## Qinghua Liu* , Yuanxin He* and Chang Jiang*## |

Algorithm | SNR (dB) | Before adding threshold | After adding threshold |
---|---|---|---|

Capon, Fig. 3(a) and 3(b) | -10 | 5.5632 | 0.0590 |

MUSIC, Fig. 3(c) and 3(d) | -10 | 4.9633 | 0.0101 |

Capon, Fig. 4(a) and 4(b) | 0 | 3.9535 | 0.0096 |

MUSIC, Fig. 4(c) and 4(d) | 0 | 2.6576 | 0.0016 |

Although the simulation results in the previous section have achieved the localization of multi-targets with different distances, it can be found that this method still has room for improvement in localization efficiency.

Different imaging resolutions should be adopted for different areas. Obviously, the target areas that we are interested in only occupy a small part of the entire detection area, therefor, the imaging resolution of the target areas are needed to increase. For non-target area, the redundant information is wanted to be discarded, and the imaging resolution is reduced.

By observing the relationship between the average program running time (APRT) and the grid spacing in Fig. 5, it can be seen that APRT decreases rapidly with the increase of grid spacing, which means that the imaging resolution has a great impact on the running time of the program. It shows that the optimization of algorithm efficiency can be effectively achieved by reducing the imaging resolution of non-target area on the premise of ensuring the high resolution of target area.

Based on the above analysis, simulation experiments are carried out on the detection area as below. The simulation parameters are consistent with the above simulation with SNR=–10 dB. The grid spacing in the low-resolution estimation of the entire detection area is [TeX:] $$\Delta d=0.05$$ m, and the grid spacing in the high-resolution estimation of the target area is [TeX:] $$\Delta d=0.01$$ m. The simulation results in Fig. 6 show that the target location is not accurately estimated under the low resolution, and the detail of the target area are missing, while the imaging result of the optimization algorithm still maintains a high resolution with the smaller value of APRT and the almost same value of ENT in Table 2.

For the conventional linear array MIMO radar, the reverse projection method is unable to correctly locate multi-targets with different distances because of the coupling of distance and angle in near-field. The structure of symmetric sub-array is designed, and the received signals of two sub-arrays are jointly reconstructed in this paper. The reconstructed signals can realize the distance-independent DOA estimation, and the subsurface target localizations are obtained with different distances. On this basis, the grid zooming design of spatial segmentation is used to optimize the localization efficiency. The effectiveness of the proposed localization method and optimization schemes is verified by simulation results.

This paper is supported by the National Natural Science Foundation of China (No. 61861011), the Guangxi Natural Science Foundation (No. 2016GXNSFAA380036 and 2018GXNSFAA138091), and the Science and Technology on Near-Surface Detection Laboratory Foundation (No. TCGZ2017A010), and the Major Science and Technology Foundation of Guangxi Province (No. AA17204093).

She received her B.Sc. degree in 1995 from Sichuan Normal University, M.Sc. degree in 2001 from Guilin University of Electronic Technology, and Ph.D. degree in 2014 from Xidian University. Now she is a professor in Guilin University of Electronic Technology. Her main research interest is radar signal processing.

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