# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 3 Results
### Measurement Results
#### 3.2.2 Two-Dimensional Surface Target Experiment  
In this section, MIMO radar data are used to verify the performance of the proposed method. The original optical scene photographed by the drone is shown in Figure 9. The  
Figure 8: Processing DOA results. (**a**) the DAS method, (**b**) the IAA method, (**c**) the LASSO method, (**d**) the proposed method.  
experimental parameters are listed in Table 3. The carrier frequency, the number of array elements, and the beam width have a determined relationship, which mainly affects the azimuth resolution. The pulse width and pulse repetition interval affect the signal to noise ratio, and bandwidth affects the range resolution.  
Figure 10a shows the 2D results of the DAS method for which the processing time is 0.0973 s. It is seen that a row of vehicles in the parking lot is vaguely distinguished. However, due to the low resolution and poor side lobe suppression ability of the DAS method, the imaging results with regard to the vehicle in the center of the scene suffer from high side lobes, and the vehicles on the right side of the scene are blurred. Figure 10b shows the 2D results of the IAA method [1] for which the processing time is 124.8980 s. It is seen that the resolution of this image is effectively improved. The side lobes of the strong scattering point are well suppressed. Figure 10c shows the 2D result of the LASSO method for which the processing time is 294.1526 s. It is seen that the resolution of this image is further improved. The side lobes of the car in the center of the scene are significantly suppressed, and the car contour becomes much clearer. The number and position of vehicles on the right side of the image can be easily determined. Unfortunately, both the IAA and the LASSO methods have enormous computational complexity, and cannot implement the online processing of the MIMO data. Figure 10d shows the 2D results of the proposed method for which the processing time is 4.8980 s. It is seen that this image is much better than that shown in Figure 10a,b. The proposed method maintains the same angular resolution as the LASSO method and can update the imaging results as the MIMO sub-aperture data are received in real time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 77 GHz \\ Bandwidth & 3.75 GHz \\ Beam width & 1.4\({}^{\circ}\) \\ Pulse width & 1 ms \\ Pulse repetition interval (PRI) & 512 μs \\ Number of transmitters & 12 \\ Number of receivers & 16 \\ Range samples & 261 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter conditions of the measured data.  
Figure 9: Optical scene.  
Figure 10: Two–dimensional processing results. (**a**) Two–dimensional result of the DAS method, (**b**) 2D result of the IAA method [1], (**c**) 2D result of the LASSO method, and (**d**) 2D result of the proposed method.  
The image entropy (IE) [49] is employed to quantitatively evaluate the performance of the 2D surface target experiment, where low values of IE commonly indicate that the image is well recovered. The image entropy of the above processed results is shown in Table 4. It can be seen that the entropy of LASSO and the proposed method are smaller than that of the DAS and IAA methods. This proves that the results of LASSO and the proposed method are clearer than those of other methods. More importantly, compared with Figure 8c,d, it can be seen that the proposed method has almost no performance loss compared with the traditional LASSO.