Detection of a spatially distributed target in white noise

Gerlach, K.; Steiner, Michael; Lin, Feng

doi:10.1109/97.596885

Cited by 123 publications

(64 citation statements)

References 4 publications

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“…The detection strategies in Gaussian-distributed clutter have been investigated in [11][12][13][14][15]. The N out of M (N/M ) detector and the integration detector are proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], the optimal and suboptimal receiver frameworks are designed in the white Gaussian clutter background, which is considered as the theoretical basis of rangespread target detection. In [13], a reasonable distribution for the target scatterer density is employed in the range-spread target detection, and the scatterer density dependent generalized likelihood ratio test (SDD-GLRT) is proposed. For the singular covariance matrix, a modified generalized likelihood ratio test (MGLRT) is designed in [14].…”

Section: Introductionmentioning

confidence: 99%

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Range-Spread Target Detection in Compound Gaussian Clutter With Reciprocal of the Square Root of Gamma Texture

Gao¹,

Zhan²,

Wan³

2014

PIER

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Abstract-In this paper, the range-spread target detection in compound Gaussian clutter with reciprocal of the square root of gamma (RSRG) texture is investigated. The RSRG distribution has been proved to be a good model for texture component of extremely heterogeneous radar clutter. Taking this compound Gaussian model as a spherically invariant random process (SIRP), the Neyman-Pearson optimal detector for the range-spread target detection with known target amplitude is derived firstly. By replacing the ideal target amplitude with its maximum likelihood estimate (MLE), the generalized likelihood ratio test (GLRT) is then obtained. The statistical property of the texture component is taken into account in both of these two detectors, which makes the detectors computationally complicated. A suboptimal generalized likelihood ratio test based on order statistics (OS-GLRT) is finally proposed by substituting the texture component with its MLE. The OS-GLRT makes use of some largest observations from the range cells occupied by the most likely target scatters. The performance assessment conducted by Monte Carlo simulation validates the effectiveness of the proposed detectors.

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“…The detection strategies in Gaussian-distributed clutter have been investigated in [11][12][13][14][15]. The N out of M (N/M ) detector and the integration detector are proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Range-Spread Target Detection in Compound Gaussian Clutter With Reciprocal of the Square Root of Gamma Texture

Gao¹,

Zhan²,

Wan³

2014

PIER

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show abstract

“…For range-spread targets in steady flight or in a short integration interval, the target returns can be modeled well by the simple tensor model in (4). Based upon that, the GLRT-based detectors [6] can coherently integrate target returns in non-Gaussian clutter for high performance detection.…”

Section: Comparison With Mcom Detector and Glrt Detectormentioning

confidence: 99%

“…The methods mentioned above exploit the energy distribution of target HRRPs while not exploiting the waveform characteristics. The two recent non-coherent detectors in white Gaussian noise, the SSD-GLRT detector [4] and the MCOM detector [5], exploit the waveform characteristics.…”

Section: Introductionmentioning

confidence: 99%

Entropy-based detection of manoeuvring range-spread targets in homogeneous weather clutter

Shui

2013

EURASIP J. Adv. Signal Process.

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This paper proposes a waveform-cross-entropy (WCEN)-based detection scheme to detect manoeuvring range-spread targets in homogeneous weather clutter. The input of the detector is composed of complex-valued high resolution range profiles (HRRPs) from a train of coherent pulses in the observation window. The observation window contains a detection window and a reference window, where the received data in reference window is clutter-only secondary data. The detector consists of temporal approximate whitening filtering for clutter suppression followed by the waveform-cross-entropy-based detection. The former is operated on the received vector at each range cell to whiten the clutter and enhance target returns. Then, the WCEN of each pair of filtered power-HRRPs is calculated to capture their sparsity, similarity and energy. The average of the WCENs of all the pairs is utilized as the test statistic to decide whether a target is present or not. Finally, we assess the proposed detector by the raw radar target data collected by high range resolution radar and simulated weather clutter. The experimental results show that the WCEN-based detector outperforms the existing detectors in detection performance for manoeuvring range-spread targets.

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“…In order to solve the so-called collapsing loss which will lead to a performance loss for range-spread targets detection [4], many detectors are presented. Assuming the a priori scatterer density of range-spread targets, a detector dependent of spatial density is investigated [5]. Given that the scattering characteristics of range-spread targets are significantly different, completing the aforementioned method is difficult.…”

Section: Introductionmentioning

confidence: 99%

Binary integration nonparametric detection for range-spread targets in distributed terahertz radar network under unknown clutter

Liu

Min

et al. 2016

EURASIP J. Adv. Signal Process.

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In this study, to detect person-borne concealed threats in range profiles under the circumstance of unknown clutter, we propose a binary integration nonparametric detection method based on the generalized sign (GS) detector for range-spread targets in a distributed terahertz radar network (DTRN). In the detection, the length of range-spread targets and the number of dominant scatterers on range-spread targets are considered and adaptively estimated. Furthermore, the GS detection method is applied to maintain a constant false alarm rate (CFAR) under the circumstance of unknown clutter. The detection performance of the proposed method for single terahertz radar and DTRN are both examined with the data synthesized by real range-spread targets data and real clutter data. Experimental results show that the proposed method is effective, and for a given false alarm probability, the DTRN exhibits better detection performance than the single terahertz radar.

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Detection of a spatially distributed target in white noise

Cited by 123 publications

References 4 publications

Range-Spread Target Detection in Compound Gaussian Clutter With Reciprocal of the Square Root of Gamma Texture

Range-Spread Target Detection in Compound Gaussian Clutter With Reciprocal of the Square Root of Gamma Texture

Entropy-based detection of manoeuvring range-spread targets in homogeneous weather clutter

Binary integration nonparametric detection for range-spread targets in distributed terahertz radar network under unknown clutter

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