Research On Ground Moving Target Detection Techniques In Nonhomogeneous Environment

The inhomogeneous phenomena of nonhomogeneity of clutter power, interference target and isolated interference are always coexisting in the real environment of airborne radar, however, the majority of inhomogeneous detection methods are researched only for one of inhomogeneous phenomena at present. Therefore research on new inhomogeneous detection methods applied to the case of coexisting several inhomogeneous phenomena has become an important subject in the field of research on radar signal detection technology.In this paper, we discussed the impact of several different inhomogeneous phenomena on the statistical STAP detection performance, and introduced the principles of several different detection methods which are always used to suppress one of inhomogeneous phenomena. Based on analyzing the impact of the environment of coexisting several inhomogeneous phenomena on those detection methods introduced above, the emphasis is on researching the inhomogeneous detection methods for the real environment of airborne radar. Firstly, the traditional Direct Data Domain (DDD) algorithm is improved by adding the mainlobe clutter compensation factor since in which pre-filtering target signal will arouse serious influence to output of signal to clutter and noise ratio (SCNR) , and simulation results show that the improved algorithm can increase SCNR; the multiple constraints method is applied to the improved algorithm for the case that the estimated direction of target signal is inaccuracy, and simulation experiments indicate that it protects the gain of the direction of target signal; in view of decreasing the system implementation complexity of direct data domain STAP method, a new phase-only direct data domain STAP algorithm based on hybrid genetic algorithms is presented, and it is effective proved by simulation experiments. Secondly, the new combined Space Time Auto Regressive (STAR) algorithm is proposed for suppressing all three kinds of inhomogeneous phenomena while the existed STAR algorithms can not do, and the proposed algorithm can suppress all three kinds of inhomogeneous phenomena effectively indicated in the results of simulation. At last, the DDD algorithm and the STAR algorithm of basing on pulse-dimensional data model are researched, and simulation results show that the moving target detection performances of those two algorithms are better than traditional DDD algorithm.