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Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects. [ABSTRACT FROM AUTHOR]

This article studies the problem of radar detection of correlated gamma-fluctuating targets in the presence of clutter described by compound models with correlated speckle. It models signals from a pulsed radar with pulse-to-pulse correlation of components of the range-gated returns from a target with added thermal noise and compound Gaussian clutter. If the correlation is not accounted for in a radar model, the required signal-to-interference ratio as predicted by the model for a given probability of detection will be incorrect, resulting in overestimated performance. Although more generally applicable, the focus of this article is on airborne maritime radar systems. Hence, $K$ -distributed sea clutter is used as the main example. Detection via square-law noncoherent pulse integration is formulated in a way that accommodates arbitrary partial correlation for both target radar cross section (RCS) and clutter speckle. The obstacle to including this degree of generality in previous work was the fact that Swerling’s original characterization of the standard RCS fluctuation classes based on specifying distributions for the power is not sufficient to accommodate inclusion of both sources of correlated returns (i.e., target and clutter speckle) in the case of gamma-fluctuating targets. An extension of the model is required at the quadrature component (i.e., voltage) level, as phase relationships can no longer be neglected. This is addressed in the present article, which not only postulates an extended model, but also demonstrates how to efficiently compute it, with and without a number of simplifying approximation schemes within the framework of the saddle-point technique. [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]

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]