Target detection of RADAR deals with different and manifold problems over few decades. The detection capability is one of the most significant factors in RADAR system. The main aim of detection is to increase probability of detection while decreasing rate of false alarm. The threshold of detection is modified as a function of the receiver noise level to keep a fixed rate of false alarm. Constan...

The paper introduces a new class of adaptive CFAR methods to cope with the problem of outliers due to the presence of clutter edges and interfering targets. A fundamental distinction between the proposed approach and existing adaptive CFAR approaches is that in order to maintain robust performance the former uses information on positions at which estimated outlier-free cells appear in the full ...

0018-9251188/1100-0678 $1.00 (C 1988 IEEE Ordered statistics (OS) constant false alarm rate (CFAR) algorithm, introduced by Rohling [ I ] , is a CFAR technique with special immunity to interfering targets. CFAR usually suffers some detection loss due to the adaptive threshold concept. Furthermore, the presence of strong returns among the cells used to determine the background noise or clutter (...

In order to improve the radar’s detection performance under Pearson distributed background clutter, the problem of designing CFAR detectors is studied in this paper. Three kinds of distributed fuzzy CA scheme and two kinds of OS scheme are proposed. The numerical simulations prove that CA CFAR detector based on algebraic sum fusion rule provides more robust detection performance in homogenous b...

This paper presents the development and performance evaluation of a particular Multi-Layer Perceptron neural network (MLP) classifier for radar target detection in a noisy, non-Gaussian environment using CFAR (Constant False Alarm Rate). The Technique, architecture details and principle of working of the MLP-CFAR detector training algorithm are presented. A comparison of the MLP-CFAR performanc...

Target detection is important content in Synthetic Aperture Radar (SAR) image applications. There is a common target detection method, which is called the Constant False Alarm Ratio (CFAR) detector. But it must satisfy the condition under strong contrast ratio between target area and background clutter area. In fact, it is very difficult for SAR images to satisfy the condition. In order to enha...

Constant False Alarm Rate (CFAR) detection in Synthetic Aperture Radar (SAR) is the rst step in most ATR and image exploitation systems. In this paper several CFAR algorithms and their implementation on a 1-D SIMD array processor are investigated. We primarily focus on CFAR algorithms using the Weibull clutter model, but algorithms assuming K-distributed clutter should have similar implementati...

Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to t observed data better a...

Among ship detection methods for SAR image, constant false alarm rate (CFAR) is the most important one. However, several factors, such as detector parameter and distribution of ocean clutter, affect the performance of CFAR detection. This paper proposes a novel hierarchical complete and operational ship detection approach based on detector parameter estimation and clutter pixel replacement, whi...

Real-time performance of adaptive digital signal processing algorithms is required in many applications but it often means a high computational load for many conventional processors. In this paper, we present a configurable hardware architecture for adaptive processing of noisy signals for target detection based on Constant False Alarm Rate (CFAR) algorithms. The architecture has been designed ...