# A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging
## 5 Real Data Processing  
The performance of the proposed algorithm has also been proven by real measured data. Here, the measured Ka band radar data of the motion platform is used to verify the performance of the new algorithm. Figure 7 shows the installation of the Ka band radar system and a flight photo of the motion platform. From Figure 7a,b, we can find that the radar system is fixed at the bottom of the helicopter, which points to the front of the airplane heading. It irradiates the forward-looking region and records the received data.  
Figures 8 and 9 are two real measured data, which can verify that the proposed sparse Bayesian algorithm can be used in the typical applications of the obstacle avoidance of helicopters in adverse weather and ground target location and tracing, respectively.  
In the first experiment, the forward-looking imaging region of the helicopter mainly includes three buildings, which cannot be resolved in the real beam image of Figure 8b. The helicopter must spend more time passing around the buildings to avoid striking. Although the Poisson-based MAP algorithm improves the cross-range resolution, it cannot guide the pilot to across the buildings with a shortcut. The proposed sparse Bayesian algorithm significantly improves the cross-range resolution about four-times more than the Poisson-based MAP algorithm. We can clearly find the spacing among the buildings that makes us choose a better route to across the obstacles.  
In the second experiment, we put some corner reflectors in specific positions to verify the ground target detection performance of the proposed algorithm. In order to determine the superresolution limitation of the proposed algorithm, the experiments employ six groups of corner reflectors which are located at different range units with different intervals, including one isolated target and five groups of adjacent targets with an interval of one-time, four-times, eight-times, 10-times, and 12-times, respectively, corresponding to the real aperture antenna beamwidth. Figure 9a shows the distribution of corner reflectors.  
Figure 9 shows the processed results of real measured data. Figure 9b shows the real aperture image after pulse compression and motion compensation in the range dimension. In this experiment, the ground clutter has little affect on the imaging result because this experiment is to simulate the application of the target location of close-range and strong scattering targets. Compared with the real aperture image and superresolution results of the Poisson-based MAP approach, we can find that the proposed approach can compress the single target and resolve the adjacent targets. It improves the cross-range resolution by more than 12-times compared to the real beam echo, and the maximum location error of the corner reflectors is 0.1\({}^{\circ}\). However, the Poisson-based MAP approach cannot resolve most of the adjacent targets. It just compress the single corner reflector about three-times as the beamwidth. In conclusion, the processed result of the proposed approach has better cross-range resolution and contour features than the real aperture and the processed result of the Poisson-based MAP approach.  
Figure 7: The Ka bandwidth radar system and helicopter platform. (**a**) The Ka bandwidth radar system. (**b**) The helicopter platform.  
Figure 8: Processed results of real measured data. (**a**) The forward-looking imaging region of three buildings. (**b**) Real aperture image after pulse compression. (**c**) Superresolution result processed by the Poisson-based MAP algorithm. (**d**) Superresolution result processed by the proposed algorithm.  
Figure 9: Processed results of real measured data. (**a**) The distribution of corner reflectors. (**b**) Real aperture image after pulse compression in the range dimension. (**c**) Superresolution result processed by the Poisson-based MAP algorithm. (**d**) Superresolution result processed by the proposed algorithm.