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High-resolution maritime surveillance radars need to detect small targets and moderate/large targets such as ships in short/medium-distance regions. At a single-range resolution, radar echoes of moderate/large targets severely affect the detection of small targets around them. In this article, a simple digital filtering method is given to realize range resolution conversion by which multiresolution radar echoes data are generated from high-range resolution data. As one of the main contributions, the compound-Gaussian model with inverse Gaussian textures (CGIG) is extended to characterize multiresolution sea clutter. Moreover, an across-resolution parameter estimation method is proposed to estimate the parameters of the multiresolution CGIG model based on across-resolution moment relationship. The other contribution is a hierarchical target detection scheme in the multiresolution CGIG clutter model. Simple nonadaptive noncoherent integration detectors are used at lower resolution channels to detect moderate/large targets and adaptive near-optimum coherent detectors in the CGIG-distributed sea clutter are adopted at higher resolution channels to detect small targets using the detections from the lower resolution channels as the prior information on environment. Finally, the multiresolution CGIG model and hierarchical target detection scheme are examined by an X-band island-based measured data using an unmanned aerial vehicle as a test target.

Sea-surface small target detection in high-resolution sea clutter is always an intractable problem. Feature-based detection in multidimensional feature spaces is recognized to be an effective way, and therein, learning algorithms with controllable false alarm rate play an important role. In this article, a weighted biased soft-margin support vector machine (WBSM-SVM) algorithm is proposed to design two-class classifiers with controllable false alarm rate in feature spaces and the induced feature-based detectors can accurately control false alarm rate and have excellent detection ability of small targets in sea clutter. The WBSM-SVM algorithm contains three innovations. First, special two-class SVM classifiers are used to lower the loss from the one-class $\nu $ -SVM classifiers by additional use of the training samples of the feature vector from simulated returns of typical targets plus measured sea clutter. Second, extremely unbalanced misclassification weight of penalty factors of the two classes and the Mahalanobis distance of the training sample vectors are introduced in the SVM to meet the demand of extremely unbalanced false alarm rate versus target missing probability. Third, a biased classification boundary is used to tune the false alarm rate to the expected one. The experimental results on the two recognized databases for sea-surface small target detection show that the WBSM-SVM-based detectors attain better detection performance than existing feature-based detectors.

Mechanically scanning radars are still widely used in ocean guard and surveillance areas nowadays, owing to simple structure, light weight and low cost. The antenna rotation causes amplitude modulation of target returns along pulses by the antenna azimuth beampattern. The traditional coherent detection that ignores this modulation suffers from some performance loss. In this article, an antenna beampattern matched optimum coherent detector and a dual-threshold detection scheme are proposed for high-resolution mechanically scanning maritime surveillance radars. First, it is shown that the modulation of target returns is determined by the antenna azimuth beampattern, scan rate, and pulse repetition interval and the antenna scanning also affect the temporal correlation of sea clutter. Second, the antenna beampattern matched optimum coherent detector is given in compound-Gaussian sea clutter with inverse Gamma distributed texture and the pulse scanning mode is used to deal with unknown target azimuth angle in the matched filter. Third, to mitigate the high computational cost of the pulse scanning mode, a dual-threshold scheme is given, which searches all pulse-range cells by short-time integration and further confirms suspected cells whose test statistics are between the two lower and higher thresholds in the search by long-time integration. Moreover, two fast recursive clutter covariance matrix inversion algorithms along pulses and range cells are presented, which bring a great reduction in computational complexity. Finally, real measured radar data is used to verify the proposed detector and scheme and the experimental results show that it behaves better in detection of small targets than the traditional detectors.