Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presen...Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.展开更多
Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms us...Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.展开更多
Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performan...Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms.However,SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios.In the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique.Furthermore,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP technique.Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques.Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP technique.In addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques.Moreover,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.展开更多
Based on the stable multipath structure characteristics of the under-ice acoustic channel,a scheme utilizing the preamble signal of the communication frame for channel estimation is adopted.Leveraging the sparse chara...Based on the stable multipath structure characteristics of the under-ice acoustic channel,a scheme utilizing the preamble signal of the communication frame for channel estimation is adopted.Leveraging the sparse characteristics of the under-ice acoustic channel,an adaptive sparsity orthogonal matching pursuit(ASOMP)algorithm is proposed to address the reliance of the orthogonal matching pursuit(OMP)algorithm on prior knowledge of sparsity.Firstly,the sparsity of the channel is coarsely estimated using the autocorrelation characteristics of the preamble signal,and the simplified dictionary matrix is constructed.Subsequently,based on the coarsely estimated sparsity and the simplified dictionary matrix,the OMP algorithm is employed to obtain a preliminary estimation of the channel impulse response(CIR).Finally,fine estimations of both the sparsity and CIR are performed using residual differences derived from the coarsely estimated CIR.The adopted channel estimation scheme eliminates the need to insert training sequences into the data segment,thereby enhancing the data rate.The proposed channel estimation method enables effective channel estimation without requiring prior knowledge of channel sparsity.Simulation results show that the ASOMP algorithm achieves performance comparable to the OMP algorithm with known sparsity and outperforms the existing sparsity adaptive matching pursuit(SAMP)algorithm under low signal-to-noise ratio conditions.The data processing results from the 14th Chinese National Arctic Research Expedition’s under-ice experiment validate the effectiveness and reliability of the proposed algorithm.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61073079)the Fundamental Research Funds for the Central Universities (2011JBM216,2011YJS021)
文摘Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.
基金supported by the National Natural Science Foundation of China(61773202,71874081)the Special Financial Grant from China Postdoctoral Science Foundation(2017T100366)+2 种基金the Key Laboratory of Avionics System Integrated Technology for National Defense Science and Technology,China Institute of Avionics Radio Electronics(6142505180407)the Open Fund of CAAC Key laboratory of General Aviation Operation,Civil Aviation Management Institute of China(CAMICKFJJ-2019-04)the Innovation Project of the Civil Aviation Administration of China(EAB19001)。
文摘Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.
文摘Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms.However,SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios.In the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique.Furthermore,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP technique.Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques.Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP technique.In addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques.Moreover,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.
基金supported by the National Key Research and Development Program of China(2021YFC 2801201)the National Natural Science Foundation of China(62271442,42227901)+1 种基金the Natural Science Foundation of Zhejiang Province(LZ23F010006)the Zhejiang Provincial Department of Science and Technology(LGG22F010007).
文摘Based on the stable multipath structure characteristics of the under-ice acoustic channel,a scheme utilizing the preamble signal of the communication frame for channel estimation is adopted.Leveraging the sparse characteristics of the under-ice acoustic channel,an adaptive sparsity orthogonal matching pursuit(ASOMP)algorithm is proposed to address the reliance of the orthogonal matching pursuit(OMP)algorithm on prior knowledge of sparsity.Firstly,the sparsity of the channel is coarsely estimated using the autocorrelation characteristics of the preamble signal,and the simplified dictionary matrix is constructed.Subsequently,based on the coarsely estimated sparsity and the simplified dictionary matrix,the OMP algorithm is employed to obtain a preliminary estimation of the channel impulse response(CIR).Finally,fine estimations of both the sparsity and CIR are performed using residual differences derived from the coarsely estimated CIR.The adopted channel estimation scheme eliminates the need to insert training sequences into the data segment,thereby enhancing the data rate.The proposed channel estimation method enables effective channel estimation without requiring prior knowledge of channel sparsity.Simulation results show that the ASOMP algorithm achieves performance comparable to the OMP algorithm with known sparsity and outperforms the existing sparsity adaptive matching pursuit(SAMP)algorithm under low signal-to-noise ratio conditions.The data processing results from the 14th Chinese National Arctic Research Expedition’s under-ice experiment validate the effectiveness and reliability of the proposed algorithm.
