空间目标T/R-R型雷达多视角融合成像算法

doi:10.12305/j.issn.1001-506X.2024.09.14

Abstract

Abstract:

A T/R-R (transmitting receiving-receiving) radar sparse aperture multi-view fusion imaging method based on the complex Bayesian compressed sensing (BCS) algorithm is proposed for the practical imaging of T/R-R configuration radar, taking into account the orbital priors of space targets and the advantages of the dual-station inverse synthetic aperture radar (ISAR) imaging system. On the basis of establishing a dual station radar fusion imaging model, the proposed method utilizes Laplace priors to establish a sparse model of the target in the complex domain, improving the sparsity promotion effect of the algorithm and obtaining high-resolution target images. The simulation experiment results show that the proposed method can not only achieve multi-view fusion imaging of dual station radar, but also achieve multi-view fusion imaging of dual-station radar with sparse aperture respectively, further expanding the application scenarios and effectively improving azimuth resolution and imaging quality.

Key words: T/R-R (transmitting receiving-receiving) radar, space target, multi-view fusion imaging

CLC Number:

TN958

Baofeng GUO, Liting JIAO, Sheng LI, Xiaoxiu ZHU, Dongfang XUE, Huixian SUN. Multi-view fusion imaging algorithm for T/R-R radar of space targets[J]. Systems Engineering and Electronics, 2024, 46(9): 3019-3030.

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参数名称	参数值	参数名称	参数值
载频/GHz	10	脉冲重复频率/Hz	100
带宽/MHz	800	累积脉冲个数/个	100
采样率/GHz	1	旋转角速度/(rad/s)	0.026 5
脉冲宽度/μs	20	累积转角/(°)	1.5

算法	指标	SNR/dB
算法	指标	20	10	0
稀疏约束最优化算法	IC	51.35	46.49	39.38
稀疏约束最优化算法	TBR	29.54	25.47	20.16
本文算法	IC	54.98	51.14	46.28
本文算法	TBR	32.56	28.73	25.17

算法	指标	SNR/dB
算法	指标	20	10	0
稀疏约束最优化算法	IC	43.35	34.49	21.38
稀疏约束最优化算法	TBR	25.13	19.47	12.32
本文算法	IC	51.51	47.13	44.25
本文算法	TBR	30.17	26.85	23.35

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Multi-view fusion imaging algorithm for T/R-R radar of space targets

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