Decentralized CFAR detection based on order statistics

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).

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.

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. >

Performance of CFAR detectors for M-sweeps in the presence of interfering targets

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.

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.

Constant false alarm rate (CFAR) detection techniques have been of much interest in radar and sonar applications. In this paper we consider CFAR detection in a decentralized, multisensor context and show some interesting characteristics of cell averaging (CA) in this setting. We investigate cases with observations which are dependent from sensor to sensor, for which results have been lacking. The in-phase and quadrature components of the received narrowband observation at each sensor consist of a weak random signal in additive clutter and noise. Each sensor transmits a single binary decision to a fusion center which uses either an AND or an OR fusion rule to develop a final decision. The forms of the best sensor detector rules are found and a set of necessary conditions are given for their thresholds. Solutions are obtained for some representative cases and their detection probability performance is studied. Their ability to maintain constant false alarm probability in the face of clutter edges is also studied. We show that solutions which use different fusion rules may excel for each of these different criteria.

The order statistic (OS) constant false alarm rate (CFAR) detector is designed from a perspective of quantile estimation and uses the sample quantile (SQ) estimator to estimate background level. Following this idea, we find a more efficient quantile estimator, i.e. Kaigh-Lachenbruch quantile (KLQ) estimator, to construct a robust CFAR detector, which is referred to as KLQ-CFAR. We prove that the KLQ estimator is an asymptotic unbiased estimator to the quantile function and derive the asymptotic variance of KLQ estimator in Exponential case. It is shown that the KLQ estimator has more efficient estimation efficiency than SQ estimator. Subsequently, we explore the performance of KLQ-CFAR in Exponential clutter. In homogeneous background, the KLQ-CFAR conquers the shortcoming of large detection loss of OS-CFAR, and exhibits comparable performance with trimmed mean (TM) CFAR. The superiority of KLQ-CFAR is also reflected in multiple-target scenario, especially in the case where the number of interferences exceeds the preset parameters of KLQ- and TM-CFAR. In this case, the detection performance and false alarm control ability of KLQ-CFAR have significant improvement compared with TM-CFAR. Then, we extend the KLQ-CFAR from Exponential background to non-Gaussian clutter via transformation approach, and obtain the transformed KLQ (TKLQ) CFAR. We take Weibull clutter as a specific example. It is found that the behavior of TKLQ-CFAR in Weibull background is very similar to that of the KLQ-CFAR in Exponential clutter, but the difference lies in that the performance of TKLQ-CFAR is related to Weibull shape parameter. The TKLQ-CFAR tends to have better detection performance with the increase of Weibull shape parameter. Finally, the validity of TKLQ-CFAR is verified in skywave over-the-horizon radar data.

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.

The classical rank sum (RS) nonparametric constant false alarm rate (CFAR) detector plays an important role in the theoretical study and practical application of radar target detection. In order to improve the ability of the classical RS nonparametric detector to control the false alarm rate at clutter edges, a modified rank sum (MRS) nonparametric CFAR based on the mean ratio of the samples in the leading and lagging windows is proposed. The analytical expressions of the detection probability and false alarm rate of the MRS nonparametric CFAR in homogeneous background and at clutter edges are derived, and a comparison to the performance of the classical RS nonparametric detector along with some conventional parametric CFAR schemes in homogeneous background, multiple targets situation and clutter edges is made. The numerical results show that the detection performance of the MRS nonparametric CFAR in homogeneous background and in a moderate number of interfering targets situation is close to that of the classical RS nonparametric detector, and its ability to control the rise of the false alarm rate at clutter edges is evidently improved.

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.

This study deals with the performance prediction of constant false alarm rate (CFAR) detector for non-independent and possibly non-independent and non-identically distributed (non-IID) gamma fluctuating targets in the presence of homogeneous background. Accounting for this target model, including the well-known Swerling models as special cases, the authors develop the analytic expressions of detection probability of the cell averaging CFAR (CA-CFAR) and the ordered-statistic CFAR (OS-CFAR) in terms of converging series. At the analysis stage, they study the truncation error and the convergence rate of the derived expressions. Finally, they assess the impacts on detection performance of the target parameters and compare the detection performance between CA-CFAR and OS-CFAR for the non-IID gamma fluctuating targets via numerical simulations.