分布式无源雷达接收机配置优化及其成像技术
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摘要
由于其较低的成像成本和较强的鲁棒性,使得利用多发射机和多接收机对目标进行有效观测的分布式无源雷达成为雷达技术研究的热门领域。本文在分布式雷达稀疏成像模型基础上,提出一种分布式无源雷达成像接收机配置优化方法,以成像分辨率最高为优化目标函数,针对不同发射机布局采用遗传算法计算出最优接收机布局。同时针对正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法在低信噪比下成像精度较低,信号估计不准确的情况,推导出用协方差稀疏表示接收信号,利用稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)进行信号重构的成像算法,并通过仿真实验对成像性能的改善进行了验证。
Due to its low imaging cost and strong robustness,the distributed passive radar had become a popular research area. Based on the distributed radar sparse imaging model,this paper proposes a distributed passive radar imaging receiver configuration optimization method,with the highest imaging resolution as the optimization objective function,and the genetic algorithm to calculate the optimal receiver for different transmitter layouts. At the same time,the Orthogonal Matching Pursuit algorithm has low imaging accuracy and low SNR,and the signal estimation is inaccurate. It is derived that the received signal is sparsely represented by covariance,and Sparse Bayesian Learning is used. An imaging algorithm for signal reconstruction is performed,and the improvement of imaging performance is verified by simulation experiments.
Due to its low imaging cost and strong robustness,the distributed passive radar had become a popular research area. Based on the distributed radar sparse imaging model,this paper proposes a distributed passive radar imaging receiver configuration optimization method,with the highest imaging resolution as the optimization objective function,and the genetic algorithm to calculate the optimal receiver for different transmitter layouts. At the same time,the Orthogonal Matching Pursuit algorithm has low imaging accuracy and low SNR,and the signal estimation is inaccurate. It is derived that the received signal is sparsely represented by covariance,and Sparse Bayesian Learning is used. An imaging algorithm for signal reconstruction is performed,and the improvement of imaging performance is verified by simulation experiments.
引文
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[2]Engl H W,Ramlau R.Regularization of Inverse Problems[M].Kluwer Academic,1996.
[3]王硕.分布式无源雷达成像方法研究[D].中国科学技术大学,2014.Wang Shuo.Research on Distributed Passive Radar Imaging Method[D].University of Science and Technology of China,2014.(in Chinese)
[4]Yin J,Chen T.Direction-of-Arrival Estimation Using a Sparse Representation of Array Convariance Vectors[J].Signal Processing IEEE Transactions on 2011,2011,59(9):4489-4493.
[5]Alam M,Jamil K.Maximum likelihood(ML)based localization algorithm for multi-static passive radar using range-only measurements[C]∥IEEE Radar Conference.IEEE,2015:180-184.
[6]王天云.分布式雷达稀疏成像技术研究[D].中国科学技术大学,2015.Wang Tianyun.Research on Distributed Radar Sparse Imaging Technology[D].University of Science and Technology of China,2015.(in Chinese)
[7]Hu X,Zhang S,Lu Z,et al.Receiver disposition optimization in disposition optimization in distributed passive radar imaging[C]∥IGARSS 2016-2016 IEEEInternational Geoscience and Remote Sensing Symposium.IEEE,2016:1018-1021.
[8]Ottersten B,Stoica P,Roy R.Covanriance Matching Estimation Techniques for Array Signal Processing Applications[J].Digital Signal Processing,1998,8(3):185-210.
[9]Scherzer O.The use of Morozov’s discrepancy principle for Tikhonov regularization for solving nonlinear ill-posed problems[J].Computing,1993,51(1):45-60.
[10]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.