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This article presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) property. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size. [ABSTRACT FROM AUTHOR]

This paper handles the problem of optimizing the scanning policy of a surveillance system in the presence of Rayleigh targets in a mixture of K-distributed clutter and Gaussian noise. Adopting the detection rate (DR) and false alarm rate (FAR) as performance measures, we show that the said optimization amounts to solving a constrained DR maximization, wherein the free parameters are the transmit beamwidth, the dwell time, and the detection threshold, whereas the constraints concern the maximum FAR and time on target. A thorough performance assessment is offered, aimed at eliciting the influence of the signal-to-noise and clutter-to-noise ratios, the clutter shape parameter, and the rate of change of the target response. The results indicate that, although a uniformly optimum transmit policy does not exist, some trends can be devised, which appears of particular relevance especially for systems that can avail themselves of some degree of cognition. [ABSTRACT FROM AUTHOR]

We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data. [ABSTRACT FROM PUBLISHER]

In the general framework of radar detection, estimation of the Gaussian or non-Gaussian clutter covariance matrix is an important point. This matrix commonly exhibits a particular structure: for instance, this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval. We propose using the particular persymmetric structure of the covariance matrix to improve the detection performance. In this context, this work provides two new adaptive detectors for Gaussian additive noise and non-Gaussian additive noise which is modeled by the spherically invariant random vector (SIRV). Their statistical properties are then derived and compared with simulations. The vast improvement in their detection performance is demonstrated by way of simulations or experimental ground clutter data. This allows for the analysis of the proposed detectors on both real Gaussian and non-Gaussian data. [ABSTRACT FROM AUTHOR]