Research On Key Technologies Of Photonic Compressive Sensing Based On Pulse Dispersion Processing

Due to the information redundancy existed in signals,if a signal is sampled according to the Nyquist sampling rate,it will inevitably waste the sampling bandwidth and increase the cost of signal acquisition and processing.Assuming that a signal is sparse,according to the principle of compressive sensing(CS),it can be compressed through a measurement process.During the process,the signal sampling rate can be much lower than its Nyquist rate.Considering the ever-increasing carrier frequency in communications systems,it is highly desirable to employ CS in signal processing and other areas.In the past decade,scholars have combined the advantages of large bandwidth of photonics and low sampling rate of CS,and proposed several photonic CS schemes.Based on photonic time stretch,wavelength-to-time mapping,microwave photonic filtering and other photonics methods,photonic CS realizes the mixing process and the integration process in the optical domain which promotes the development of CS and provides more options for signal acquisition.Based on the technique of pulse dispersion processing,this thesis investigates the CS schemes for the acquisition of spectrally sparse signals.We proposes two new photonic CS schemes.The first scheme can realize the mixing of bipolar pseudo-random sequences and input signals.The second scheme can realize adjustable compression ratio.With theoretical analysis and experiment results,the effectiveness of the given two schemes is verified.The main research contents and results of this thesis are as follows.1.According to the principle of wavelength-to-time mapping,a novel photonic CS scheme based on bipolar coding is proposed.It can realize the mixing between the bipolar pseudo-random binary sequence(PRBS)and the input signal in the optical domain,which improves the performance of the CS system.In the scheme,an optical pulse is broadened and get chirped firstly after propagating through a dispersive fiber.The chirped pulse is modulated by the input signal in the following electrooptic modulator.The modulated pulses are incident into the spectral shaper with dual outputs.In the spectral shaper,the pulse spectrum is carved by two complementary PRBSs,each for one output.According to the property of wavelength-to-time mapping,the complementary PRBSs in the frequency domain are mapped to the pulse waveform in the time domain and mixed with the input signal respectively.With the balanced detector at the outputs of the spectral shaper,the mixing signal between the bipolar PRBS and the input signal can be achieved.Finally,after the low-pass filtering and down-sampling of the mixing signal,the measurement results of the input signal can be used to recover the input signal by using a sparse recovery algorithm.2.Based on the time compression and the wavelength-to-time mapping,a new photonic CS scheme is proposed.It can adjust the compression ratio and improve the adaptability and flexibility of the system.In this scheme,an optical pulse is broadened and get chirped after propagating through the first dispersive fiber.The input signal to be measured is modulated on the stretched pulse via the first modulator.Then the modulated pulse is further modulated by a PRBS at the second modulator.In this way,the signal mixing between the PRBS and the input signal can be achieved.Next,the modulated pulse is compressed in the time domain after propagating through the second dispersive fiber.The compressed pulse is then incident into a programmable optical filter.In the filter,the pulse spectrum is divided into a number of segments and demultiplexed into the different ports of the filter.Each output port corresponds to a spectrum segment.Due to the property of wavelength-to-time mapping,each spectral segment corresponds to a time duration of the chirped pulse.At the outputs of the programmable optical filter,the spectrally segmented pulse is converted into multiple subpulses.Through the processes of photo-detection and down-sampling on the sub-pulses,the peak value of each sub-pulse is captured,which is used as a measurement result.In this way,different compression ratio can be set by controlling the number of spectral segments and output ports.Finally,according to the measurement results,the input signal can be recovered by using a sparse recovery algorithm.