Knowledge-aided Detection of DistributedTargets without Secondary Data

With prior information of clutter covariance matrix of cells under test, knowledge aided detection methods for distributed targets without secondary data are researched based on Bayesian approach. First, for the heterogeneous clutter environment that the clutter covariance matrix of each cell is not the same with probability one, the Generalized Likelihood Ratio Test (GLRT) and Maximum-A-Posterior (MAP) GLRT are proposed. Then, for the homogeneous clutter environment that the clutter covariance matrix of each cell is the same, the one step GLRT and two-step GLRT are proposed. Furthermore, the detection performance under the prior model mismatched condition is analyzed using computer simulation. The results show that, when the parameter u of prior information model is small, the detection performance of detectors is related to matching degree of prior information. And when the parameter u trends to infinity, all the detectors proposed in this paper have similar performance.