With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direc...With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.展开更多
this paper,we introduce the L_(p) Shephard problem on entropy of log-concave functions,a comparison problem:whether ∏_(p)f≤∏_(p)g implies that Ent(f)≥Ent(g),for 1≤p<n,and Ent(f)≤Ent(g),for n<p,where ∏_(p)...this paper,we introduce the L_(p) Shephard problem on entropy of log-concave functions,a comparison problem:whether ∏_(p)f≤∏_(p)g implies that Ent(f)≥Ent(g),for 1≤p<n,and Ent(f)≤Ent(g),for n<p,where ∏_(p)f is the L_(p) projection body of a log-concave function f.Our results give a partial answer to this problem.展开更多
For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better den...For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better denoising effect on the pulse noise, it is chosen as the model fidelity term, and the overlapping group sparse term combined with non-convex higher term is used as the regularization term of the model to protect the image edge texture and suppress the staircase effect. At the same time, the alternating direction method of multipliers, the majorization–minimization method and the mathematical program with equilibrium constraints were used to solve the model. Experimental results show that the proposed model can effectively suppress the staircase effect in smooth regions, protect the image edge details, and perform better in terms of the peak signal-to-noise ratio and the structural similarity index measure.展开更多
In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency est...In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance.展开更多
稀疏恢复(Sparse Recovery,SR)空时自适应信号处理(Space Time Adaptive Processing,STAP)仅需要少量的杂波样本即可有效抑制杂波,但是稀疏恢复空时自适应信号处理依赖于空时字典,当载机运动方向与天线放置方向存在偏航角时,杂波脊偏离...稀疏恢复(Sparse Recovery,SR)空时自适应信号处理(Space Time Adaptive Processing,STAP)仅需要少量的杂波样本即可有效抑制杂波,但是稀疏恢复空时自适应信号处理依赖于空时字典,当载机运动方向与天线放置方向存在偏航角时,杂波脊偏离空时字典格点,出现离格问题,从而导致杂波抑制性能下降。已有的基于l_(1)范数类的离格稀疏恢复算法在存在噪声时性能下降,没有充分利用杂波的稀疏性,文章提出一种基于l_(p)(0<p<1)范数的离格空时自适应处理算法,首先将建立基于空时字典更新的稀疏恢复空时自适应模型,然后将该模型松弛为l_(p)(0<p<1)范数的非凸优化问题,最后利用主函数最大化算法将该优化问题转化成凸优化问题,利用两层迭代求解的方法得到该问题的解,最后利用模型的解估计杂波协方差矩阵。通过仿真实验表明,提出的算法能够提高存在离格问题时的杂波恢复精度,抑制杂波的性能也优于已有的基于变分推断的算法。展开更多
In this paper,we prove the uniqueness of the Lp Minkowski problem for q-torsional rigidity with p>1 and q>1 in smooth case.Meanwhile,the Lp Brunn-Minkowski inequality and the Lp Hadamard variational formula for ...In this paper,we prove the uniqueness of the Lp Minkowski problem for q-torsional rigidity with p>1 and q>1 in smooth case.Meanwhile,the Lp Brunn-Minkowski inequality and the Lp Hadamard variational formula for q-torsional rigidity are established.展开更多
For 1<p<∞,let S(Lp)+be the set of positive elements in L_(p) with norm one.Assume that V_(0):S(L_(p)(Ω_(1)))+→S(L_(p)(Ω_(2)))+is a surjective norm-additive map;that is,‖V_(0)(x)+V_(0)(y)‖=‖x+y‖,■x,y∈S(...For 1<p<∞,let S(Lp)+be the set of positive elements in L_(p) with norm one.Assume that V_(0):S(L_(p)(Ω_(1)))+→S(L_(p)(Ω_(2)))+is a surjective norm-additive map;that is,‖V_(0)(x)+V_(0)(y)‖=‖x+y‖,■x,y∈S(L_(p)(Ω_(1)))+.In this paper,we show that V_(0) can be extended to an isometry from L_(p)(Ω_(1))onto L_(p)(Ω_(2)).展开更多
Let M be a semifinite von Neumann algebra.We equip the associated noncommutative Lp-spaces with their natural operator space structure introduced by Pisier via complex interpolation.On the other hand,for L_(p),p(M)=(...Let M be a semifinite von Neumann algebra.We equip the associated noncommutative Lp-spaces with their natural operator space structure introduced by Pisier via complex interpolation.On the other hand,for L_(p),p(M)=(L_(∞)(M),L_(1)(M)_(1/p,p)be equipped with the operator space structure via real interpolation as defined by the second named author(J.Funct.Anal.139(1996),500–539).We show that Lp,p(M)=Lp(M)completely isomorphically if and only if M is finite dimensional.This solves in the negative the three problems left open in the quoted work of the second author.We also show that for 1<p<∞and 1≤q≤∞with p 6=q,(L_(∞)(M;l_(q)),L_(1)(M;l_(q)_(1/p,p)=L_(p)(M;l_(q)with equivalent norms,i.e.,at the Banach space level if and only if M is isomorphic,as a Banach space,to a commutative von Neumann algebra.Our third result concerns the following inequality:||(∑iixtq)^(1/q)||lp(M)≤||(∑iixit)^(1/q)||lp(M),for any finite sequence(xi)⊂L+p(M),where 0<r<q<∞and 0<p≤∞.If M is not isomorphic,as a Banach space,to a commutative von Meumann algebra,then this inequality holds if and only if p≥r.展开更多
基金supported by the National Basic Research Program of China。
文摘With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
基金The National Natural Science Foundation of China(11701373)The Shanghai Sailing Program(17YF1413800)。
文摘this paper,we introduce the L_(p) Shephard problem on entropy of log-concave functions,a comparison problem:whether ∏_(p)f≤∏_(p)g implies that Ent(f)≥Ent(g),for 1≤p<n,and Ent(f)≤Ent(g),for n<p,where ∏_(p)f is the L_(p) projection body of a log-concave function f.Our results give a partial answer to this problem.
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better denoising effect on the pulse noise, it is chosen as the model fidelity term, and the overlapping group sparse term combined with non-convex higher term is used as the regularization term of the model to protect the image edge texture and suppress the staircase effect. At the same time, the alternating direction method of multipliers, the majorization–minimization method and the mathematical program with equilibrium constraints were used to solve the model. Experimental results show that the proposed model can effectively suppress the staircase effect in smooth regions, protect the image edge details, and perform better in terms of the peak signal-to-noise ratio and the structural similarity index measure.
文摘In this work,we address the frequency estimation problem of a complex single-tone embedded in the heavy-tailed noise.With the use of the linear prediction(LP)property and l_(1)-norm minimization,a robust frequency estimator is developed.Since the proposed method employs the weighted l_(1)-norm on the LP errors,it can be regarded as an extension of the l_(1)-generalized weighted linear predictor.Computer simulations are conducted in the environment of α-stable noise,indicating the superiority of the proposed algorithm,in terms of its robust to outliers and nearly optimal estimation performance.
文摘稀疏恢复(Sparse Recovery,SR)空时自适应信号处理(Space Time Adaptive Processing,STAP)仅需要少量的杂波样本即可有效抑制杂波,但是稀疏恢复空时自适应信号处理依赖于空时字典,当载机运动方向与天线放置方向存在偏航角时,杂波脊偏离空时字典格点,出现离格问题,从而导致杂波抑制性能下降。已有的基于l_(1)范数类的离格稀疏恢复算法在存在噪声时性能下降,没有充分利用杂波的稀疏性,文章提出一种基于l_(p)(0<p<1)范数的离格空时自适应处理算法,首先将建立基于空时字典更新的稀疏恢复空时自适应模型,然后将该模型松弛为l_(p)(0<p<1)范数的非凸优化问题,最后利用主函数最大化算法将该优化问题转化成凸优化问题,利用两层迭代求解的方法得到该问题的解,最后利用模型的解估计杂波协方差矩阵。通过仿真实验表明,提出的算法能够提高存在离格问题时的杂波恢复精度,抑制杂波的性能也优于已有的基于变分推断的算法。
基金The authors were supported by NSFC(11771132)Hunan Science and Technology Project(2018JJ1004).
文摘In this paper,we prove the uniqueness of the Lp Minkowski problem for q-torsional rigidity with p>1 and q>1 in smooth case.Meanwhile,the Lp Brunn-Minkowski inequality and the Lp Hadamard variational formula for q-torsional rigidity are established.
基金partially supported by the NSF of China(11671314)partially supported by the NSF of China(12171251)。
文摘For 1<p<∞,let S(Lp)+be the set of positive elements in L_(p) with norm one.Assume that V_(0):S(L_(p)(Ω_(1)))+→S(L_(p)(Ω_(2)))+is a surjective norm-additive map;that is,‖V_(0)(x)+V_(0)(y)‖=‖x+y‖,■x,y∈S(L_(p)(Ω_(1)))+.In this paper,we show that V_(0) can be extended to an isometry from L_(p)(Ω_(1))onto L_(p)(Ω_(2)).
基金the French ANR project(ANR-19-CE40-0002)the Natural Science Foundation of China(12031004).
文摘Let M be a semifinite von Neumann algebra.We equip the associated noncommutative Lp-spaces with their natural operator space structure introduced by Pisier via complex interpolation.On the other hand,for L_(p),p(M)=(L_(∞)(M),L_(1)(M)_(1/p,p)be equipped with the operator space structure via real interpolation as defined by the second named author(J.Funct.Anal.139(1996),500–539).We show that Lp,p(M)=Lp(M)completely isomorphically if and only if M is finite dimensional.This solves in the negative the three problems left open in the quoted work of the second author.We also show that for 1<p<∞and 1≤q≤∞with p 6=q,(L_(∞)(M;l_(q)),L_(1)(M;l_(q)_(1/p,p)=L_(p)(M;l_(q)with equivalent norms,i.e.,at the Banach space level if and only if M is isomorphic,as a Banach space,to a commutative von Neumann algebra.Our third result concerns the following inequality:||(∑iixtq)^(1/q)||lp(M)≤||(∑iixit)^(1/q)||lp(M),for any finite sequence(xi)⊂L+p(M),where 0<r<q<∞and 0<p≤∞.If M is not isomorphic,as a Banach space,to a commutative von Meumann algebra,then this inequality holds if and only if p≥r.
