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Principle and key technology of generalized high precision simulation of TT&C channel
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作者 Yang Zhou Zhe Zheng Siliang Wu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第2期318-323,共6页
A generalized simulation method of the tracking,telemetry and control(TT&C) channel,which is applicable to wideband and arbitrary radio frequency(RF) signal,is proposed.It can accurately simulate the dynamic tran... A generalized simulation method of the tracking,telemetry and control(TT&C) channel,which is applicable to wideband and arbitrary radio frequency(RF) signal,is proposed.It can accurately simulate the dynamic transmission delay of the arbitrary RF signal in channels,especially regardless of any prior knowledge including signal form,signal parameters,and so on.The proposed method orthogonally demodulates the wideband and arbitrary RF signal to complex baseband by a known local oscillator(LO) signal.Whereafter,it takes measures to obtain the delay reconstruction signal of baseband signals based on the dynamic transmission delay between a ground station and a responder.Meanwhile,it manages to obtain the delay reconstruction signal of LO signals.The simulation output signal(the delayed RF signal) can be achieved through the synthesis of the two delay reconstruction signals mentioned above.The principle and its related key technology are described in detail,and the realizable system architecture is given. 展开更多
关键词 GENERALIZATION channel simulation high precision dynamic delay reconstruction dynamic delay cancellation
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BINARY LEAST SQUARES:AN ALGORITHM FOR BINARY SPARSE SIGNAL RECOVERY
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作者 Jinming Wen 《Journal of Computational Mathematics》 2025年第2期493-514,共22页
A fundamental problem in some applications including group testing and communications is to acquire the support of a K-sparse signal x,whose nonzero elements are 1,from an underdetermined noisy linear model.This paper... A fundamental problem in some applications including group testing and communications is to acquire the support of a K-sparse signal x,whose nonzero elements are 1,from an underdetermined noisy linear model.This paper first designs an algorithm called binary least squares(BLS)to reconstruct x and analyzes its complexity.Then,we establish two sufficient conditions for the exact reconstruction of x’s support with K iterations of BLS based on the mutual coherence and restricted isometry property of the measurement matrix,respectively.Finally,extensive numerical tests are performed to compare the efficiency and effectiveness of BLS with those of batch orthogonal matching pursuit(BatchOMP)which to our best knowledge is the fastest implementation of OMP,orthogonal least squares(OLS),compressive sampling matching pursuit(CoSaMP),hard thresholding pursuit(HTP),Newton-step-based iterative hard thresholding(NSIHT),Newton-step-based hard thresholding pursuit(NSHTP),binary matching pursuit(BMP)andΙ_(1)-regularized least squares.Test results show that:(1)BLS can be 10-200 times more efficient than Batch-OMP,OLS,CoSaMP,HTP,NSIHT and NSHTP with higher probability of support reconstruction,and the improvement can be 20%-80%;(2)BLS has more than 25%improvement on the support reconstruction probability than the explicit BMP algorithm with a little higher computational complexity;(3)BLS is around 100 times faster thanΙ_(1)regularized least squares with lower support reconstruction probability for small K and higher support reconstruction probability for large K.Numerical tests on the generalized space shift keying(GSSK)detection indicate that although BLS is a little slower than BMP,it is more efficient than the other seven tested sparse recovery algorithms,and although it is less effective thanΙ_(1)-regularized least squares,it is more effective than the other seven algorithms. 展开更多
关键词 Binary sparse signal Precise support reconstruction Binary least squares
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