Greatest Of Constant False Alarm Rate Research Articles

Application of CFAR detection to multiple pulses for gamma distributed clutter

ABSTRACT This paper deals with radar constant false alarm rate (CFAR) detection for multiple pulses for gamma-distributed clutter. In this work, some CFAR detectors are extended to cover the case of multiple pulses. Moreover, using the proprieties of the gamma distribution, the expressions of the false alarm probability are also generalized to multiple pulses transmission. The obtained results show the detection performance of multiple pulses generalized detectors – cell averaging-CFAR, greatest of-CFAR and order statistic-CFAR – compared with their counterpart in single-pulse detectors.

CFAR Strategy Formulation and Evaluation Based on Fox’s H-function in Positive Alpha-Stable Sea Clutter

The problem of target detection in impulsive non-Gaussian sea clutter has attracted a lot of attention in recent years. The positive alpha-stable (PαS) distribution has been validated as a suitable model for the impulsive non-Gaussian sea clutter. Since the probability density function (PDF) of the PαS variable cannot be expressed as a closed-form expression, the research into constant false alarm rate (CFAR) detectors in PαS distributed sea clutter is limited. This paper formulates and evaluates some CFAR detectors, such as Greatest Of-CFAR (GO-CFAR), Smallest Of-CFAR (SO-CFAR), Order Statistic-CFAR (OS-CFAR) and censored mean level (CML) detectors, in PαS distributed sea clutter. Firstly, the Fox’s H-function is adopted to express the PDF of the PαS variable, and the cumulative density function based on Fox’s H-function is derived in this paper. Then, by use of the properties of the H-function and PαS distribution, exact expressions of the probabilities of false alarm and detection for CFAR detectors in the PαS background are derived. Some CFAR properties of these detectors in the PαS background are also explored. Numerical results based on derived expressions are given and verified by Monte Carlo simulation. Some analyses of detection performance from a practical perspective are also given.

Performance Evaluation of a Real-Time Seismic Detection System Based on CFAR Detectors

In this research work, we present a novel application specific Ordered cell averaging-CFAR (OCA-CFAR) detector for anthropogenic seismic events. We investigate the use of geophones and real-time hardware for the detection of moving targets such as vehicle, human footsteps and activities such as hammering on the ground. We present here energy assisted variants of Constant False Alarm Rate (CFAR) detector implemented on a real-time embedded platform to obtain detection cues of the ground seismic source. The OCA-CFAR algorithm has been modified in terms of window length and position of Cell Under Test (CUT), suitably adapted to the nature of seismic events engendered from moving vehicles and humans. The proposed detector has been compared with the popularly known variants such as Cell Averaging-CFAR, Least Of-CFAR, Greatest Of-CFAR and Ordered Statistics-CFAR for probabilities of false alarm ranging from 1e-1 to 1e-5. The proposed method shows an increased detection rate with minimized false alarms on the field trials and a dataset containing the seismic signature of human activities and some of civilian vehicles.

Pietra index based processor for a heterogeneous Pareto background

This study addresses the problem of automatic target detection in a heterogeneous Pareto background. To achieve this, the Pietra index based and constant false alarm rate processor (CFAR) is conceived. Specifically, assuming a non-stationary Pareto background with the presence or not of any clutter edge or interfering targets, the Pietra index and the log geometric mean ratio statistic tests are concomitantly used to allow the proposed processor to switch dynamically to the appropriate detector; i.e. the geometric mean-CFAR, the greatest of-CFAR or the trimmed mean-CFAR, where all of these detectors assume a priori unknown scale parameter. That is, according to the outcomes of the Window Selection Probability, the background level is systematically estimated through the preselected detector. The detection performances of the proposed processor are assessed, via Monte Carlo simulations, in multiple target and clutter edge situations.

Optimization of Distributed CFAR Detection using Grey Wolf Algorithm

In this paper, decentralized constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Grey Wolf Optimization technique is investigated. For independent signals with known power, optimal thresholds of local Greatest Of-CFAR and Smallest Of-CFAR detectors are optimized simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, both the Biogeography Based Optimization and the Grey Wolf Optimization tools are used to conduct distributed CFAR detection comparisons. In terms of achieving fixed probabilities of false alarm and higher probabilities of detection, simulation results show that the new GWO scheme performs better than the BBO method described in the literature in most cases.

Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background

In this study, the authors analyse the performance of some constant false alarm rate (CFAR) detectors namely: the cell averaging-CFAR (CA-CFAR), greatest of CFAR (GO-CFAR), the smallest of CFAR (SO-CFAR), the smallest of OS and CA-CFAR (SOSCA-CFAR) and the OS and CA-CFAR greatest of (OSCAGO-CFAR) detectors in homogeneous and non-homogeneous Weibull background with binary integration under the assumption of a known shape parameter. The non-homogeneity is modelled by the presence of interfering targets and the presence of a clutter edge in the reference window. The authors derive close-form expressions of the probability of false alarm in the case of a homogeneous clutter environment. For a non-homogeneous environment, the performance of these detectors is investigated by means of Monte Carlo simulations. The obtained results showed that the best false alarm rate performance at clutter boundary is obtained for the OSCAGO and GO-CFAR detectors, whereas the SOSCA and SO-CFAR detectors present better performance for the interfering targets situation.

Antenna array characterisation and signal processing for an FM radio-based passive coherent location radar system

The design of passive coherent location radar, which exploits broadcasting transmitters of opportunity in the very high frequency (VHF) radio bandwidth, is presented. Here, the authors primarily focus on the system set-up and on the digital pre-processing steps. Emphasis is given to the antenna section analysis. The eight-element circular array, which is used for the signal acquisition, is analysed by means of simulations and measurements. Compensation for the mutual coupling effect between the different channels is achieved by applying a technique in the digital domain. An innovative digital beamforming algorithm is introduced to reduce the sidelobe level of the circular array pattern. Direct path interference suppression, range/Doppler data processing, greatest-of constant false alarm rate algorithm and range/Doppler-time plots extraction provide the final output of the processing chain. Experimental validation of the processing architecture is presented and the final detection results are compared with an automatic dependent surveillance broadcast data set.

Improved switching CFAR detector for non-homogeneous environments

In order to mitigate the excessive false alarm rate of the switching constant false alarm rate (S-CFAR) detector at the clutter edge, an improved switching CFAR (IS-CFAR) detector is proposed in this paper. In IS-CFAR, a comparison threshold is generated by multiplying the amplitude of the test cell by a scaling factor. Then in the leading and lagging reference windows, the numbers of reference cells whose amplitudes are smaller than the comparison threshold are countered and compared with a threshold integer. Based on the comparison result, the detection threshold is generated by selecting appropriate reference cells for background noise/clutter power estimation. The detection probability of IS-CFAR in various environments is derived in a closed-form expression. The performance is evaluated and compared with other CFAR detectors. It is shown that IS-CFAR exhibits a low CFAR loss in a homogeneous environment and almost the same detection performance as S-CFAR in a multiple targets situation; at a clutter edge, the false alarm rate of IS-CFAR is much lower than that of S-CFAR and approximates to the greatest-of CFAR (GO-CFAR). Experimental results from a linear frequency modulated continuous wave (LFMCW) radar system are given to demonstrate the efficiency of IS-CFAR.

Performance analysis of ordered-statistic greatest of-constant false alarm rate with binary integration for M-sweeps

The performance of the ordered-statistic greatest of (OSGO) constant false alarm rate (CFAR) scheme with binary integration for M non-coherent sweeps in Weibull background was investigated for homogeneous and non-homogeneous backgrounds, with an assumption of known shape parameter. This kind of processing is based on the fact that the clutter can be segmented in regions in many real radar scenarios where a time-varying two-parameter distribution function family can be fitted, but the clutter power may vary locally inside the region. Under the assumption of known shape parameter, the authors examined the changes of the false alarm rate and detection probability of the OSGO-CFAR with binary integration when the shape parameter differs from the nominal one, and compared them to those of the OSGO-CFAR with single pulse processing. The authors have derived analytic expressions of the detection probability and false alarm rate during clutter power transitions for the OSGO-CFAR with binary integration in Weibull background. It is shown that the OSGO-CFAR with binary integration can not only improve the detection performance significantly, but it also control the rise of the false alarm rate at clutter edges more effectively compared to the OSGO-CFAR with single pulse processing. Moreover, it exhibits a good immunity to the variation of the shape parameter.

Noncoherent Constant False-Alarm Rate Schemes With Receive Diversity for Code Acquisition Under Homogeneous and Nonhomogeneous Fading Circumstances

In this paper, we address the cell averaging (CA), greatest of (GO), and smallest of (SO) constant false-alarm rate (CFAR) processors for code acquisition in a homogeneous and a nonhomogeneous environment. The performance characteristics of the CA, GO, and SO processors are analyzed and compared when receiving antenna diversity is employed in the pseudonoise code acquisition of direct-sequence code-division multiple-access systems. From the simulation results, it is observed that the GO CFAR scheme has the best performance in a nonhomogeneous environment and has almost the same performance as the CA CFAR scheme in a homogeneous environment.

Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter

In this paper, we analyse the performance of the Greatest Of-Constant False Alarm Rate (GO-CFAR) and the Smallest Of (SO)-CFAR detectors in the presence of clutter environments whose amplitude statistics are modelled by the Pearson V distribution and where the clutter dominates the receiver noise. The performance of these detectors are evaluated both in homogeneous and non-homogeneous clutter. The non-homogeneity is modelled as a step function discontinuity in the reference window. The target in the test cell is assumed to fluctuate according to the Swerling I model. We derive closed form expressions for the probability of false alarm (Pfa) and the probability of detection (Pd) in homogeneous and non-homogeneous Pearson V distributed clutter. The comparison of the two detectors for a non-homogeneous clutter environment showed that the best false alarm rate performance at clutter boundary is obtained for the SO-CFAR detector when the test cell is in low level clutter, while the GO-CFAR detector exhibits better performance when the test cell is from the higher level clutter as expected.

Intelligent CFAR processor based on data variability

An intelligent constant false alarm rate (CFAR) processor to perform adaptive threshold target detection is presented. It employs a composite approach based on the well-known cell averaging CFAR (CA-CFAR), smallest of CFAR (SO-CFAR), and greatest of CFAR (GO-CFAR) processors. Data in the reference window is used to compute a second-order statistic called the variability index (VI) and the ratio of the means of the leading and lagging windows. Based on these statistics, the VI-CFAR dynamically tailors the background estimation algorithm. The VI-CFAR processor provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments including multiple targets and extended clutter edges.

Adaptive censored greatest-of CFAR detection

A new CFAR detection algorithm in which two tentative estimates of the noise level in the test cell are obtained by independently processing the outputs of the leading and the lagging cells is proposed and studied. The two tentative estimates are obtained by employing a cell-by-cell criterion for accepting or rejecting reference samples. The final estimate of the noise level in the cell under test is set to be the maximum of the two tentative estimates. The proposed detection scheme is referred to as the 'adaptive censored greatest-of' (ACGO) CFAR detector. The false alarm regulation properties and the detection performance of the ACGO-CFAR processor are evaluated and compared with those of the 'greatest-of' (GO) CFAR and the 'trimmed-mean' (TM) CFAR detectors for both homogeneous and nonhomogeneous background environments. >

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. >