Space-Time Adaptive Processing Algorithms And Its Real Time Implementation For Airborne Early Warning Radar

The Space-Time Adaptive Processing (STAP) algorithm that is well-known in the area of multi-channel Airborne Early Warning (AEW) radar has outstanding performance of clutter and jamming suppression. The practical STAP algorithms and its real time implementation problems are researched in this dissertation.To optimize existing reduced dimension STAP processor, local clutter DOFs (degree of freedom) problem is further researched. The impact of reduced dimension transformation on interference covariance matrix is analyzed. The functional relationship between the constitution of local processing domain and local clutter DOFs is presented. By this research finding, the number of adaptive weights can be known from the number of local clutter DOFs which can be calculated from the constitution of local processing domain. Then, the conclusion can be drawn that the system DOFs of current reduced dimension STAP processor can be feasibly reduced more by reduced-rank methods. The validity of the research is proved by simulated data and measurement data processing results.A robust localized space-time adaptive processor, which is called FDSP (Flexible DOF STAP Processor), is presented. The processor adjusts system DOFs adaptively with variable interference environment. Compared with the fixed DOF STAP processor, FDSP reduces large computational complexity and improves the performance of clutter and jamming suppression. The implementation of FDSP is illustrated. The validity of FDSP is proved by measurement data processing results.The data independent sample selection strategies are surveyed by measurement data of Chinese new type AEW radar, which indicates that range segment method have the best results. Then, the comparative study on PST (Power Selected Training) and range segment method is given, which shows that PST has better performance on strong clutter discretes rejection because it exploits more information inherent in the data. Many times, however, targets are also present whose inclusion in the STAP weight training results in significant target self-cancellation as well as degradation in clutter mitigation performance. An ameliorated PST training method is presented that excises targets from the training set based on a phase measurement for each potential STAP training sample. The resulting training method based on both phase and power selection critera is shown to offer significant performance gains on experimental data.The test results of two practical STAP weights computational algorithms are presented, which shows that both algorithms have satisfactory performance on resisting clutter and jamming, and traditional SMI algorithm has better performance on clutter rejection, but QRD-SMI is more robust to the effect of target self-nulling. Considering algorithm performance, numerical characteristic and parallelism, QRD-SMI is more suitable for real time implementation of STAP.STAP algorithms require very high computing power and hardly implements. A detail analysis of computation steps of partially-adaptive STAP algorithm is present, which indicates that there is a natural, inherent parallelism in STAP algorithm. A parallel algorithm based on multi-DSP system of partially-adaptive STAP is presented. The execution model, task mapping strategy and performance evaluation functions of this algorithm is also illustrated. Data remapping is used between successive computation steps of STAP. The effectiveness of the implementation is demonstrated with experimental results.