A CFAR Adaptive Subspace Detector for First-Order or Second-Order Gaussian Signals Based on a Single Observation

Generalized Robust Shrinkage Estimator and Its Application to STAP Detection Problem

“…The joint processing of these space-time data, by appropriate two-dimensional adaptive filtering methods, allows stronger interference/clutter rejection and therefore improved target detection. [22] and analyzed in [23]- [26]. It is given by:…”

“…One important consequence is that one can always estimate the scatter matrix instead of the covariance matrix. Moreover, for applications that are invariant to a scale factor, like for instance DOA estimation with the MUltiple SIgnal Classification (MUSIC) [21] or detection using the Adaptive Normalized Matched Filter (ANMF), firstly introduced by [22] and analyzed in [23]- [26], the resulting performance is the same.…”

Automatic Ship Detection in Single-Pol Sar Images Using Texture Features in Artificial Neural Networks

“…To date, most detection algorithms try to ensure a constant false-alarm rate (CFAR) (Crisp., 2004), (Brusch et al, 2011), (Fei et al, 2012), (Novak et al, 1989) and (Novak et al, 1993), and for this purpose, the clutter distribution is always locally estimated so that the decision threshold can be adaptively determined according to a given probability of false alarms (PFA). A large number of CFAR detectors have been proposed with different local statistics of the background clutter (Crisp, 2004), ( Pourmottaghi et al, 2012) and (Liu et al, 2011). A conventional constant false alarm rate (CFAR) detector searches ship targets adaptively in the whole imagery with a sliding window, which consumes much time and cannot meet the near real-time processing requirement ( Xiangwei at al., 2012).…”

Adaptive polarimetric detection method for target in partially homogeneous background

“…Differently, the AMF designs the test statistic by first assuming that noise is known a prior and then replaces the NCM with its MLE. They are usually referred to as one-step and two-step detectors, respectively [3,4].…”

“…In this background, noise in primary data is assumed to have the same covariance structure with different power, which is also referred to as partially homogeneous background. Many detectors, such as matched subspace detector (MSD) [7][8][9] and adaptive subspace detectors (ASDs) [4,[10][11][12], are proposed to deal with the target detection problem in partially homogeneous background. Besides, many detectors, such as texture-free GLR test (TF-GLRT) [13] and threshold-systembased detector (TD) is defined and used in [14], are proposed Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/sigpro for target detection in other non-homogeneous background, e.g., compound-Gaussian background.…”