Multi-narrowband signals receiving method based on analog-to-information convertor and block sparsity  

作　　者：Hongyi Xu Haiqing Jiang Chaozhu Zhang 

机构地区：[1]School of Information and Communication Engineering, Harbin Engineering University [2]School of Information and Electronic Engineering, Beijing Institute of Technology

出　　处：《Journal of Systems Engineering and Electronics》2017年第4期643-653,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61172159)

摘　　要：The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model based on AIC and block sparsity. To overcome the practical problems, the block sparsity is divided into uniform block and non-uniform block situations, and the block restricted isometry property and sub-sampling limit in different situations are analyzed respectively in detail. Theoretical analysis proves that using the block sparsity in AIC can reduce the restricted isometric constant, increase the reconstruction probability and reduce the sub -sampling rate. Simulation results show that the proposed model can complete sub -sampling and reconstruction for multi-narrowband signals. This paper extends the application range of AIC from the finite information rate signal to the multi-narrowband signals by using the potential relevance of support sets. The proposed receiving model has low complexity and is easy to implement, which can promote the application of CS theory in the radar receiver to reduce the burden of analog-to digital convertor (ADC) and solve bandwidth limitations of ADC.The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model based on AIC and block sparsity. To overcome the practical problems, the block sparsity is divided into uniform block and non-uniform block situations, and the block restricted isometry property and sub-sampling limit in different situations are analyzed respectively in detail. Theoretical analysis proves that using the block sparsity in AIC can reduce the restricted isometric constant, increase the reconstruction probability and reduce the sub -sampling rate. Simulation results show that the proposed model can complete sub -sampling and reconstruction for multi-narrowband signals. This paper extends the application range of AIC from the finite information rate signal to the multi-narrowband signals by using the potential relevance of support sets. The proposed receiving model has low complexity and is easy to implement, which can promote the application of CS theory in the radar receiver to reduce the burden of analog-to digital convertor (ADC) and solve bandwidth limitations of ADC.

关 键 词：compressive sensing (CS) block sparsity analog-to-information convertor (AIC) multi-narrowband signals 

分 类 号：TN911.7[电子电信—通信与信息系统]