A radar detector with enhanced range estimation capabilities for partially homogeneous environment

Aubry, Augusto; Maio, Antonio De; Foglia, Goffredo; Chen, Hao; Orlando, Danilo

doi:10.1049/iet-rsn.2013.0316

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…As a final remark, we highlight that possible future research tracks might include the possibility to account for oversampled data at the receiver side to improve detection performance for partially homogeneous environments [40,41].…”

Section: Discussionmentioning

confidence: 99%

Correlation feature-based detector for range distributed target in sea clutter

Yuan

Hong²,

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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In this paper, a novel correlation feature-based detector is proposed to deal with the challenging problem of detecting a range-distributed target embedded in nonstationary sea clutter. It is well known that sea clutter consists of a speckle component modulated by texture. The nonstationary property of sea clutter is mainly reflected in texture, but its correlation characteristic is mainly dominated by the speckle. Therefore, this detector using the correlation feature of sea clutter can effectively eliminate the negative effect of the nonstationary property of sea clutter on the detection performance. In addition, in order to get rid of the limitation of the knowledge shortage of target scatterers, the modified entropy method is applied to adaptively estimate the number of target scatterers. The real range distributed target data and high-resolution sea clutter are used to evaluate the detector, and the experimental results show that it attains a better detection performance in comparison with several existing detectors. Comparing with the feature-based detector, the proposed detector can effectively reduce the computational complexity.

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Section: Discussionmentioning

confidence: 99%

Correlation feature-based detector for range distributed target in sea clutter

Yuan

Hong²,

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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“…The maximum a posteriori, minimum mean square error, and minimum mean absolute error peak estimators are utilised in [3, 4, 6–8]. In addition, the effects of several specific factors are also investigated, such as the signal oversampling [9–11] and the sweep nonlinearity in practical LFMCW system [12]. An adjacent‐cell amplitude‐comparison (ACAC) method is introduced in [13], wherein only the scheme of engineering interest is given and the mathematical principles are lacking.…”

Section: Introductionmentioning

confidence: 99%

High‐precision estimation of target range, radial velocity, and azimuth in mechanical scanning LFMCW radar

Zhou

Xie

et al. 2017

IET Radar, Sonar & Navigation

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“…In [29], Orlando and Ricci focused on STAP and proposed new GLRT detectors for small targets which take advantage of such a spillover to improve localisation and enhance detection for the CUT. More recently, the same approach is used in [30], where two adaptive receivers are proposed with better detection and localisation performances, by jointly exploiting the oversampling of the noisy returns and the spillover of target energy to adjacent range samples.…”

Section: Introductionmentioning

confidence: 99%

Parametric space–time detection and range estimation of a small target

Chen

Gazor

Orlando³

et al. 2015

IET Radar, Sonar & Navigation

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In this study, the authors deal with the problem of parametric detection for relatively small targets using space-time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.

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A radar detector with enhanced range estimation capabilities for partially homogeneous environment

Cited by 15 publications

References 27 publications

Correlation feature-based detector for range distributed target in sea clutter

Correlation feature-based detector for range distributed target in sea clutter

High‐precision estimation of target range, radial velocity, and azimuth in mechanical scanning LFMCW radar

Parametric space–time detection and range estimation of a small target

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