Signal Reconstruction And Analysis Based On Compressed Sampling

In the battlefield environment with abundant information elements,real-time and accurate electronic reconnaissance is the key factor of seizing electromagnetic power,that is to say,the receiver needs to monitor the signals of multiple non cooperative radiation sources in the receiving frequency band in real time without prior information to obtain key information such as the modulation of the signal.The requirement of mobile communication is to achieve large bandwidth and high-speed rate,in other words,most of the wireless communication processes are complex environments with large bandwidth,multi band and multiple modulation categories.In this dissertation,we study the reconstruction of wideband modulation signal in low Signal to noise ratio(SNR)environment,and complete the parameter estimation and modulation recognition based on the reconstructed signal.Communication receiver should achieve real-time monitoring of the large bandwidth spectrum(such as 2-18GHz),so the traditional signal receiving scheme based on the Nyquist sampling theorem collect a lot of data,bringing inconvenience to storage and data processing.In order to solve this problem,we can consider the method based on the compressed sensing theory,that is to say,we can use the frequency sparsity of the signal to transform the traditional receiving problem into the sparse reconstruction problem while the traditional sparse reconstruction algorithm has high computational complexity and poor performance in low SNR reconstruction,which makes it a worthy research topic to effectively reconstruct the signal in low SNR environment.With the purpose of resolve the high computational complexity of sparse reconstruction algorithm and improve the accuracy of signal reconstruction,we first detect the support set of the signal in frequency domain,transform the signal reconstruction problem into the traditional parameter estimation problem,use the least square estimation to obtain the value of the signal in the frequency support set,then recover the original signal and realize the separation of the sub signals,and finally accomplish parameter estimation and modulation recognition for each sub signal.First,introduce the spectrum sensing of wideband signal in low signal to noise ratio environment.We add on a fast power spectrum reconstruction algorithm based on Multicoset compression sampling framework,which is suitable for wideband signal spectrum detection.Discuss the power spectrum distribution function of noise,and then estimate the noise power.Based on the reconstructed power spectrum and the estimated noise power,complete the signal frequency domain detection to obtain the support set information of the original signal.And then,the support set information of each sub signal is obtained by using the frequency band sparsity of the sub signal in the frequency domain.Next,introduce the signal reconstruction problem based on frequency support set.Based on the support set information,we can transform the compression sensing problem into the problem of signal parameter estimation.Recover the original signal by using the least square estimation,and discuss the Cramer Rao low bound.In addition,separate the sub signal by using the support set information.Finally,introduce the carrier frequency and bit rate estimation and modulation recognition problem of the reconstructed signal.Discuss several classical algorithms of carrier frequency and bit rate estimation,and verify their performance of parameter estimation under different signal to noise ratio,select the time-domain parameters to realize the signal modulation recognition.