Research on Fast Reconstruction of Frequency Hopping Using Compressive Sampling
Abstract: Limited by the Nyquist sampling theorem, for traditional frequency hopping signals acquisition which needs very high sampling rate and high processing cost, compression sampling theory breaks through the limitation of the Nyquist sampling theorem, and the sampling rate can be significantly reduced. The paper proposes an iteration that takes three continuous atomic bases, which have got the nearest hopping point, as the sparse representation block of this hopping point. This algorithm weakens instantaneous frequency bandwidth caused by the adjacent signal frequency mutation and the modulated data symbol brings frequency deviation effect, and makes it more suitable for the actual frequency hopping signal. The simulation results verify the correction of the effectiveness of the sparse degree of adaptive matching pursuit algorithm, which improves the performance of reconstruction algorithm. Moreover, the paper compares the correction algorithm’s reconstruction probability with the original algorithm under different M value.
文章引用: 赵毅智 , 张洪峰 , 钱 建 (2015) 跳频压缩采样的快速重构研究。 无线通信， 5， 16-20. doi: 10.12677/HJWC.2015.51003
 Donoho, D.L. (2006) Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306.
 吴俊, 刘乃安, 沈常林, 张妍飞 (2013) 一种压缩域下的跳频信号盲识别新方法. 西安电子科技大学学报, 6, 1-5.
 陈宇科, 汪立新, 吴剑锋 (2011) 压缩采样中模拟信息转换器的性能仿真. 电子器件, 1, 81-84.
 Donoho, D.L. and Tsaig, Y. (2006) Extensions of compressed sensing. IEEE Signal Processing Magazine, 86, 533- 548.
 Romberg, J. (2008) Imaging via compressive sampling. IEEE Signal Processing Magazine, 25, 14-20.
 Baraniuk, R. (2007) Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 24, 118-121.
 Baraniuk, R. (2008) Compressive sensing. 42nd Annual Conference on Information Sciences and Sys-tems, Princeton, 19-21 March 2008, iv-v.
 金坚, 谷源涛, 梅顺良 (2010) 压缩采样技术及其应用. 电子与信息学报, 2, 470-475.
 Do, T.T., Gan, L., Nguyen, N., et al. (2008) Sparsity adaptive matching pursuit algorithm for practical compressed sensing. 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 26-29 October 2008, 581-587.
 朱延万, 赵拥军, 孙兵 (2012) 一种改进的稀疏度自适应匹配追踪算法. 信号处理, 1, 80-86.