## 1 Introduction  
Nowadays, radar is finding more and more applications. In general, radar is a device for acquiring information about physical objects and their surroundings based on signals received from sensors that are not in direct contact with these targets. Radar is no longer just a device for detecting targets and the distance to them using electromagnetic waves. It is also a device capable of measuring the speed and direction of a target, carrying out identification, or creating an image of the earth's surface. For this reason, the development of new algorithms related to radar signal processing technology is required [1; 2; 3].  
The space-time adaptive processing (STAP) technique is used to detect targets moving on the earth's surface through a radar system mounted on a flying platform. This technique allows the detection of moving targets against the background of two-dimensional interferences. STAP refers to the augmentation of adaptive antenna techniques to processors that simultaneously combine the signals received on multiple elements of an antenna array (the spatial domain) and from multiple pulse repetition periods (the temporal domain) of a coherent processing interval. This technique allows the detection of moving targets against the background of two-dimensional interferences [4].  
The key step in the STAP algorithm is the correct determination of the clutter covariance matrix. In the literature on the subject, it can find various methods of estimating the clutter covariance matrix. The general division into statistical and non-statistical methods is applied [5].  
The most popular statistical method is the SMI method. This method is most often the reference method for newly proposed methods. In statistical methods, the clutter covariance matrix is determined using data from training cells surrounding the cell under test [6].  
Non-statistical methods mainly operate on a single cross-section of the radar data cube, thus reducing the computational complexity. In addition, they are effective for both homogeneous and heterogeneous clutter [7]. On the other hand, the use of non-statistical methods sometimes results in a decrease in performance relative to statistical methods.  
MIMO radar refers to a radar system with multiple transmitters and multiple receivers. Compared to traditional phased array radar, MIMO radar can transmit multiple independent measurement waveforms at the same time, making it possible to increase the diversity of waveforms.  
According to the array configurations, two types of MIMO radar should be distinguished: statistical MIMO radar and coherent MIMO radar [8; 9; 10]. Statistical MIMO radar refers to a system with widely separated transmit and receive antennas. The spatial diversity provided by statistical MIMO radar can be used to achieve higher localization accuracy and to cope with target amplitude fluctuations [11; 12]. Unlike statistical MIMO radar, the antennas of coherent MIMO radar are collocated; hence, it is known as collocated MIMO radar. Compared with phased array radar, coherent MIMO radar can achieve a higher number of degrees of freedom (DOF) [13; 14].  
Thanks to its excellent target detection performance and parameter estimation, MIMO radar has recently found many applications, which include autonomous driving [15], remote sensing [16], landmine detection [17], and disaster management [18].