Comparison and Simulation of Linear/LOG CFAR Algorithms

The constant false alarm rate (CFAR) detector is a representative detection method for radar systems. Its detection performance is severely depending on clutter environments such as homogeneous and non-homogeneous. Depending on the environment, we have to adopt different CFAR algorithms. Thus, in this paper, we propose the environment detection algorithm using the Kalman filter. By using this algorithm, we can select the optimal CFAR detector for each environment.

Constant false alarm rate (CFAR) algorithms are widely used in radar signal processing for object detection in cluttered and noisy environments and their fast hardware implementation is required in many of the real time applications. For that purpose, a parametrizable and runtime reconfigurable generator of CFAR detectors, featuring fully streaming I/O data interface, is captured inside Chisel hardware design language. Generator provides up to seven different CFAR algorithms available to choose from in compile time and throughout runtime configurability. Besides the algorithm choice, a wide range of settings, such as I/O data type and bit-widths, reference and guard window sizes, linear or logarithmic processing modes, edge handling methods, as well as some implementation specific parameters, are provided, hence enabling quick and efficient design space exploration. Several generator instances are tested and verified on a commercially-available FPGA board in conjunction with off-the-shelf radar transceivers thus proving that instances from the proposed peak detector generator can be effectively used whenever low latency processing performance is mandatory.

A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

A new constant false alarm rate (CFAR) detection processor is presented that exhibits a robust CFAR performance in the condition of non-homogenous environment. Its performance would be independent of background noise power. Since the processor can track noise power variation and adopt itself to the new background conditions. The new CFAR algorithm, called as Adaptive Switching CFAR (AS-CFAR) is analysed and simulated CFAR to demonstrate its improved robustness in non-homogenous environments. (3 pages)

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 to deal with parallel/pipeline processing and to be configured for three versions of CFAR algorithms, the Cell-Average, the Max and the Min CFAR. The architecture has been implemented on a Field Programmable Gate Array (FPGA) with a good performance improvement over software implementations. Results are presented and discussed.

摘要: 针对合成孔径雷达(Synthetic aperture radar, SAR)图像目标检测中恒虚警率(Constant false alarm rate, CFAR)算法的广泛应用, 进行CFAR检测的快速算法分析具有重要研究价值. 首先概述了当前国内外对SAR图像CFAR检测快速算法的研究现状; 然后分别从快速预筛选和迭代计算方法两个方面对各类快速算法的实时性及性能进行了分析总结, 给出了四种基本CFAR检测器的迭代计算公式, 并提出了一种研究CFAR检测快速算法的基本框架, 现有的快速算法均可纳入该理论框架予以分析; 最后, 以经典双参数CFAR检测算法为例, 对该基本框架进行仿真实现和性能分析, 验证了其可行性与检测性能．结果表明: 新的CFAR检测快速算法基本框架充分融合了快速预筛选思想和迭代计算方法的优势, 有效提高了CFAR算法在SAR图像检测应用中的执行效率. 关键词: 合成孔径雷达图像 / 恒虚警率检测 / 快速算法 / 快速预筛选 / 迭代计算

A novel semantic Minimum Bayes ternary hypothesis detector based on the Rayleigh distribution is presented to decrease detection false alarm rate of synthetic aperture radar image. The method not only employs the strong scattering features of the target which is used in constant false alarm rate (CFAR) but employs the shadow features of the target which is used in the Power Ratio detector. Furthermore, target, shadow, and clutter regions are merged into the ternary image to estimate Rayleigh distribution parameters and weights. Meanwhile, the semantic relationship between the strong scattering features and the shadow features is established to partly reduce the false alarm targets. By the experiments on the Mini-SAR image of the 4-in resolution, it is shown that our method outperforms the semantic CFAR method and CFAR algorithm by a much lower false alarm rate.

In the sense of likelihood ratio test (LRT), a new type of distributed constant false alarm rate (CFAR) scheme-CCAWCA(censored cell-averaging -R-weighted cell averaging) CFAR detector is presented. Its characteristic is that censored cell-averaging (CCA) CFAR algorithms are used in local processors to form the estimation of SNR of local observations, and then the estimation transmitted to the data fusion center (DFC). Finally, the fusion center makes the final decision based on the weighted cell averaging (WCA). Since the weights are adjusted according to different SNR adaptively, the proposed detector can be available in the case that the target echo and noise/clutter have different level for every sensor. In addition, it does not need a priori knowledge about the interference in order to perform well. Furthermore, unlike the OS-CFAF the tolerance of interfering targets is restricted in appointed k value’s. Under Swerling 2 assumption, the analytic expression of detection probability and false alarm probability are derived.

Ship detection SAR images is a key technology in maritime surveillance applications. We show here the main issues arising in the processing SAR data for ship detection and how techniques, based on Constant False Alarm Rate (CFAR) algorithms, have evolved to face them and to achieve operational performances. This evolution followed the advancements in the satellite SAR system technology that have increased acquisition capability in terms of always greater number of images and modes, better spatial resolutions and wider swaths. We describe an operational CFAR ship detection algorithm having some novel features with respect to CFAR algorithms available in literature to improve quality, robustness and processing time.

While analyzing radar signals, the constant false alarm rate (CFAR) method is crucial for detecting targets. Target detection has to be very accurate, particularly in nonhomogeneous situations like multi-target or clutter edge conditions. There are several types of CFAR algorithms, which are used in radar applications that need to identify targets in real time. These algorithms require a lot of computational time. In this research, a programmable hardware architecture has been described for the implementation of multiple CFAR algorithms. The implementation will have the ability to swap between several CFAR algorithms: average, max, min, and order statistics. The proposed implementation of CFAR algorithms is tested using adaptive processing of noisy signals for target recognition in real time. Variants of CFAR algorithms are built within one architecture, which is created to handle parallel processing. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. The Xilinx Vivado toolset’s synthesis, post-place and route are to find the best point to linearly speedup processing and resource usage.

Target detection in the multiscale situation where there exit multiple ship targets with different sizes is a challenging task due to the mismatch of the sizes of ship targets and fixed windows. A new constant false alarm rate (CFAR) algorithm based on variable window for ship target detection in SAR images is proposed. First, the multiscale local contrast measure is introduced to estimate the ship target size without any prior knowledge about ships. Second, the size of neighborhood area is adaptively set and a transform algorithm is designed to enhance the contrast between targets and background. Finally, CFAR detection is implemented by adopting variable window to gain the accurate ship targets. Experimental results indicate that the proposed algorithm has better performance compared with other CFAR detection algorithms.

CFAR detectors have been utilized in radar systems where the clutter environment is partially unknown and/or has varying statistical properties (e.g., power). In such instances, the performance of the optimal detector deteriorates significantly, and a nonparametric or constant false alarm rate (CFAR) detector is designed to be insensitive to changes in the density functions of the clutter. An effective method of accomplishing this is to use local estimates for the threshold corresponding to the unknown (or varying) parameters of the clutter distribution. Recently, order statistic (OS) processors have been shown to perform robustly (in terms of CFAR loss) for inhomogeneous clutter observations, although these processors utilize order statistics in an inefficient manner in terms of estimating the power of the clutter distribution. For a more efficient threshold estimate and, consequently, more robust detector, the censored maximum likelihood (CML) and best linear unbiased (BLU) estimates are applied toward CFAR detection. In particular, these methods are utilized for estimation of the scale parameter of the Weibull-distributed clutter with known shape parameter. The design of these CFAR detectors and the probability of detection performance under Lehmann's alternative hypothesis are mathematically analyzed.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

It is necessary for automatic detection radars to be adaptive to variations in background clutter in order to maintain a constant false alarm rate (CFAR). A CFAR based on an ordered statistic technique (OS CFAR) has some advantages over the cell-averaging technique (CA CFAR), especially in clutter edges or multiple target environments; unfortunately the large processing time required by this technique limits its use. The authors present two new OS CFARs that require only ahlf the processing time. One is an ordered statistic greatest of CFAR (OSGO), while the other is an ordered statistic smallest of CFAR (OSSO). The OSGO CFAR has the advantages of the OS CFAR with only a negligible increment to the CFAR loss. >

When a flying object tracks a target, the location and velocity of the target are estimated based on a target detection process using radar. In general, the constant false alarm rate (CFAR) is used in the target detection process. However, if a high power interference wave is received in addition to the object signal, the target cannot be detected, so that tracking fails. This is caused by an uncontrolled increase in the threshold level at the time of target detection due to the interference component mixed in the reference cells. After the CFAR process, tracking of the target is possible by the use of a tracking filter as long as the existing target signal component is detected, even if the interference wave is evaluated as the target signal component. In this paper, an interference adaptive CFAR is discussed in which the CFAR process is carried out and the interference component mixed in the reference cell is detected and eliminated. The relationship between the threshold coefficient and the false alarm probability and a computation method for the CFAR loss are formulated in this paper. By means of the derived relationships, several case studies are carried out and the nature of the interference adaptive CFAR is discussed. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 1, 80(10): 29–37, 1997

Constant false alarm rate (CFAR), a target detection method commonly used in the radar systems, has an inconsistent performance against various environments. For improving the radar detectability, this paper proposes a novel scheme of radar target detection using neural network-based adaptive selection CFAR. The proposed method employs cell-averaging, ordered-statistic, greatest-of and smallest-of CFAR thresholds as the basis of references. The pattern of those threshold values combined with the cell under test signal value will be identified and classified by the neural network to compute the raw threshold. Then, the final threshold is selected depending on the nearest value between raw and four referenced CFARs. The performance of the proposed method is examined against three possible cases of the radar systems including homogeneous background, multiple targets and clutter boundary. The result of this research shows that the proposed method outperforms the classical CFARs due to the adaptive selection algorithm can select properly among referenced CFARs against the given cases particularly in the homogeneous and multiple target environments.