Radar systems are confronted with increasingly complex objectives in a highly non cooperative interference environment. To meet the challenge, sensor systems are forced to utilize multidimensional signal processing techniques. In fact, conventional signal processing perform poorly due to lack of knowledge of highly dimensional statistic requirements. On the other hand, traditional adaptive signal processing techniques break down or have suboptimal performance in these cases. A possible approach is optimal or suboptimal Space-Time Adaptive Processing (STAP) techniques. This article presents an introduction to STAP which was originally developped for detecting slow moving objectives from airborne radars. STAP is a data domain implementation of an optimal filter solution. The optimum filter is designed based on the known covariance matrix and the known Doppler-angle

Advanced airborne radar may require adaptive space-time processing (STP) to detect small targets at long ranges. STP is a multidimensional adaptive filter that resolves received radar data into a spectrum of plane waves in angular and Doppler Frequency coordinates. The platform of an airborne radar can scatter incident signals into the antenna, spreading the spectra of the clutter signals so that it overlaps the target signal and therefore increases the false alarm rate. To reduce the processing requirements of a full STP filter, a suboptimal architecture with fewer degrees of freedom is demonstrated and compared with the optimal architecture.

Space-time adaptive processing (STAP) has a huge computational complexity and a large training samples requirement, which limit its practical applications. The traditional post- Doppler adaptive processing methods such as factored approach (FA) and extended factored approach (EFA) can significantly reduce the computational complexity and the training sample requirement in adaptive processing, and maintain nearly the same performance as the optimal STAP. However, because training samples are restricted in real-world environ- ments, their performances can be considerably degraded in the large-scale antenna array. To solve this problem, the post-Doppler adaptive processing method based on the spatial domain reconstruction is proposed. In this method, the spatial clutter data after Doppler filtering is reconstructed as a matrix that has close columns and rows. The spatial weights vector in FA or EFA is also re-expressed as the product of two shorter weight vectors. Then the cyclic minimizer is applied to find the desired solution. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater moving target detection ability especially under the condition for large-scale antenna array and small training samples support than FA and EFA

The training data selection and update are crucial steps for the space-time adaptive processing (STAP) operation, since contaminated training data result in a decreased clutter suppression performance, an incorrect constant false alarm rate (CFAR) threshold and target cancellation by self-whitening. This paper presents a promising algorithm that selects the training data by applying a moving window, taking into account the changes of the clutter statistics over space and time. The goal is to improve the clutter suppression capability and, thus, to increase the number of true detections. The benefits of the proposed algorithm are verified using real 4-channel X-band radar data acquired by the DLR’s airborne F-SAR.