Covariance matrix estimation method based on inverse Gaussian texture distribution

To detect the target signal in composite Gaussian clutter, the clutter covariance matrix needs to be estimated. The corresponding detection performance is closely related to the estimation accuracy. Using the texture component obeying the inverse Gaussian distribution to model the composite Gaussian clutter can better fit the measured data of high-resolution clutter. In this paper, a two-step generalized likelihood ratio detector is proposed. Firstly, the clutter covariance matrix is assumed to be known to obtain the detection statistics, and then the maximum likelihood estimation of the covariance matrix is derived from the prior distribution of texture components. At the same time, based on Bayesian method, assuming that the texture component and covariance matrix are random quantities subject to a priori distribution, the maximum a posteriori estimation of covariance matrix is derived. Simulation results show that due to the introduction of prior information of texture components and clutter, the estimation accuracy of covariance matrix of knowledge-based adaptive detection technology is better than that of maximum likelihood estimation and sample estimation methods, and has better detection performance. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.