Order Statistics CFAR Research Articles

Analysis and comparison of two order statistics CFAR systems

Constant false alarm rate (CFAR) processors based on order statistics (OS) are considered. The classical OSCFAR and its greatest-of (OSGO) version are compared in the presence of a Weibull distributed (and Rayleigh distributed as a particular case) background, for a design false alarm probability equal to 10-6. The effects of interfering targets and background discontinuities are evaluated by Monte Carlo and importance sampling computer simulations. The advantages of the OSGO processor in these significant conditions are shown.

Performance of some generalised modified order statistics CFAR detectors with automatic censoring technique in multiple target situations

The author presents three generalised modified OS CFAR detectors and an automatic censoring technique. These three new OS CFARs are: a generalised ordered statistic cell averaging (GOSCA) CFAR, a generalised order statistic greatest of (GOSGO) CFAR and a generalised ordered statistic smallest of (GOSSO) CFAR. For these three new OS CFAR detectors the author obtains analytic expressions of the false alarm rate, the detection probabilities and the measure ADT under the Swerling 2 assumption. He analyses their detection performance in homogeneous background and in the presence of strong interfering targets, and compares them to several previously proposed CFARs. This analysis shows that the GOSCA CFAR detector has the best detection performance among the OS and the several modified OS CFAR detectors. Since a new automatic censoring technique is used in the CFAR detectors, this makes the GOSGO and GOSSO CFARs more robust than the OSGO and OSSO CFARs. When the number of strong interfering targets attains the greatest interfering targets allowed, the performance of the OS CFAR is worst among the GOSCA, GOSGO, GOSSO and OS CFAR detectors.

Generalized OS CFAR detector with noncoherent integration

Analysis of constant false alarm rate (CFAR) detectors with noncoherent integration has been limited to the cell-averaging (CA) CFAR detector, the maximum mean level detector (MX-MLD) and the order statistics (OS) CFAR detector. Detection performance of the CA CFAR detector that employs noncoherent integration has been studied by several authors even though the false alarm rate of the CA CFAR detector is sensitive to changes in the background clutter-plus-noise level under nonhomogeneous situations. Shor and Levanon analyzed the detection performance of the OS CFAR detector with noncoherent integration in homogeneous situation, but their formula requires numerical integration. In this paper, we extend the detection analysis to the generalized order statistics (GOS) CFAR detector that employs M-pulse noncoherent integration for general chi-square fluctuating targets in nonhomogeneous environments, which covers various OS and CA CFAR detectors. We obtain unified formulas of the false alarm and the detection probabilities for the GOS CFAR detector in closed form. By properly choosing the coefficients of the GOS CFAR detector, one can realize various kinds of CFAR processors, such as the CA CFAR detector, the OS CFAR detector, the censored mean level detector (CMLD) and the trimmed mean (TM) CFAR detector.

Performance of modified order statistics CFAR detectors with noncoherent integration

The order statistics cell averaging (OSCA) constant false alarm rate (CFAR) detector and the order statistics greatest of (OSGO) CFAR detector have the good properties of the order statistics (OS) CFAR detector while the processing time of each detector is less than half the processing time of the OS CFAR detector in ordering magnitude of the cells in a reference window. In this paper, we analyze the OSCA and the OSGO CFAR detectors with M-pulse noncoherent integration for chi-square fluctuating targets. We obtain the explicit formulas of false alarm rates and detection probabilities for the OSCA and the OSGO CFAR detectors.

Moving ordered statistics CFAR detection for nonhomogeneous backgrounds

A new robust order statistic-based CFAR scheme, the moving ordered statistics (MOS) CFAR detector, is proposed. The MOSCFAR detector effectively estimates the noise level in nonhomogeneous backgrounds by using the spatial subspace concept and the order statistics estimation method. Like the OS-CFAR detector, the MOS-CFAR processor selects one of the reference cells as the estimate of the background noise power. However, the rank of the selected sample is a variable and varies depending on the different background noise distributions in the reference cells. The detection performances of the MOSCFAR processor is investigated and compared with other well known CFAR detectors by means of simulations. The results show that the proposed MOS-CFAR detector is superior to the other schemes in regulating the false alarm rate in nonhomogeneous backgrounds.

Performance off order-statistics CFAR detector with noncoherent integration in homogeneous situations

The order-statistics (OS) constant-false-alarm-rate (CFAR) detector has been known to have a small loss in detection performance relative to the cell-averaging (CA) CFAR detector in homogeneous situations. However, it results in much less performance degradation in nonhomogeneous situations, i.e. the OS CFAR detector has significant performance gain over the CA CFAR detector in nonhomogeneous environments. In general, as the frequency diversity between noncoherent pulse bursts is prevalent in radar detection systems, detection analysis of the OS CFAR detector with noncoherent integration is needed. In the paper, for the chi-square family of fluctuating targets with an integer fluctuation parameter, the detection performance of the OS CFAR detector with noncoherent integration in homogeneous situations, is discussed.

Performances of order statistics CFAR

A previous analysis of order-statistics constant-false-alarm-rate (OS-CFAR) radar receiving a single pulse from a Rayleigh fluctuating target in a Rayleigh background is extended to a Rayleigh-plus-dominant target. The analysis includes effects of a multitarget environment. A detailed comparison of OS-CFAR, cell-averaging (CA) CFAR, and censored CA-CFAR is provided for a Rayleigh target in the presence of strongly interfering targets. The false-alarm analysis of OS-CFAR is extended to the more general case of a Weibull background. The deterioration of the CFAR property of OS as the shape factor, C, of a Weibull probability density function changes from Rayleigh (C=2) to a longer-tailed one (C >

CFAR detection of fluctuating targets in spatially correlated K-distributed clutter

The problem of single pulse CFAR detection of Rayleigh fluctuating targets in K-distributed sea clutter is addressed. The performance of cell averaging (CA), cell averaging greatest of (CAGO), and order statistic (OS) CFAR processors, is examined under varying conditions of clutter spatial correlation for several cases of desired false alarm probability and the number of reference cells used in the CFAR processor. Curves for the detectability loss due to the 'spikiness' of the clutter are presented and values for the additional loss due to CFAR thresholding are tabulated. The effects of incorrectly estimating the clutter shape parameter v are investigated. A model is presented to facilitate numerical analysis of detection performance under conditions of partial correlation of the clutter modulation process. It is shown that for the CFAR processors analysed here, detection performance will not in general be dramatically improved relative to the worse case condition of spatially uncorrelated clutter.< >

Performance of MAX family of order-statistic CFAR detectors

The MAX family of constant-false-alarm-rate (CFAR) detectors is introduced as a generalization of the greatest of CFAR (GO-CFAR) or MX mean-level detector (MX-MLD). Members of the MAX family use local estimators based on order statistics and generate both a near-range and a far-range noise-level estimate. Local estimates are always combined through a maximum operation; this insures false-alarm control at clutter edges. At the same time, order-statistic-based estimators result in a high-resolution detector. A complete detection analysis is provided for SWII targets and a reference channel contaminated by large outliers. Results are presented for the MX censored MLD (MX-CMLD) operating in clutter. The MX order statistic detector (MX-OSD) based on only a single-order statistic per window, is analyzed, and curves showing the required threshold, CFAR loss, optimum censoring point, and signal-to-noise ratio (SNR) loss in the presence of outliers are given. Simulations are used to compare the dynamic responses of various MX-OSD detectors in a clutter and a multiple-target environment. >

Analysis of some modified ordered statistic CFAR: OSGO and OSSO CFAR

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. >

Order statistcs CFAR for Weibull background

The paper shows how to adopt Rohling&apos;s order statistics CFAR algorithm, developed for a Rayleigh background, to the case of a Weibull background with a known shape parameter. When the shape parameter is unknown, Weber and Haykin have proposed a two-parameter algorithm in which the threshold is obtained from two ranked background samples. In the paper the CFAR loss of the Weber-Haykin CFAR algorithm is analysed and compared with another CFAR processor for Weibull clutter suggested by Hansen. It is found that, with the optimal choice of representative ranks, the Weber-Haykin CFAR processor yields a smaller, but still very excessive, detection loss. Finally it is shown that CFAR is maintained when more than two ranked samples are used in setting the threshold, but without improvement in performance.

Improvement of the defect detection capability of ultrasonic inspection of coarse-grained austentic materials by means of a combination of signal enhancement methods. Final report (In German): Kapitz, H. Frauhnefer-Inst. fuer Zerstoerungsfreie Pruefverfahren, Saarbruecken (Germany). TIB/B88-81362/GAR, 73 pp. (27 Sep. 1986)

In radar systems, detection performance is always related to target models and background environments. In “time diversity systems”, the assumed Swerling complete correlation (slow fluctuation) and complete decorrelation (fast fluctuation) target models may not predict the actual system performance when the target returns are partially correlated. The probability of detection is shown to be sensitive to the degree of correlation among the received pulses. In this paper, we derive exact expressions for the probability of false alarm (Pfa) and the probability of detection (Pd) of a pulse-to-pulse partially correlated target with 2K degrees of freedom in a pulse-to-pulse completely decorrelated thermal noise for the order statistics constant false alarm rate (OS-CFAR) detector and the censored mean level CFAR (CMLD-CFAR). The complete analysis is carried out for the “non-conventional time diversity system” (NCTDS) and multiple target situations. The obtained results are compared with the detection performance of the “conventional time diversity system” (CTDS).

Analysis of CFAR processors in nonhomogeneous background

Five different constant false alarm rate (CFAR) radar processing schemes are considered and their performances analyzed in homogeneous and nonhomogeneous backgrounds, the latter specifically being the multiple target environment and regions of clutter transitions. The average detection threshold for each of the CFAR schemes was computed to measure and compare the detection performance in homogeneous noise background. The exponential noise model was used for clear and clutter backgrounds to get closed-form expressions. The processor types compared are: the cell-averaging CFAR, the 'greatest of' CFAR, the 'smallest of' CFAR, the ordered-statistics CFAR, and a modified ordered-statistics processor called the trimmed-mean CFAR.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Detection loss due to interfering targets in ordered statistics CFAR

The ordered-statistics (OS) constant false-alarm rate (CFAR) is relatively immune to the presence of interfering targets among the reference cells used to determine the average background. OS CFAR performance in a multitarget environment was previously studied by simulation. The author obtains analytic expressions for the added detection loss, assuming strong interfering targets. The real target is assumed to be a Rayleigh fluctuating target. Numerical examples are included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Performance of an ordered statistic CFAR processor in log-normal clutter

Clutter returns for high resolution radars can be modelled by the long tail log-normal distribution. The performance of a constant false-alarm rate (CFAR) processor based on ordered statistics of the reference cells is analysed for a log-normal clutter distribution. Numerical results show that the excessive loss can be decreased by increasing the number of reference cells. >