摘要
雷达前视成像技术在精确制导打击、自主下降着陆、汽车自动驾驶等军民领域具有广阔的应用前景。由于多普勒相位历程的限制,机载平台的前视成像分辨率较低。解卷积方法可以进行前视成像,但当前视成像场景复杂时,现有的前视成像方法的成像质量会下降。针对复杂前视成像构型下的场景稀疏度度量和表征问题,该文提出一种基于概率模型驱动的机载贝叶斯前视超分辨多目标成像方法。首先通过将前视成像场景的数据维度由单帧空间扩展到多帧空间提升场景的稀疏度,然后基于广义高斯概率模型对成像场景的稀疏特性进行统计建模和稀疏度求解,最后基于贝叶斯框架完成稀疏前视成像。由于选取的稀疏度表征参数嵌入到前视成像的整个过程中,在每次迭代期间都会进行前视成像参数的更新,从而保证了前视成像算法的稳健性。通过计算机结果和实测数据处理,验证了该文方法的有效性。
Forward-looking imaging is crucial in many civil and military fields,such as precision guidance,autonomous landing,and autonomous driving.The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history.The deconvolution method can be used to improve the quality of forward-looking imaging;however,it will not work well for complex imaging scenes.To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations,an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution.First,the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene.Then,the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model.Finally,the super-resolution imaging problem was solved using the Bayesian framework.Because the sparsity characterization parameters are embedded in the entire process of imaging,the forward-looking imaging parameters will be updated during each iteration.The effectiveness of the proposed algorithm was verified using simulation and real data.
作者
陈洪猛
余继周
张文杰
李亚超
李军
蔡良
鲁耀兵
CHEN Hongmeng;YU Jizhou;ZHANG Wenjie;LI Yachao;LI Jun;CAI Liang;LU Yaobing(Beijing Institute of Radio Measurement,Beijing 100854,China;National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)
出处
《雷达学报（中英文）》
EI
CSCD
北大核心
2023年第6期1125-1137,共13页
Journal of Radars
基金
国家自然科学基金(62101396,62171337)。
关键词
前视成像
超分辨成像
稀疏特性
概率模型
多普勒解卷积
Forward-looking imaging
Super-resolution imaging
Sparsity
Probability model
Doppler deconvolution
