The PSK Signal Parameter Estimation Based On Compressed Sensing

As an important research field of signal processing, parameter estimation has drawn more and more researchers’ attention. How to use a quick and efficient algorithm to estimate the parameters of the signal has always been a hot research topic. Parameter estimation is of great significance for modulation identification of the back-end signal, and has a wide range of applications both in civilian areas, such as disaster prevention, mitigation and geological exploration, and in military fields, such as electronic countermeasures and intelligence reconnaissance. On the other hand, with the development of communication, ultra-wideband signal has become the mainstream of communication signals. The ultra-wideband bandwidth of the signal will need a large amount of data storage and also bring transmission problems. It has become a bottleneck in processing of the communication signal. Compressed sensing is a technique expected to solve problems mentioned above. In compressed sensing theory, there’s no need to sample signal in high speed. Instead, the projection matrix, a form of “low speed” sampling, is used. Hence, a lot works were done on the combination of the compression perception and phase shift keying modulation(MPSK)(as we all know, MPSK, as an important kind of digital modulation signal, is the most common modulation in digital communication). In this paper, several kinds of common phase shift keying modulation signal based on compression sensing are discussed, and a thorough research about the parameter estimation of these signal is made. The main work includes:First, this paper introduces the background of the compressed sensing theory and the research background and significance of parameter estimation briefly, and systemizes the dynamic research status and development of the compression perception and parameter estimation.Secondly, this paper systematically introduces the compressed sensing theory. In particular, a detailed introduction of the sparse representation of signal, design of measure matrix and the signal reconstruction problem is given. Also, MPSK(BPSK, QPSK, 8PSK) is analyzed in sparse domain.Thirdly, this paper introduces the PSK signal parameter estimation method based on compression perception using cyclic spectrum model in detail. Reconstruct signal’s cycle spectrum. Cairrer frequency is estimated according to the relationship between the discrete spectrum line of cyclic spectrum of signal and the signal parameters, and in the cyclic spectrum reconstruction process, use the change of the residual error estimating the source number. The simulation result shows that under proper conditions, the precision of carrier frequency estimation can reach above 99%, and it can accuratly estimate the source number.Finally, this paper studies the PSK signal parameter estimation method which is based on the cyclic cumulants model using compressive sensing. Reconstruction of the fourth-order cyclic cumulants of the signal is made. Based on the relationship between the fourth-order cyclic cumulants and signal parameter, the paper estimates carrier frequency. Moreover, this paper studies the effect of the signal to noise ratio and number of element to parameter estimation error. Through the simulation, we can see that under proper conditions, the precision of carrier frequency estimation can reach more than 99%.