Constant False Alarm Rate Approach Research Articles

Analysis of fine-scale dynamics of the Drygalski ice tongue in Antarctica using satellite SAR data

ABSTRACT This study focused on the analysis of the time variability of the morphology of the Drygalski ice tongue (DIT), Antarctica, using – for the first time – satellite synthetic aperture radar (SAR) images. A time series of Sentinel-1 interferometric wide swath SAR imagery collected from 2016 to 2021 is considered and an unsupervised methodology, based on a global threshold constant false alarm rate approach, is used to extract the boundary between the DIT and the surrounding ice-free/ice-infested sea water. The most prominent rifts/fractures identified on the extracted profiles and the ice front are selected to analyse the DIT time variability. The feature tracking allows deriving information on the morphological evolution of the DIT, including the annual displacement and average surface velocity. Experimental results show that the DIT ice front calls for a relatively stable motion trend towards the sea with an average surface velocity of about 670 m per year. Our outcomes show a fairly good agreement with similar studies appeared in the scientific literature, which are mostly based on optical imagery.

Adaptive Superpixel-Level CFAR Detector for SAR Inshore Dense Ship Detection

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

SummaryOne of the main challenges of spectrum sensing (SS) in the cognitive radio network (CRN) is selection of threshold for captivating the sensing decision, and such an issue becomes more challenging especially at low signal‐to‐noise ratio (SNR) because it affects the sensing performance and throughput of cognitive user (CU). However, the sensing decision of individual CU may not be accurate due to fading effects in the sensing environment, and significant improvement is accomplished with the cooperation of users. Therefore, in this paper, we have investigated the performance of energy detector SS (EDSS) technique in the CRN under fading environment with cooperative and noncooperative SS scenario. Further, we have introduced the concept of critical SNR (γc) to select the appropriate threshold and based on which the algorithms are proposed to enhance the throughput and reduce the total error (sensing error) probability of CU at different SNR (γ). From the results, we have accomplished that at the low SNR region (γ ≤ γc), throughput is maximum with constant false‐alarm rate (CFAR) threshold selection approach in the cooperative scenario while in the high SNR region (γ > γc), its value is maximum with minimizing the error probability (MEP) threshold selection approach in the noncooperative scenario. Moreover, the sensing error is reduced with cooperation, and threshold selection with MEP approach outperforms CFAR approach for γ ≤ γc. Further, the proposed approach outperforms the other approaches in terms of throughput and sensing error (total error) probability in the Rayleigh and Nakagami‐m fading environments.

Joint Energy Detection and Massive Array Design for Localization and Mapping

The adoption of massive arrays for simultaneous localization and mapping or personal radar applications enables the possibility to detect and localize surrounding objects through an accurate beamforming procedure. Unfortunately, when a classical constant false alarm rate approach accounting for ideal-pencil beam pattern is adopted, ambiguities in signal detection could arise due to the presence of side-lobes which can cause non-negligible errors in target detection and ranging. To counteract such effect, in this paper we propose a joint threshold-array design approach, where the antenna characteristics are taken into account to best set the threshold and to guarantee the desired detection and ranging performance at the non-coherent receiver section. In order to consider realistic arrays impairments, we focus our attention on the number of antenna elements and of phase shifter bits used for beamforming as key players in defining a trade-off between structural complexity, well-defined radiation pattern, and localization performance. Simulation and measurement results show that the number of bits per phase shifter can be relaxed in favor of a simpler array design, if the number of antennas is sufficiently high and the side-lobes are kept within a suitable level allowing a desired robustness to interference signals.

Constant false alarm rate detection of point targets using distributed sensors

The problem of orthogonalization of multiple satellite images, focusing at the same field of view, is studied in this paper for constant false alarm rate (CFAR) point target detection. Information fusion tech- niques are incorporated. The Karhunen-Loeve (K-L) orthogonalization method offers the best CFAR detection performance. However, it re- quires central fusion. It is shown that a version of the Gram-Schmidt (G-S) orthogonalization method, which preprocesses data in a pipeline form, offers a comparable CFAR detection to that of the K-L method. Point target CFAR detection is carried out by employing various fusion approaches on the orthogonal data. Sensor level fusion with quality in- formation is shown to be preferable when the proposed sequential G-S orthogonalization is applied. The proposed CFAR approach is applied to dissimilar sensors and avoids overloading the communication channel transmitting only in the case of target detection. Trade-off studies and experimental results on real and simulated data are presented. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)01002-2)

CFAR for homogeneous part of high-resolution imagery

In high-resolution imaging, weak target pixel amplifiers may not be detected in the presence of clutter containing strong nonhomogeneities, when conventional approaches are used. The authors describe a constant false alarm rate (CFAR) approach that avoids the elimination of these significant target returns. The nonhomogeneous clutter as well as the weak target components are detected with this approach. The targets could then be discriminated from the homogeneities by discrimination techniques. It is shown how the lower amplitude components of the background noise and homogeneous clutter (which have Rayleigh statistics) can be detected in the presence of strong homogeneous clutter and targets. The average level of the homogeneous component is then determined using these lower-amplitude components. This CFAR approach avoids having a CFAR on the strong nonhomogeneities as well as the homogeneous component. The avoidance is what yields the ability to detect weak target pixel amplitudes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>