Ordered Statistics Constant False Alarm Rate Detectors Research Articles

Multi-target CFAR Detection of a Digital Phased Array Radar System

As the core of a digital phased array radar system, a radar signal processing environment can be perfected by exploring the constant false alarm rate (CFAR) detection technology. One typical combination is selected to construct the multi-target radar echo signal by considering that its component signals have many types, and the simulated signal processed by moving target detection (MTD) module serves as the input of the CFAR detection module. The mean level (ML) and ordered statistics (OS) CFAR detectors are analyzed theoretically and simulated. Simulation results indicate that, all the true targets are validated while keeping the false alarm rate constant, but some false targets are introduced by using the smallest of (SO) and OS CFAR detectors. Based on the built radar signal generation and processing environments, a graphical user interface (GUI) of MATLAB will be developed to reveal the detected multi-targets on the radar search interface.

Definition of adaptive detection threshold under employment of the generalised detector in radar sensor systems

An adaptive detection threshold under employment of the generalised detector (GD) in radar sensor systems is defined. GD is constructed in accordance with the generalised approach to signal processing in noise. To define the GD adaptive threshold based on the observed noise samples, the authors apply an appropriate noise power estimation technique. This study deals with an adaptive GD detection threshold definition as a function of the estimated noise power. Under investigations, they use two noise power estimation procedures. The first is the sliding window technique with the reference cells. The second procedure is based on the adaptive noise power estimation. Comparative analysis of simulation results demonstrates superiority by detection performance in favour of GD implementation in comparison with the well-known constant false alarm rate (CFAR) detectors, namely, cell averaging CFAR and ordered statistics CFAR detectors.

Threshold optimization of decentralized CFAR detection in weibull clutter using genetic algorithms

The use of genetic algorithms (GAs) tool for the solution of distributed constant false alarm rate (CFAR) detection for Weibull clutter statistics is considered. An approximate expression of the probability of detection (PD) of the ordered statistics CFAR (OS-CFAR) detector in Weibull clutter is derived. Optimal threshold values of distributed maximum likelihood CFAR (ML-CFAR) detectors and distributed OS-CFAR detectors with a known shape parameter of the background statistics are obtained using GA tool. For the distributed ML-CFAR detection, we consider also the case when the shape parameter is unknown of the Weibull distribution. A performance assessment is carried out, and the results are compared and given as a function of the shape parameter and of system parameters.

Detection performance of CFAR detectors based on order statistics for partially correlated chi-square targets

The order statistics cell averaging (OSCA) constant false alarm rate (CFAR) and the order statistics greatest of (OSGO) CFAR detectors have the advantages of the order statistics (OS) CFAR detector, while the processing time of the OSCA and the OSGO CFAR detectors is less than that of the OS CFAR detector. This paper describes the exact formulas of the false alarm and the detection probabilities of CFAR detectors based on OS employing M-pulse noncoherent integration for partially correlated chi-square targets with two and four degrees of freedom.

A New Formula to Predict the Exact Detection Probability of a Generalized Order Statistics CFAR Detector for a Correlated Rayleigh Target

In this paper we present a new formula which can predict the exact detection probability of a generalized order statistics (GOS) constant false alarm rate (CFAR) detector for a partially correlated Rayleigh target model (0 < ρ < 1) in a closed form, where ρ is the correlation coefficient between returned pulses. By simply substituting a set of specific coefficient into the derived formula, one can obtain the detection probability of any kind of CFAR detector. Detectors may include the order statistics CFAR detector, the censored mean level detector, and the trimmed mean CFAR detector, but are not necessarily restricted to them. The numerical result for the first order Markov correlation model as applied to some of the detectors shows that as p increases from zero to one, higher signal-to-noise ratio is required to achieve the same detection probability.

Analysis of the Generalized Order Statistics Constant False Alarm Rate Detector

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.

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.

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.