This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the b...This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.展开更多
This paper proposes new hierarchical structures for generating pseudorandom sequences and arrays. The principle of the structures is based on a new concept-multi-interleaving. It is the generalization of normal sequen...This paper proposes new hierarchical structures for generating pseudorandom sequences and arrays. The principle of the structures is based on a new concept-multi-interleaving. It is the generalization of normal sequence decimation(sampling). The kernal of the structures is a lower speed linear feedback shift register together with several high speed time-division multiplexers arranged hierarchically. These new structures have much higher speed compared with that of other schemes proposed before.展开更多
利用负超可加相依(NSD)随机阵列的Rosenthal型矩不等式和截尾方法,在随机阵列{X nk,1≤k≤k n,n≥1}关于{a nk,1≤k≤k n,n≥1}一致可积的条件下,讨论NSD随机阵列加权和最大值max 1≤j≤k n∑j k=1 a nk X nk-E∑j k=1 a nk X nk的弱收...利用负超可加相依(NSD)随机阵列的Rosenthal型矩不等式和截尾方法,在随机阵列{X nk,1≤k≤k n,n≥1}关于{a nk,1≤k≤k n,n≥1}一致可积的条件下,讨论NSD随机阵列加权和最大值max 1≤j≤k n∑j k=1 a nk X nk-E∑j k=1 a nk X nk的弱收敛、L r收敛和完全收敛性.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61071163,61271327,and 61471191)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(Grant No.BCXJ14-08)+2 种基金the Funding of Innovation Program for Graduate Education of Jiangsu Province,China(Grant No.KYLX 0277)the Fundamental Research Funds for the Central Universities,China(Grant No.3082015NP2015504)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA),China
文摘This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.
文摘This paper proposes new hierarchical structures for generating pseudorandom sequences and arrays. The principle of the structures is based on a new concept-multi-interleaving. It is the generalization of normal sequence decimation(sampling). The kernal of the structures is a lower speed linear feedback shift register together with several high speed time-division multiplexers arranged hierarchically. These new structures have much higher speed compared with that of other schemes proposed before.
文摘利用负超可加相依(NSD)随机阵列的Rosenthal型矩不等式和截尾方法,在随机阵列{X nk,1≤k≤k n,n≥1}关于{a nk,1≤k≤k n,n≥1}一致可积的条件下,讨论NSD随机阵列加权和最大值max 1≤j≤k n∑j k=1 a nk X nk-E∑j k=1 a nk X nk的弱收敛、L r收敛和完全收敛性.
基金supported by the National Natural Science Foundation (10771070)Talents Youth Fund of Anhui Province Universities (2011SQRL012ZD)the 211 Project of Anhui University (2009QN020B)
