The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR)imaging has been successfully addressed by popular motion compensation algorithms.However,when the target’s rotational velocity is su...The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR)imaging has been successfully addressed by popular motion compensation algorithms.However,when the target’s rotational velocity is sufficiently high during the dwell time of the radar,such compensation algorithms cannot obtain a high quality image.This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm.The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion,and the deep learning algorithm is used to achieve a super-resolution image.The uniformly distributed point target data are used as the data set of the training u-net network.In addition,this method does not require estimating the motion parameters of the target,which simplifies the algorithm steps.Finally,several experiments are performed to demonstrate the effectiveness of the proposed algorithm.展开更多
To correct the range walk through resolution cell in Doppler beam sharpening （DBS） imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters an...To correct the range walk through resolution cell in Doppler beam sharpening （DBS） imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters and the look angle of the radar platform in the multi-targets environment, a linear trans- form on the received data is employed to correct different range walk values accurately under the condition of Doppler frequency ambiguity in this algorithm. This method can realize the cohe- rent integration in azimuth dimension and improve the azimuth resolution. In order to reduce the computational burden, a fast implementation of Keystone transform is used. Theoretical anal- ysis and simulation results demonstrate the effectiveness of the new algorithm. And through comparing the computational load of the fast implementation with several other algorithms, the real-time processing ability of the proposed algorithm is superior to that of other algorithms.展开更多
Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous...Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter back-ground,and at the same time improving the calculation efficiency has become an urgent problem to be solved.The sparse transformation theory is introduced to LTCI in this paper,and a non-parametric searching sparse LTCI(SLTCI)based maneuvering target detection method is proposed.This method performs time reversal(TR)and second-order Keystone transform(SKT)in the range frequency&slow-time data to complete high-order range walk compensation,and achieves the coherent integra-tion of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform(RSFRFT).It can compensate for the nonlinear range migration caused by high-order motion.S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method,which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.展开更多
基金This work was supported by the National Natural Science Foundation of China(61571388,61871465,62071414)the Project of Introducing Overseas Students in Hebei Province(C20200367).
文摘The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR)imaging has been successfully addressed by popular motion compensation algorithms.However,when the target’s rotational velocity is sufficiently high during the dwell time of the radar,such compensation algorithms cannot obtain a high quality image.This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm.The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion,and the deep learning algorithm is used to achieve a super-resolution image.The uniformly distributed point target data are used as the data set of the training u-net network.In addition,this method does not require estimating the motion parameters of the target,which simplifies the algorithm steps.Finally,several experiments are performed to demonstrate the effectiveness of the proposed algorithm.
基金supported by the Basic Research of the National Department of Defense (A2220060054)the Foundation of Shanghai Aerospace Science and Technology
文摘To correct the range walk through resolution cell in Doppler beam sharpening （DBS） imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters and the look angle of the radar platform in the multi-targets environment, a linear trans- form on the received data is employed to correct different range walk values accurately under the condition of Doppler frequency ambiguity in this algorithm. This method can realize the cohe- rent integration in azimuth dimension and improve the azimuth resolution. In order to reduce the computational burden, a fast implementation of Keystone transform is used. Theoretical anal- ysis and simulation results demonstrate the effectiveness of the new algorithm. And through comparing the computational load of the fast implementation with several other algorithms, the real-time processing ability of the proposed algorithm is superior to that of other algorithms.
基金supported by the National Natural Science Foundation of China(62222120,61871391,U1933135)Shandong Provincial Natural Science Foundation(ZR2021YQ43).
文摘Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter back-ground,and at the same time improving the calculation efficiency has become an urgent problem to be solved.The sparse transformation theory is introduced to LTCI in this paper,and a non-parametric searching sparse LTCI(SLTCI)based maneuvering target detection method is proposed.This method performs time reversal(TR)and second-order Keystone transform(SKT)in the range frequency&slow-time data to complete high-order range walk compensation,and achieves the coherent integra-tion of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform(RSFRFT).It can compensate for the nonlinear range migration caused by high-order motion.S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method,which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.
