## 5 Simulation
### Improvement Factor  
The basic parameter that determines the performance of any STAP linear processor in the literature is called the improvement factor (IF), which defines the quotient of the signal-to-noise (SNR) power ratio at the output and input of the optimal processor. Taking into account the cited definition of the IF parameter, it is worth simulating the effect of the number of pulses on the value of the IF of the proposed method.  
Accordingly, the following figure shows the effect of the number of pulses processed by the STAP processor on the performance of the processor as measured by the IF coefficient. The analysis of the obtained results clearly shows that the more pulses transmitted by the radar need to be processed in the STAP processor, the higher the performance of the processor.  
Figure 5 shows the simulation for the case when the receiver's own noise-to-interference ratio was \(-20\) dB. If one assumes for the simulation a receiver noise-to-interference ratio of \(-30\) dB (Figure 6), which means less influence of the receiver's own noise, it turns out that the performance of the STAP processor improves significantly. From an engineering point of view, it can be concluded that it is worthwhile to use low-noise components in the radar's receiving path, which will significantly improve the performance of the STAP processor and thus have a positive effect on target detection.  
It was necessary to check the performance of the proposed SMI-LU method in terms of clutter suppression performance based on the IF. Figure 7 shows the clutter suppression performance for the four STAP algorithms used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Radar operating frequency & 10 GHz \\ Number of transmitting antennas & 5 \\ Number of receiving antennas & 5 \\ Pulse repetition frequency & 30 kHz \\ Pulse duration & 33 μs \\ Number of pulses & 10 \\ Range & 5000 m \\ Transmitter power & 5.6 kW \\ Directional gain & 20 dB \\ Platform height & 1000 m \\ Speed of movement of the platform & [160 m/s, 160 m/s, 0] \\ Speed of movement of the target & [30 m/s, 30 m/s, 0] \\ Initial localization of the platform & [0,0,1000] \\ Initial localization of the target & [0,1000,1000] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters adopted for simulation.  
The proposed method uses LU decomposition, for which better clutter suppression can be achieved compared to the same STAP, but by using QR decomposition (SMI) or other methods, the IF curve indentation is narrower and reaches higher values. Furthermore, the implementation of the SMI algorithm based on QR decomposition is much more efficient than the other methods.  
Figure 5: Diagram of the IF depending on the number of pulses. The ratio of the receiver’s own noise-to-interference equal to \(-20\) dB.  
Figure 6: Diagram of the IF depending on the number of pulses. The ratio of the receiver’s own noise-to-interference equal to \(-30\) dB.  
complicated programmatically, which also affects its practical application. The STAP-LU algorithm is much simpler computationally, which is a direct result of the previously cited steps of both algorithms, which consist of numerous operations performed on the matrices.  
The IF of more than 70 dB is a very good result in the context of the IF parameter results boasted by researchers in recent publications [21; 22; 23; 24]. Moreover, it provides evidence of proper clutter cancellation. It can be concluded that the developed algorithm and its software implementation in the Matlab environment encourage one to go a step further and try practical verification of the proposed solution. Unfortunately, this involves the outlay of considerable financial costs.