Optimality of the cell averaging CFAR detector

Gandhi, P.P.; Kassam, S.A.

doi:10.1109/18.335950

Cited by 72 publications

(30 citation statements)

References 6 publications

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“…Gandhi and Kassam 51 show that the performance of the CA-CFAR processor approaches that of the NP detector in that the limit of the number of pixels in the boundary ring approaches infinity under the condition that homogeneity is maintained in the boundary ring. Thus, CA-CFAR achieves asymptotically optimal performance under these (theoretical) assumptions.…”

Section: Cfar-based Methodsmentioning

confidence: 99%

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

El-Darymli

McGuire

Power

et al. 2013

J. Appl. Remote Sens

176

112

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Abstract. Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidean distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Cfar-based Methodsmentioning

confidence: 99%

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

El-Darymli

McGuire

Power

et al. 2013

J. Appl. Remote Sens

176

112

View full text Add to dashboard Cite

show abstract

“…In most radar signal processing literature to date, a Cell Averaging (CA) CFAR detector is the preferred method of target detection [16]- [18]. A CA-CFAR processor is used on the experimental data presented in Section V-B.…”

Section: Radar Detectionmentioning

confidence: 99%

Radar Noise Reduction Based on Binary Integration

Lühr

Adams

2015

IEEE Sensors J.

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Artículo de publicación ISIShort range radars can provide robust information about their surroundings under atmospheric disturbances, such as dust, rain, and snow, conditions under which most other sensing technologies fail. However, this information is corrupted by received power noise, resulting in false alarms, missed detections, and range/bearing uncertainty. The reduction of radar image noise, for human interpretation, as well as the optimal, automatic detection of objects, has been a focus of radar processing algorithms for many years. This paper combines the qualities of the well established binary integration detection method, which manipulates multiple images to improve detection within a static scene, and the noise reduction method of power spectral subtraction. The binary integration method is able to process multiple radar images to provide probability of detection estimates, which accompany each power value received by the radar. The spectral subtraction method then utilizes these probabilities of detection to form an adaptive estimate of the received noise power. This noise power is subtracted from the received power signals, to yield reduced noise radar images. These are compared with state-of-the-art noise reduction methods based on the Wiener filter and wavelet denoising techniques. The presented method exhibits a lower computational complexity than the benchmark approaches and achieves a higher reduction in the noise level. All of the methods are applied to real radar data obtained from a 94-GHz millimetre wave FMCW 2D scanning radar and to synthetic aperture radar data obtained from a publicly available data set.Fondecyt Project 1110579; Conicyt-DAAD PCCI-2012009, and AMTC, Universidad de Chil

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“…In order to provide a reasonable fit to the amplitude statistics of the background clutter, several CFAR detectors with different local statistics have been proposed in the past. For example, in homogenous clutter environment the mean-level detectors are widely used, an example of which is cell averaging CFAR (CA-CFAR) (Gandhi, 1994). Other commonly used detectors are order statistic (OS-CFAR) (Peihong, 1996), greatest of CFAR (GO-CFAR) and smallest of CFAR (SO-CFAR) (Gandhi, 1988), optimal Weibull CFAR (OW-CFAR) (Anastassopoulos, 1995), the concept of variability index CFAR (VI-CFAR) (Smith, 1997), as well as combinations of the previous ones, for solving the problems of clutter edge and interference of neighbour target.…”

Section: Cfar Detection Approachmentioning

confidence: 99%

Detection of Ship Targets in Polarimetric Sar Data Using 2d-Pca Data Fusion

Theoharatos

Makedonas

Fragoulis

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

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Optimality of the cell averaging CFAR detector

Cited by 72 publications

References 6 publications

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Radar Noise Reduction Based on Binary Integration

Detection of Ship Targets in Polarimetric Sar Data Using 2d-Pca Data Fusion

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