| Radar is widely used in military and civilian fields because it can work all day and all weather,and it has strong penetrability and can perform long-distance imaging.In recent years,radar imaging technology has experienced a development stage from low-resolution imaging to high-resolution imaging,from monostatic imaging to multi-static and distributed imaging,and from a single working mode to a multi-functional collaborative mode.Due to the high requirements of users on the resolution of radar images and the joint processing of multi-static and distributed radar echo data,modern radars are usually faced with the problem of massive data acquisition,transmission and storage.At the same time,in the actual working process of the radar,the echo data missing phenomenon often occurs due to the influence of the external complex electromagnetic environment or the radar system itself.Therefore,how to use a small amount of effective data for high-resolution radar imaging is a major research hotspot at present.Based on inverse synthetic aperture radar(ISAR)imaging technology and matrix completion(MC)theory,this paper studies the problems of high resolution ISAR imaging under the condition of sparse aperture and high resolution distributed ISAR imaging with the observation angle interval,and has achieved some research results.The specific work content of this paper is principally generalized as the following aspects:1.Introduce the basis of the full text work—ISAR imaging technology and matrix completion theory.Firstly,the related theories of ISAR imaging is described,including the turntable model often used in ISAR,the traditional range doppler(RD)imaging algorithm,and the two-dimensional resolution of ISAR system is analyzed.At the same time,the inevitable translation compensation in ISAR imaging is introduced,including two steps of envelope alignment and phase compensation,and the introduced methods of envelope alignment and phase compensation are verified by the measured data of Yake-42 aircraft.Finally,the model and algorithm of matrix completion theory are given,and numerical simulation and image simulation experiments are carried out.The experimental results show the effectiveness of the introduced matrix completion solution algorithm.2.Aiming at the problem that the traditional monostatic ISAR echoes are missing the entire row and column or continuously missing,and the traditional matrix completion method cannot fill in the missing data,an ISAR sparse imaging method based on enhanced matrix completion(EMa C)is proposed.By constructing the echo matrix into its enhanced form,the proposed method can not only eliminate all zero rows and all zero columns in the matrix,but also enhance the low rank of the matrix.After filling up the missing data in the enhanced echo matrix,it is then transformed into a reconstructed echo matrix with the same dimension as the original echo matrix,and finally the RD algorithm is used for imaging.The experimental results of simulation and measured data show that the proposed method can effectively fill in the missing echo data of the entire row and column under the conditions of low sparse sampling rate and low signal-to-noise ratio(SNR),and obtain high-resolution ISAR images,which has certain engineering application value.3.Aiming at the problems of degraded imaging quality and high side-lobe of distributed ISAR when there is interval between observation angles,a new matrix completion model—joint low rank and sparsity(JLRS)model is introduced,and a distributed ISAR sparse imaging method based on JLRS model is proposed.With the help of alternating direction method of multiplies(ADMM)framework,the proposed method combines the characteristics of low rank and sparsity,and solves the optimization sub-problems of each variable respectively,with low computational complexity and high solving speed.Finally,the effectiveness of the proposed method in side-lobe reduction and noise suppression is verified by several simulation experiments at different angles. |
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