Study On Environment Knowledge-Aided Robust Space Time Adaptive Processing Algorithm

In real application, the complex battle field makes radar facing a heterogeneous clutter environment, which degraded the performance of traditional space time adaptive processing(STAP) algorithms seriously. Therefore, this dissertation focuses on exploiting the environment knowledge-aided robust STAP methods to improve the clutter/interference suppression performance in non-homogeneous environment under the airborne array radar application.First, a clutter subspace STAP method utilizing the clutter distribution and geometrical configuration has been proposed based on the time-limited and band-limited features of the prolate spheroidal wave functions. It can reduce the computational complexity and demand for homogeneous samples of the system. However, it has some performance degradation when there is a crab angle which made it impossible to estimate the precise clutter rank. Therefore, based on the radar parameters and the detection environment geometry, a pre-process method is presented to eliminate the crab influence and to improve the clutter suppression performance in heterogeneous environment.Then, aiming at the problems of the computational complexity and susceptible to the outlier of the traditional generalized inner product method, a knowledge-aided sample selecting method has been proposed combined with the prior knowledge of the certain distribution in space-time plane of the clutter in side-looking situation. In addition, the signal model of the arbitrary antenna array has been analyzed. Because of the traditional non-homogeneity detector has significant performance degradation for the arbitrary antenna array, a contaminated selecting method has been proposed based on the prior knowledge that the clutter subspace is only depends on the clutter’s space-time steering vector.Finally, when there exists the isolate interference, the performance of the statistical STAP methods reduced seriously. In order to solve this problem, a two dimension amplitude and phase estimation method is proposed based on sliding windows in spatial and temporal of the cell under test. The proposed methods utilizing the statistical information of the echo, not only suppress the clutter and the isolate interference effectively, but also reduce thesystem computational complexity. Then, a robust amplitude and phase estimation method is proposed to against steering vector mismatch which is caused by the inaccuracy prior knowledge of the target in complex battle environments.The effectiveness of the research achievements of this dissertation have been verified though the simulation and real data, which have a certain significant theoretical guidance and valuable for engineering application.