Research On Data Compression And Communication Signal Identification

Data compression is a technical method which could reduce the amount of data, so storage space could be reduced, transmission speed and process rate could be enhanced. The compression method could reduce the data redundancy through reorganizing the data according certain algorithm. Compressed sensing (CS) is a newly raised theory in data sampling and compression direction in recent years. Existing data compression algorithms are based on the stored data itself, they are to remove the redundancy of available data to reduce storage space. CS breaks the limit of Nyquist theory, and data acquisition and compression are processed at the same time with this method, while it can sample signal below the Nyquist frequency. In this paper, according to the research works about CS and communication signal processing, the author did some investigations in application of CS in medical signals processing and signal modulation. Combining the wavelet transform and compressed sensing theory, CS method was applied in ECG and EEG signal processing. In the reconstruction algorithm based on Bayesian, iterative threshold selection method was used for wavelet coefficients selection. The method could achieve good compression properties, at the same time it has denoising effects, and it could improve SNR with 2-3 dB. In the signal modulation, a hybrid modulation scheme combining with Time Hopping-Pulse Position Modulation (TH-PPM), CS and Orthogonal Frequency Division Multiplexing (OFDM) is presented. With the introduction of data compression, data transfer rate could be increased. Taking advantage of the characteristics of OFDM, multi-path interference in wireless channel could be resolved effectively. In addition, through the research in frequency hopping spread spectrum technology and time-frequency analysis technology, a parameter estimation and identification method for frequency hopping signal was proposed based on smoothed pseudo Wigner-Ville distribution (SPWVD). Through the use of sliding window, also with base line derivation and adaptive iterative threshold methods, the problem of frequency hopping signal parameter estimation under low SNR could be improved effectively. To the situation of multiple signals presents, according to the spectrum characteristics of frequency hopping and fixed frequency signals, a method based on spectrum cancellation was proposed. It could achieve the signals separation without any priori knowledge. And then the signal parameter estimation and identification could be processed. The signal separation method effectively reduced the complexity of traditional signal separation system.