In this paper, we consider the detection problem with the polarization diversity technique in the compound Gaussian sea clutter with Gamma texture. We derive the polarimetric detector based on the maximum a posterior (MAP) and two-step generalized likelihood ratio test (GLRT). In the first step, we assume that the polarimetric clutter covariance matrix (PCCM) and the Gamma textures are known, and we develop the test statistics of the proposed detector. In the second step, we obtain the adaptive polarimetric detector by using the fixed point covariance estimator (FPCE) to estimate the PCCM and the MAP method to estimate the Gamma textures. Then we prove the constant false alarm rate (CFAR) property of the proposed adaptive polarimetric detector. Finally, we take advantage of the simulated data and the real sea clutter data to evaluate the performance of the proposed polarimetric detector.

This paper addresses the adaptive persymmetric detection of range-spread targets problem in compound Gaussian sea clut-ter with Gamma texture. A new compound Gaussian detector based on two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is developed. Specifically, the first step is to acquire the test statistic of GLRT on the as-sumption that the texture components and the clutter covari-ance matrix (CCM) are known. The second step is to employ the MAP approach to acquire the estimates of texture components and persymmetric method to obtain the estimate of CCM. Remarkably, the novel detector ensures the constant false alarm rate (CFAR) property with respect to the covari-ance matrix structure. The effectiveness of the proposed de-tector is verified by using simulated and real sea clutter data.

This paper considers the adaptive detection problem of range-spread targets in Gaussian environment. Model the Gaussian disturbance with unknown covariance matrix as an auto-regressive (AR) process with known AR order but unknown parameters. In order to solve this problem, we propose a novel generalised likelihood ratio test (GLRT)-based detector, which estimates the unknown parameters with secondary data support by maximum likelihood estimate (MLE). The simulation experiments are conducted by comparing the proposed detector with the traditional detectors on both synthetic data and real sea clutter data. And simulation results demonstrate the effectiveness of the proposed detector.

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximuma posteriori(MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.

This paper investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximuma posteriori(MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then the proposed detectors are proved to have the constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

Mobile 3D video communication, especially with 2D and 3D compatible, is a new paradigm for both video communication and 3D video processing. Current techniques face challenges in mobile devices when bundled constraints such as computation resource and compatibility should be considered. In this work, we present a wireless 2D and 3D switchable video communication to handle the previous challenges, and name it as Stereotime. The methods of Zig-Zag fast object segmentation, depth cues detection and merging, and texture-adaptive view generation are used for 3D scene reconstruction. We show the functionalities and compatibilities on 3D mobile devices in WiFi network environment.