Multi-channels wideband digital reconnaissance receiver based on compressed sensing

作者：Yu Nan^*; Qi Xiao hui; Qiao Xiao lin

来源：IET Signal Processing, 2013, 7(8): 731-742.

DOI：10.1049/iet-spr.2012.0086

• low-pass filters
• military radar
• military systems
• signal reconstruction
• signal sampling
• compressed sensing
• multichannel wideband digital reconnaissance receiver
• compressed sensing method
• multiband RF signal sampling
• multiband RF signal reconstruction
• Bernoulli random signal
• low-pass filter
• multiple-narrow-band signal recovery
• broadband range
• reconstruction algorithm
• ACGPMMV
• OMPMMV
• channel number reduction
• hardware cost reduction
• sampling chip analogue bandwidth

In order to meet the bandwidth need of modern wideband digital reconnaissance receiver, an implementation based on compressed sensing is proposed. The compressed sensing method is directly used to sample and reconstruct multi-band RF signal in this receiver. The original signal is mixed with Bernoulli random signal, and then filtered by low-pass filter. As completing the multiple narrow-band signals recovery within broadband range, the sampling signal is reconstructed in the digital domain. In this study, a novel reconstruction algorithm is proposed, which is adaptive conjugate gradient pursuit multiple measurement vectors (ACGPMMV), to overcome the drawback of orthogonal matching pursuit multiple measurement vectors (OMPMMV). Meanwhile, how to reduce the channel number of system required is also analysed, which will be reduce hardware costs. The shortcoming of limitation of sampling chip's analogue bandwidth in the parallel alternating sampling system is overcame, and the requirement of receiver bandwidth is achieved by using the lower rate sampling. In this study, the study is carried out by means of numerical simulations of multi-signals in the different system model. The conclusions illuminate the algorithm can get well-signal reconstruction results under the conditions of less channel and low sampling rate, and well-noise stability.

• 出版日期2013-10
• 单位哈尔滨工业大学