This letter deals with the knowledge-aided adaptive detection problem of radar targets in non-homogenous correlated compound Gaussian sea clutter. The lognormal distribution is used as the prior distribution of the clutter texture to match the non-Gaussianity of sea clutter. In addition, in order to ensure the estimation accuracy of the covariance matrix structure of sea clutter, a convex combination estimator (CCE) is proposed by jointly exploiting the prior information and the current secondary data. Then, a knowledge-aided adaptive detector is designed on the basis of the complex parameter Gradient test and the CCE. Numerical experiments verify the effectiveness of the proposed CCE and adaptive detector in comparison with their counterparts, respectively.

This paper deals with the detection problem of a moving point-like target in correlated non-Gaussian sea clutter, which is modelled by a compound Gaussian model with a lognormal-distributed texture and an unknown covariance matrix. In order to improve the detection performance for radar targets in sample-starved environments where the number of secondary data is limited, the persymmetric structure is exploited to transform the original radar data. Based on the two-step generalized likelihood ratio test (GLRT) and its maximum a posterior version, we propose two adaptive persymmetric coherent detectors for radar target detection. Theoretical and experimental confirmations are provided to show that the proposed detectors guarantee the constant false alarm rate property with respect to the clutter covariance matrix structure and the clutter power mean. Experimental results on simulated and measured radar data demonstrate that two proposed detectors perform better than traditional ones, especially when the number of secondary data is small.

In this paper, we consider the problem of robustly and accurately estimating the shape parameters ofK-distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy. To improve the estimation performance, we first propose a bipercentiles feedforward neural network for the shape parameter$\eta$(BP-FFNN-$\eta$), which utilizes a ratio of two percentiles and a two-hidden-layer feedforward neural network. The BP-FFNN-$\eta$can learn the mathematical relationship between the shape parameter and the ratio of two percentiles, and can work in environments where the number of outliers is approximately known. Moreover, to solve the case where the number of outliers is not known due to dynamic changes in the environment, we also design another neural network (referred to as MBP-FFNN-$\eta$), which consists of multiple BP-FFNNs-$\eta$and a multi-class classification network. The MBP-FFNN-$\eta$can perceive the change in the proportion of outliers, so an accurate estimate can be obtained from an unaffected BP-FFNN-$\eta$. Finally, training and test data are constructed to train and evaluate the proposed methods, respectively. Experimental results demonstrate that the BP-FFNN-$\eta$performs better than traditional moments-based estimators, and has almost the same performance as the tri-percentile estimator. Compared with the tri-percentile estimator, the BP-FFNN-$\eta$avoids table lookups, and produces a continuous estimate. The MBP-FFNN-$\eta$can achieve more than 97% overall classification accuracy on simulated and measured data, and thus an accurate estimate of the shape parameter can be obtained when the number of outliers varies.

We consider the detection problem of maritime radar targets in the training-sample-starved and non-Gaussian sea clutter environment. The performance of conventional detectors for radar targets is seriously degraded due to both the starvation of training samples for estimating the clutter covariance matrix and the non-Gaussianity of sea clutter. In this letter, we adopt the inverse Gaussian distribution and the inverse complex Wishart distribution to model the texture and speckle covariance matrix of sea clutter, respectively. Then an adaptive Bayesian detector is developed based on the two-step generalized likelihood ratio test and the maximum posterior estimates of clutter parameters. Finally, the experimental results on simulated and measured data demonstrate the performance superiority of the proposed detector over its competitors, especially when the training samples are starved.

This article deals with the problem of detecting a marine target with coherent radars in a correlated heavy-tailed sea clutter background. The heavy-tailed sea clutter is modeled by a compound-Gaussian model, and the clutter texture is characterized by the lognormal distribution with a new parameterization form. We develop an adaptive coherent detector on the basis of the two-step generalized likelihood ratio test. The proposed detector can achieve adaptation to sea clutter characteristics by using the maximum a posterior estimate of the clutter texture, the constrained approximate maximum likelihood estimator of the speckle covariance matrix, and the proposed negative- and positive-fractional moment estimate of amplitude parameters of sea clutter. Remarkably, the proposed detector inherently ensures a constant false alarm rate with respect to the clutter power mean and the speckle covariance matrix. Finally, numerical experiments using simulated data and real radar data demonstrate that the proposed estimator and adaptive coherent detector outperform their respective competitors.

Non-Gaussian sea clutter causes conventional detectors designed in Gaussian clutter to suffer detection performance degradation, and some nuisance parameters cause the uniformly most powerful test to be unavailable. To improve the detection performance of maritime radar targets, we investigate the design of adaptive detectors in correlated non-Gaussian sea clutter via using suboptimal tests. The non-Gaussian sea clutter is modelled as a product of lognormal-distributed texture and complex Gaussian speckle. Two adaptive radar target detectors are developed by using the suboptimal two-step Wald and Rao tests. Specifically, a non-adaptive detector is derived by the Wald or Rao test when the clutter texture and speckle covariance matrix are assumed to be known in the first step; then the clutter parameters known in the first step are estimated, and the true parameters of the detector obtained in the first step are replaced with the estimated values. Theoretical proof and experimental verification indicate that the two proposed detectors have the constant false alarm property with regard to the clutter speckle covariance matrix and the clutter average power. Numerical results on simulated and measured radar data show that the proposed Rao-based detector outperforms its competitors, and has the stronger robustness to the signal mismatch compared to the proposed Wald-based detector.