# A Robust Dual-Platform GMTI Method against Nonuniform Clutter  
[PERSON]  
1 School of Electronic Science, National University of Defense Technology, Changsha 410073, China;  
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Time-space adaptive processing (STAP) is a space-time filter design method aiming at the space-time coupling of ground clutter, which can greatly reduce the minimum detectable velocity (MDV) of the target and improve GMTI performance [7; 8]. [PERSON] deduced the space-time two-dimensional adaptive optimal processor structure, namely the optimal STAP, according to the maximum likelihood ratio theory. The research results showed that STAP could effectively compensate the platform motion effect of airborne radar and obtain ideal clutter suppression capability. The core problem of optimal STAP is to accurately estimate the CCM of the cell under test (CUT). However, in the actual working environment, a complex ground scene clutter with alternating hot and cold clutter, strong interference targets as well as other factors often lead to uneven clutter distribution. At this time, the pollution of nonuniform samples and the reduction of available uniform samples make the clutter covariance matrix (CCM) estimated by the maximum likelihood method mismatch with the clutter statistical characteristics of the the cell under test (CUT), which leads to the serious deterioration of the STAP performance. In view of the above problems, domestic and foreign scholars have put forward corresponding solutions from different perspectives. According to the low-rank characteristics of clutter, the reduced-rank STAP algorithm estimates the clutter subspace based on the feature space classification analysis method, which alleviates the performance loss caused by insufficient samples in a heterogeneous environment [13; 14; 15]. Based on this idea, two methods were proposed: the direct data domain least-squares (D3-LS) STAP method [16] and the maximum likelihood estimation detector (MLED) method [17; 18; 19]. Unfortunately, the benefits of these methods are generated by reducing the degree of freedom (DOF) in the system, which leads to performance degradation. Recently, many new methods have also been proposed. A CRM STAP method for BiSAR-GMTI nonstationary clutter suppression was proposed in [20]. In [21], a new intrinsic cyclic characteristic of CCM was found. After that, the knowledge-assisted STAP algorithm provides an alternative solution to reduce the required samples using prior knowledge [22]. In order to obtain accurate clutter information for the CCM estimation, an auxiliary channel selection method based on prior knowledge was proposed. The auxiliary channel was selected along the clutter ridge of the first-order sea clutter, and its distribution could be determined by the system parameters and used as prior knowledge. Unfortunately, it is not easy to acquire accurate knowledge. Recently, the correlation of matrix information geometry method for radar signal processing has become a focus and been proved [23; 24]. These methods make use of the nonlinear geometry of the matrix manifold and show advantages in terms of signal processing. In [25], a manifold-based clutter suppression method with limited samples was proposed. Ref. [26] mentions a new SAR-GMTIm algorithm in the framework of compressed sensing (CS), which is able to obtain high-resolution SAR images with a high focus response and precise localization. In [27; 28], a novel MTI scheme, which consists of DPCA detection, clustering and ATI discrimination, is also noted to reduce the probability of false alarms and improve the detection performance. However, although these methods suppress clutter in several ways, such as reducing the number of samples, reducing dimensionality, and selecting uniform samples, they have not fundamentally solved the problem. In this paper, we propose a robust GMTI method using a spaceborne dual-platform synthetic aperture radar system.