Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise. It is a great challenge to precisely recover the pulse signal in loud background noise. The conven...Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise. It is a great challenge to precisely recover the pulse signal in loud background noise. The conventional approaches,which are mostly based on the distribution of the pulse energy spectrum,do not well determine the locations and shapes of the pulses. In this paper,we propose a time domain method to reconstruct pulse signals. In the proposed approach,a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions. The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm. Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.展开更多
Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures t...Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.展开更多
基金Supported by the National Natural Science Foundation of China(61501385)Science and Technology Planning Project of Sichuan Province,China(2016JY0242,2016GZ0210)Foundation of Southwest University of Science and Technology(15kftk02,15kffk01)
文摘Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise. It is a great challenge to precisely recover the pulse signal in loud background noise. The conventional approaches,which are mostly based on the distribution of the pulse energy spectrum,do not well determine the locations and shapes of the pulses. In this paper,we propose a time domain method to reconstruct pulse signals. In the proposed approach,a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions. The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm. Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.
基金Supported by the National Natural Science Foundation of China(61471386)
文摘Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.
