Target Detection in High-Resolution SAR Image Via Minimum Bayes Ternary Hypothesis Detector

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.

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.

An intelligent CFAR (Constant False Alarm Rate) circuit can effectively combine the advantages of linear and LOG CFAR circuits by switching from linear to LOG or vice-versa and also deciding the algorithm that suits a particular environment best based on various parameters such as clutter, distance, optimum number of range cells etc automatically. This paper presents a comprehensive tabulation of results which can be used to implement the intelligent CFAR circuit. The MATLAB simulations were performed for seven algorithms both LOG and linear for all possible environments (homogeneous/non-homogeneous). They were done for various sizes of range cells (16, 32 and 64). They were also simulated for different clutter environments such as Rayleigh, Weibull and log-normal. The results including the number of targets, no of false alarms, no of missed targets along with the multiplication factors were tabulated for 1000 iterations.

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.

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

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.

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed — the MLCψfits into a mine line pattern with fixed mine spacing; random -the MLCψfits into a mine line pattern with random mine spacing; or false alarm-ψ-the MLCψdoes not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-spaced mine lines. Extensive Matlab simulations characterize the algorithm's performance across a wide range of operating environments and simulated minefield configurations. In total, ten replications each of 1,367 different configurations have been evaluated. These configurations include three different water depths (10 ft., 6 ft. and 200 ft.), six false notional alarm rates (1, 2,5,10,15 and 20 false alarms per sq. NM), three false alarm distributions (uniform, sloped, and striped), three sensor probabilities of detections (0.4, 0.6 and 0.9), three target confidence scores (0.6,0.8 and 1.0) and four false alarm confidence scores (0.5, 0.6, 0.8 and 1.0). The range of simulation parameters was selected in part as an attempt to push the algorithm past its acceptable performance limits. However, the algorithm performed remarkably well even under the worst conditions. For MLC sets with false alarm rates as high as 20 per square nautical mile, the algorithm correctly classifies over 98% of the MLCs as mines and detects 97% of the mine lines. Furthermore, it reduces the false alarm rate from 20 to 0.2 per square nautical mile with a false mine line rate of 0.14 false lines per square nautical mile. The algorithm achieves even lower false alarm rates when the initial false alarm rate is less than 20. The automated mine line detection discovers many obscure lines that are virtually undetectable to the naked eye.

For the vehicle-mounted forward-looking synthetic aperture radar (SAR), detecting algorithms should be effective, robust and real-time. This paper proposed a fast statistic detecting algorithm based on circulatory iteration. We also presented feasible methods to select the optimal parameters of constant false alarm rate (CFAR) detector. Experiment results showed that our algorithm had high detecting rate, low false alarm rate, good adaptability and much less computation load.

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.

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)

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.

The measurement of optical fibre vibration is a key part of optic fibre pre-warning system, which has gradually focused on phase-sensitive optical time-domain reflectometer. However, for this instrument, false alarm rate is very high and some unstable intrusion signals cannot be detected by using its fixed threshold method in the actual application. It needs to develop new vibration detection method to overcome the above defect. The vibration signals normally consist of three parts, that is, noise, interference and intrusion signals. After a large number of data analysis, the authors find that the system noise is time varying and follows the Rayleigh distribution. Hence, the authors innovatively use the constant false alarm rate (CFAR) method to detect this type of intrusion. Considering interference is also time varying and diverse, a good detection performance cannot be obtained only by using the conventional CFAR. For this reason, a background homogeneity adaptive CFAR (BHA-CFAR) method is further proposed to detect the vibration signals in this study. The BHA-CFAR consists of two detectors, cell averaging CFAR (CA-CFAR) detector and greatest-of/smallest-of CFAR (GO/SO-CFAR) detector. A parameter, homogeneity of background, is estimated first to classify the surrounding. Then CA-CFAR and GO/SO-CFAR are optionally used according to the surrounding is homogeneous or heterogeneous, respectively. This new detection method can adapt to any background surrounding and has a good detection performance. In order to check the feasibility and validity of the BHA-CFAR method, several experiments were carried out in Da Gang oilfield. The detection results show that the proposed method can provide a good tradeoff between the detection performance and computation time.

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