Reseach On KA-STAP For Airborne Phased Array Radar

Space-time adaptive processing(STAP) is a key technology for airborne phase-array radar to detect moving targets. In the non-homogeneous clutter environment, adaptive performance decreases seriously due to the lack of a sufficient number of independent and identically distributed(IID) training samples for estimating the statistical properties of clutter, which is the biggest problem for STAP processing. Therefore, new STAP algorithms which can adapt to non-homogeneous clutter environment become one of current research directions of STAP technology. A priori knowledge of the detecting environment is used in radar signal processing to implement knowledge-aided space-time adaptive processing(KA-STAP), which is a research hotspot to solve the problem about non-homogeneity of clutter and improve the performance of STAP. In addition, tremendous computational complexity is another problem faced by STAP. Therefore, the research of this paper will focus on KA-STAP technology and its calculating algorithm in numerical domain. The research includes the following aspects:1. The problem of prior clutter covariance estimation of the detecting environment is studied. Prior information such as real scene topography, digital elevation model(DEM) data, radar system parameters and aircraft platform motion parameters are used to accurately estimate a priori clutter covariance of cells under test(CUT).2. A knowledge-aided adaptive power remaining(KA-APR) non-homogeneous sample detection algorithm is proposed in this paper. This algorithm indirectly using a priori knowledge of clutter to detect non-homogeneous training samples has better performance than conventional detection methods. The type of the test sample data(homogeneous or non-homogeneous) is determined according to proposed KA-APR detection algorithm. The problem about selecting adaptive filtering algorithms intelligently for different types of test samples is investigated.3. KA-STAP color loading technology and its numerical calculating algorithm are studied. A priori knowledge of clutter is used to filter the sampling clutter by pre-whitening filter in color loading processing, which reduces the dimension of clutter subspace and the need for IID samples in subsequent STAP processing. In order to achieve optimal performance, the derivation of an effective algorithm to calculate the optimal loading factors is proposed in detail. The color loading algorithm calculated in numerical domain is studied in depth. This numerical calculating algorithm based on QR and inverse QR decomposition has better numerical stability due to avoiding sample covariance inversion(SMI). Besides, the fast recursive color loading algorithm can be implemented easily through a highly parallel computing structure, which satisfies the requirements of real-time processing of large data throughput for the airborne radar.4. Lastly, a KA-STAP simulation system for airborne phase-array radar is built. The design method and implement structure of this system are analyzed for engineering applications of KA-STAP technology.