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 detection of small targets around them. In this paper, 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 non-adaptive 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 (UAV) as test target.

Compound-Gaussian models (CGMs) are widely used to characterize sea clutter. Various types of texture distributions have been developed so that the CGMs can cover sea clutter in different conditions. In this paper, the Weibull distributions are used to model textures of sea clutter, and the CGM with Weibull-distributed textures is used to derive the CGWB distributions, a new type of biparametric distribution. Like the classic K-distributions and Compound-Gaussian with lognormal texture (CGLN) distributions, the biparametric CGWB distributions without analytical expressions can be represented by the closed-form improper integral. Further, the properties of the CGWB distributions are investigated, and four moment-based estimators using sample moments, fractional-order sample moments, and generalized sample moments are given to estimate the parameters of the CGWB distributions. Their performance is compared by simulated clutter data. Moreover, measured sea clutter data are used to examine the suitability of the CGWB distributions. The results show that the CGWB distributions can provide the best goodness-of-the-fit for low-resolution sea clutter data as alternatives to the classic K-distributions.

This paper investigates adaptive coherent detectors in compound Gaussian clutter with Nakagami-distributed textures. The maximum a posterior ratio test (MAPRT) detector is firstly derived. Using the two-step method, the MAP-Rao/Wald detectors are derived. The three detectors have analytical expressions owing to the root formula of depressed cubic equation. The three detectors are proved to be constant false alarm rate (CFAR) with respect to the scale parameter of the texture, speckle covariance matrix, and Doppler steering vector. Their adaptive versions are shown by simulation to be approximate CFAR with estimated speckle covariance matrix by the iterative maximum likelihood (IML) estimator. The performances of the detectors are compared by using simulated and measured data. The results show that the adaptive MAP-Rao and MAPRT detectors obtains better whole performance than MAP-Wald detector and classic adaptive matched filter (AMF) and adaptive normalized matched filter (ANMF) detectors.