Distributed Cell-averaging Cfar Detection of Signal Returns

Linearly combined order statistic (LCOS) constant false-alarm rate (CFAR) detectors are examined for efficient and robust threshold estimation applied to exponentially distributed background observations for improved detection. Two optimization philosophies have been employed to determine the weighting coefficients of the order statistics. The first method optimizes the coefficients to obtain efficient estimates of clutter referred to the censored maximum likelihood (CML) and best linear unbiased (BLU) CFAR detectors. The second optimization involves maximizing the probability of detection under Swerling II targets and is referred to as the most powerful linear (MPL) CFAR detector. The BLU-CFAR detector assumes no knowledge of the target distribution in contrast to the MPL-CFAR detector which requires partial knowledge of the target distribution. The design of these CFAR detectors and the probability of detection performance are mathematically analyzed for background observations having homogeneous and heterogeneous distributions wherein the trade-offs between robustness and detection performance are illustrated. >

In this paper, we analyze the performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds. We employ the ordered statistics (OS) CFAR detectors as local detectors. In the homogeneous background, we maximize the global probability of detection for a given fixed global probability of false alarm by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, we analytically analyze the performance of the detection system and compare its performance with the performance of the distributed cell-averaging (CA) CFAR detection system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper develops a method to model the two parameter Constant False Alarm Rate (CFAR) detector as an intensity test. For optimality we show that the shape of the stencil should be matched to the radial intensity profile of the target. Using principal component analysis (PCA) we show experimentally that the first gamma kernel is a good approximation to the target profile, which explains the good results of the (gamma) -CFAR detector, and may lead to configurable stencils for better detectability.

Two novel constant false alarm rate (CFAR) detectors, the And-CFAR and Or-CFAR, are proposed, The two new CFAR detectors improve the conventional cell averaging CFAR (CA-CFAR) and order statistics CFAR (OS-CFAR) by making full use of the cell information. The novel CFAR processors combine the result of the CA-CFAR and OS-CFAR to get a better detection performance. In a homogeneous background, the mathematical medals of the two new CFAR detectors are derived and their performance has been evaluated and compared with that of CA-CFAR and OS-CFAR.

In this paper, we focus on the performance improvement of constant false alarm rate (CFAR) detector in heterogeneous Compound-Gaussian background. This paper is motivated by the fact that the detectors' performance degradation when an unknown located clutter edge exists in the reference window that divide the data samples into two different independent and identically distributed (IID) Compound-Gaussian distribution. To account for this issue, we propose an automatic clutter edge estimation algorithm based on goodness of fit (GoF) which can select IID data with the cell under test (CUT), and we also suggest a CFAR detector (CFARD) uses this clutter edge estimation algorithm as preprocessing to enhance the detection performance around clutter edges. Simulations are provided to demonstrate the performance of the proposed CFARD in comparison with Ordered-Statistic-CFARD (OS-CFARD).

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.

This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a-priori information provided by a geographic information system (GIS), chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes are analysed in the presence of real radar data, collected by the McMaster IPIX radar, also in comparison with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a-priori information available about the sensed environment.

In this paper, a reduced-rank multistage Wiener filter based constant false alarm rate (CFAR) detector is applied for spectrum sensing in cognitive radio. Spectrum sensing is an essential component in cognitive radio. In order to enhance the performance of primary user signal detection under the multipath fading environment, a multiple antenna technique is employed at the secondary user. In addition, the general sidelode canceller (GSC) is utilized prior to the detection to suppress the effect of interference and noise. And then the adaptive matched filter (AMF) CFAR detector is employed for the secondary user. For the purpose of reduction in computational complexity and reliable performance, reduced-rank processing is a well-known technique under the condition of finite sample support in short sensing time. Numerical results show that the reduced-rank multistage Wiener filter based AMF CFAR detector outperforms the full-rank filter.

In high-resolution synthetic aperture radar (SAR) images, the target size may be much larger than the resolution of SAR and the background clutter statistics are generally not Gaussian-distributed. But traditional constant false alarm rate (CFAR) detection algorithm, which based on the Gaussian-distributed model, performs only one test cell at a time and ignores the mutuality information with other cells in the target, so the performance of the CFAR detection algorithm can be substantially degraded. In the paper, an extend target detection method based on the generalized compound model is proposed. Unlike traditional CFAR detection method which distinguish test cell from the background only on the basis of energy contrast, the extend target detection is sensitive to both the contrast and the distribution of the target. We demonstrate the improved performance using the extend target detection method in the real high-resolution SAR images.

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.

Two types of distributed constant false alarm rate (CFAR) detection using binary and fuzzy weighting functions in fusion center are developed. In the two types of distributed detectors, it was assumed that the clutter parameters at the local sensors are unknown and each local detector performs CFAR processing based on ML and OS CFAR processors before transmitting data to the fusion center. At the fusion center, received data is weighted either by a binary or a fuzzy weighting functions and combined according to deterministic rules, constructing global test statistics. Moreover, for the Weibull clutter, the expression of the weighting functions, based on ML and OS CFAR processors in local detectors, is obtained. In the binary type, we analyzed various distributed detection schemes based on maximum, minimum, and summation rules in fusion center. In the fuzzy type, we consider the various distributed detectors based on algebraic product, algebraic sum, probabilistic OR, and Lukasiewicz t-conorm fuzzy rules in fusion center. The performance of the two types of distributed detectors is analyzed and compared in the homogenous and nonhomogenous situations, multiple targets, or clutter edge. The simulation results indicate the superiority and robust performance of fuzzy type in homogenous and non homogenous situations.

For high‐resolution synthetic aperture radar (SAR), constant false alarm rate (CFAR) detectors are widely used to separate targets from background and it is proved that generalised gamma distribution ( ) can model the non‐homogeneous clutter appropriately. However, CFAR detectors based on possess two problems. First, the methods for solving highly non‐linear equations of resort to numerical iterative algorithms, which are computationally expensive. To avoid this, the authors present a novel analytic solution for parameter estimation of by exploiting a third‐order approximation of the polygamma function. This novel analytic solution can result in more accurate parameter estimation and can fit a wider range of the parameters. The second problem is that in a multi‐target SAR image, some target pixels may be classified as clutter pixels. To select pixels of interest, an iterative sliding window approach is often used in CFAR. They analyse the relationship between the detection probability and the number of iterations, and prove that this strategy can reduce the miss rate and false detection rate effectively. On the basis of the aforementioned parameter estimation method and the iterative sliding window approach, an innovative and effective CFAR detector is proposed in this study and its superiority is demonstrated by experiments.

This paper deals with the design and the analysis of constant false alarm rate (CFAR) detectors exploiting knowledge-based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a priori information provided by a geographic information system, chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes is analysed in the presence of real radar data, collected by the McMaster IPIX radar, and compared with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a priori information available about the sensed environment.

ABSTRACTIn this paper, the zlog(z) based estimator for constant false alarm rate (CFAR) detection in Weibull clutter is proposed. This estimation method is obtained in terms of the digamma function where the estimates of the shape parameter are determined by the interpolation tool. The non-integer order moments estimator (NIOME) is also given and coincides the zlog(z) estimation results for low values of the moment’s fractional order. Via simulated data, it is shown that the CFAR detection performances based on the zlog(z) estimator have almost similar results as well as the existing maximum likelihood (ML) CFAR detector, but with low time-consuming which is very important in real-time applications.

The constant false alarm rate (CFAR) detection method based on multi‐look polarisation whitening filter (MPWF) has not been obtained in the polarimetric synthetic aperture radar imagery when the texture variable obeys the Inverse Beta distribution. To solve the problem, a new CFAR detection method with analytical expression for false alarm rate is proposed. First, the probability density function (PDF) of the output via MPWF is derived based on the hypothesis of the texture variables obeying the Inverse Beta distribution in the product model. Secondly, the analytical expression of false alarm rate about detection threshold is obtained by integrating the PDF of MPWF output, and the procedure of the CFAR detection method is designed. Finally, the performance comparisons are made among the new method, Wishart CFAR detection algorithm and the two‐parameter CFAR method via simulated and real data of AIRSAR. The results show that figure of merit of the new method is the highest and the new method gives the best performance in Inverse Beta distribution case.