考虑地形起伏的机载SAR测绘航线规划方法

doi:10.7527/S1000-6893.2022.28143

Abstract

Abstract:

Synthetic Aperture Radar （SAR） can obtain high-resolution images regardless of the influence of weather and time； therefore， it is widely used in mapping， disaster monitoring， environmental monitoring， resource reconnaissance， etc. Due to the side-view imaging of SAR， the radar beam will be blocked by obstacles in some complex terrains， resulting in layover and shadow. This will not only cause data missing but also aggravate the workload of subsequent data processing. To address the above problems， it is usually necessary to ensure the overlap ratio of adjacent flight swaths to reduce the influence of layover and shadow on image quality when planning the path of the Unmanned Aerial Vehicle （UAV）-borne SAR. To reduce the overlap ratio while ensuring high-quality images， this paper studies the SAR mapping path planning problem considering terrains. Taking the lateral overlap ratio， coverage ratio， and overlap ratio as evaluation targets， a UAV-borne SAR mapping path planning method is proposed based on simulated annealing. Simulation experiments are carried out to verify the proposed method. The experimental results show that the method proposed can control the lateral overlap ratio of flight swath within the set value， and make the coverage ratio reach more than 90%. The number of paths decreased by 31.21% compared with that of the method based on the average altitude plane， and the coverage ratio of test area increased by 84.8% compared with that of the method without considering the terrain. The geometric deformation ratio and the number of redundant paths are also reduced by 8.73% and 100% respectively compared with that of the Greedy algorithm.

Key words: area coverage, path planning, UAV surveying and mapping, UAV-borne SAR, terains, simulated annealing

CLC Number:

V279

Yougang XIAO, Xiangna MAN, Guohua WU, Qizhang LUO. Surveying and mapping path planning method for UAV-borne SAR considering terains[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(17): 328143.

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参数	C1	C2	C3	C4
平均高程/m	1 335.67	15.53	1 299.99	1 505.22
高程标准差/m	272.50	1.19	198.35	264.60
最小高程/m	772.44	11.12	851.52	966.74
最大高程/m	2 133.59	19.34	1 885.99	2 242.42
角点1/（°）	E109.752 N31.895	E116.418 N 37.781	E109.618 N31.896	E109.752 N31.835
角点2/（°）	E109.752 N31.847	E116.418 N37.772	E109.618 N31.846	E109.752 N31.789
角点3/（°）	E109.815 N31.847	E116.430 N37.772	E109.679 N31.846	E109.815 N31.789
角点4/（°）	E109.815 N31.895	E116.430 N37.781	E109.679 N31.896	E109.815 N31.835
投影面积/（km）²	31.51	1.05	31.54	30.57

参数	C1	C2	C3	C4
雷达俯角补角/（°）	10	10	10	10
视场角/（°）	20	40	20	20
飞行高度/m	2 233.59	119.34	1 985.99	2 342.42
旁向重叠度/%	30	30	30	30

模型	航线数量/条				覆盖率/%
模型	C1	C2	C3	C4	C1	C2	C3	C4
CPPM	11.4	11.6	13	10.6	95.42	95.09	95.56	92.51
CPP1	17	13	23	17	94.81	90.58	96.72	94.64
CPP2	8	11	9	7	52.00	90.99	39.74	43.90
模型	几何形变率/%				旁向重叠度/%
模型	C1	C2	C3	C4	C1	C2	C3	C4
CPPM	14.62	0.83	8.79	15.40	22.88	25.89	24.69	22.55
CPP1	6.96	0.04	3.28	11.90	29.88	10.60	33.14	36.03
CPP2	4.59	0.00	1.14	5.65	0	10.02	0.36	0

参数	C1		C2		C3		C4
参数	CPP1	CPP2	CPP1	CPP2	CPP1	CPP2	CPP1	CPP2
航线数量	3.066 98×10^-6	3.066 98×10^-6	0.009 023 439	0.117 185 087	6.704 11×10^-6	6.704 11×10^-6	3.066 98×10^-6	3.066 98×10^-6
覆盖率	0.061 969 62	0.061 969 62	0.009 023 439	0.009 023 439	0.520 050 527	6.704 11×10^-6	0.119 844 279	3.066 98×10^-6
几何形变率	3.066 98×10^-6	3.066 98×10^-6	0.117 185 087	0.601 508 134	6.704 11×10^-6	6.704 11×10^-6	0.000 621 759	3.066 98×10^-6
旁向重叠度	3.066 98×10^-6	3.066 98×10^-6	0.009 023 439	0.009 023 439	6.704 11×10^-6	6.704 11×10^-6	3.066 98×10^-6	3.066 98×10^-6

算法	覆盖率/%				几何形变率/%
算法	C1	C2	C3	C4	C1	C2	C3	C4
CPPM	97.30	93.98	96.55	92.80	8.03	0	5.47	14.73
Greedy	96.81	93.66	95.50	92.95	8.41	0	7.86	14.73
算法	航线数量/条				冗余航线数量/条
算法	C1	C2	C3	C4	C1	C2	C3	C4
CPPM	11	11	14	10	0	0	0	0
Greedy	12	10.5	13.8	10	1	0	0.8	0.2

Surveying and mapping path planning method for UAV-borne SAR considering terains

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