Adaptive detection of range spread targets with orthogonal rejection

Maio, Antonio De; Farina, Angelo; Gerlach, K.

doi:10.1109/taes.2007.4285365

Cited by 51 publications

(26 citation statements)

References 17 publications

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“…The Neyman-Pearson optimal detector is the ratio of the densities under H 1 and H 0 hypothesis. Hence, according to (14), the likelihood ratio test can be expressed as…”

Section: The Optimal Detectormentioning

confidence: 99%

“…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 [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]. Moreover, an orthogonal rejection MGLRT (OR-MGLRT) is introduced in [15] for range-spread target detection in correlated Gaussian noise and used as the second stage of a double threshold receiving structure.…”

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 Neyman-Pearson optimal detector is the ratio of the densities under H 1 and H 0 hypothesis. Hence, according to (14), the likelihood ratio test can be expressed as…”

Section: The Optimal Detectormentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

Gao¹,

Zhan²,

Wan³

2014

PIER

View full text Add to dashboard Cite

show abstract

“…Compared with those detectors in white Gaussian noise, detecting range-spread targets in nonstationary clutter environments is more complicated and has recently received much more attentions [6][7][8][9][10][11][12][13][14][15]. Those methods mainly focus upon steady range-spread targets, such as air targets in steady flight or slow ship target in sea clutter.…”

Section: Introductionmentioning

confidence: 99%

“…Under this model, the GLRT-based detectors suffer from additional performance loss, because the rotation incurs different Doppler shifts in the individual cells and too many model parameters need to be estimated from the received data corrupted by strong clutter. Also, the spatially adaptive methods [13][14][15] were proposed for radars using antenna arrays to detect range-spread targets.…”

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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Two adaptive detectors for range-spread targets in non-Gaussian clutter

Jian

et al. 2011

Sci. China Inf. Sci.

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This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both adaptive detectors are assessed and the influence of mismatched steering vector on detection probability is analyzed. The experimental results show that the adaptive detectors have an acceptable loss with respect to the nonadaptive counterparts. Furthermore, the ANSDD-GLRT converges much faster than the ASDD-GLRT, however, the latter rejects the misaligned signals more effectively. Both detectors perform well for the uniformly-distributedenergy target, but degenerate for the fluctuating targets. In addition, the correlation between target scatterers results in an additional gain for low signal-to-clutter power ratio.Keywords high-resolution radar, range-spread target detection, constant false alarm rate, generalized likelihood ratio test, spherically invariant random vector CitationHe Y, Jian T, Su F, et al. Two adaptive detectors for range-spread targets in non-Gaussian clutter.

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Adaptive detection of range spread targets with orthogonal rejection

Cited by 51 publications

References 17 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

Two adaptive detectors for range-spread targets in non-Gaussian clutter

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