Research On STAP Algorithm In Nonhomogeneous Environments

The technique of Space Time Adaptive Processing (STAP) can suppress clutter effectively and improve the target detection performance of airborne phased array radar greatly. In the beginning, the focus of the STAP research at home and abroad is on the theory of optimum full rank space time adaptive detection and the characteristics of airborne radar clutter. Although full rank STAP exhibits excellent performance,both its computation load and its implementation complexity are so great that it is unable to be implemented in real time. Thereby, the focus of successive research is concentrated on reduced-rank (RR) algorithms. Once the solution is obtained, the progress to implement STAP in engineering will be accelerated greatly.The premise of the superiority of the statistical STAP is to have ample homogeneous training samples that are independently and identically distributed (IID) with interference in the test sample, in order to estimate the covariance matrix. However, in practical airborne radar system, the degree of freedom of the system is very high, and the actual environment is often changeful, which cause the condition difficult to satisfy. At this time we call the training samples are nonhomogeneous. The performance of the conventional statistical STAP meets great degradation under nonhomogeneous samples or environment. This paper puts great emphasis on the investigation of the STAP algorithm in nonhomogeneous environments. In Chapter 2,We primarily introduced some basic theory of the STAP algorithm,and we introduced a method for nonhomogeneous samples choosing, nonhomogeneity detector (NHD) based on nonhomogeneous environments.In Chapter 3, we introduced some conventional statistical reduced-rank (RR) partially STAP algorithm. Rank reduction can be conducted in space,time or joint space time domain. Wherever it is realized,the process of rank reduction may be equivalent to an operation on full space time data vectors with a transfer matrix T. If T is independent of the data eigen-structure, the processor structure of this kind is prefixed in advance as well. However, if T is eigen-structure dependent, that is to say, STAP is performed in data eigenspace. The research result show that the output signal to clutter plus noise ratio of reduced-rank (RR) partially STAP will be probably near as or even better than of full rank STAP. In this Chapter,we introduced two basic types of adaptive processor, one is Direct From Processor (DFP) and the other is Generalized Sidelobe Canceler (GSC) and discussed the equivalence between DFP and GSC.In Chapter 4, a conjoint algorithm of nonhomogeneity detection and JDL is researched, which use the configuration of joint domain localised (JDL) reduced-rank partially STAP that applies to training data. In addition, the aforementioned approach, which use nonhomogeneity detector based on adaptive power residual(APR) eliminate the outliers,and then educe the adaptive weight vector. Theoretical analysis and simulation results are presented to demonstrate that, the aforementioned approach can effectively eliminate the outliers and improve the targets detection performance. In addition, the aforementioned approach can significantly reduce the computational load, which feasible for engineering application.In Chapter 5, we introduced the direct data domain (DDD) algorithm of STAP. The DDD algorithm,putting forward by Sarker and other people,can directly use the sample data which is to be tested to compute weight vector by treating the data of the range gate to be tested with signal filtering. In this algorithm,not any other sample data of range gate is used. And the algorithm has good performance under nonhomogeneous environments.