum over all range cells of the Euclidean norms of the spatially whitened observed sequence. Simulation results demonstrate that the performance of the proposed GLR test in AWGN is very close to the upper bound performance, i.e., to the UMPI test in known SNR.

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CFAR detection in clutter with unknown correlation properties

Cited by 52 publications

References 6 publications

Time Reversal in Multiple-Input Multiple-Output Radar

Time Reversal in Multiple-Input Multiple-Output Radar

Screening among Multivariate Normal Data

Rapid-fluctuating Radar Signal Detection with Unknown Arrival Time

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