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.
This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.
固定角度旋转的CORDIC(Coordinate Rotation Digital Computer)算法已经广泛的应用于高速数字信号处理、图像处理、机器人学等领域.针对固定角度旋转CORDIC算法在相位旋转过程中,存在数据吞吐率较高、占用硬件资源较多且资源消耗量大等缺点,提出了利用混合CORDIC算法,将角度旋转分为单向角度旋转和一次角度估计旋转两部分.本文根据欠阻尼理论,将固定角度旋转采用单向旋转CORDIC算法实现,减少了流水线的级数和迭代符号位的判决,然后通过对角度估计旋转的二进制表示,修正常数因子,再根据角度映射关系进行相关处理,完成高速高精度坐标旋转.最后在硬件平台上进行了仿真实验.实验结果表明,在误差范围一定的前提下,混合算法进一步的减少了迭代次数,并且资源消耗较低,提高了数据吞吐率.
