Abstract：Evaporation duct helps in the over-the-horizon operating of the communication,radar systems,etc.at the microwave frequency band.In addition,it causes abnormal blind areas,too.Therefore,the evaporation duct situation acquisition is the key to seize the mastery of the electromagnetic.However,if the density of the sensor is increased to improve the sensing resolution,the cost is high and the improvement is limited.Compressed sensing (CS) provides the theoretical basis for the awareness of evaporation duct,which is recovered from a small number of low speed measurements.The blind adaptive Karhunen-Loéve transform (BAKLT) pursuit is able to fully exploit the sparsity and reconstruct the time and space situation of the evaporation duct.The analysis and simulation demonstrate that the BAKLT evaporation duct situational awareness accuracy is better than that of the control group using discrete cosine transform.The reconstructed result of the proposed method is able to reach the reconstructed SNR level of 30dB saving 90% of the sampling resources,and provides the compression basis for the full time global evaporation duct situation acquisition.
[1] Zhang P,Bai L,Wu Z,et al.Applying the parabolic equation to tropospheric groundwave propagation:a review of recent achievements and significant milestones[J].IEEE Antennas and Propagation Magazine,2016,58(3):31-44.
[2] Yang K,Zhang Q,Shi Y,et al.On analyzing space-time distribution of evaporation duct height over the global ocean[J].Acta Oceanologica Sinica,2016,35(7):20-29.
[3] Candès E J.The restricted isometry property and its implications for compressed sensing[J].Compte Rendus-Ma-thématique,2008,Series I(346):589-592.
[4] Baraniuk R,Davenport M,DeVore R,et al.A simple proof of the restricted isometry property for random matrices[J].Constructive Approximation,2008,28(3):253-263.
[5] 田文飚,芮国胜,王绍班,等.三维观测模型下蒸发波导时空态势压缩感知[J].电波科学学报,2014,29(2):207-212. Tian W,Rui G,Wang S,et al.Situational compressed sensing of evaporation duct based on 3-dimensional measurements model[J].China Journal of Radio Science,2014,29(2):207-212.
[6] Chung J,Kim Y,Yang J.3-D probe:low-cost variation modeling using intertest-item correlations[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2014,33(12):2005-2009.
[7] Liu Y,Li M,Pados D A.Motion-aware decoding of compressed-sensed video[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(3):438-444.
[8] Davila C E.Blind adaptive estimation of KLT basis vectors[J].IEEE Transactions on Signal Processing,2001,49(7):1364-1369.
[9] Ai X,Luo Y,Zhao G.Transient interference excision in over-the-horizon radar by robust principal component analysis with a structured matrix[J].IEEE Geoscience and Remote Sensing Letters,2016,13(1):48-52.
[10] Pad P,Unser M.Optimality of operator-like wavelets for representing sparse AR(1) processes[J].IEEE Transactions on Signal Processing,2015,63(18):4827-4837.
[11] Yilmaz O,Akansu A N.Quantization of eigen subspace for sparse representation[J].IEEE Transactions on Signal Processing,2015,63(14):3576-3585.
[12] Sezer O G,Guleryuz O G,Altunbasak Y.Approximation and compression with sparse orthonormal transforms[J].IEEE Transactions on Image Processing,2015,24(8):2328-2343.
[13] Jokar S,Mehrmann V.Sparse solutions to underdetermined Kronecker product systems[J].Linear Algebra & Its Applications,2009,431(12):2437-2447.
2018, Vol. 46 
