Research Of Practical Superresolution Algorithm Based On NC-MUSIC Algorithm

Spatial superresolution is a technology of array signal processing, which has a better estimation capability than the traditional beam forming methods. Due to the better estimation accuracy, nowadays it has been applicated in communication, radar, medical imaging, geological exploration and so on. The more complex the current electromagnetic environment is, the more attention to the technology of spatial superresolution under the actual background. This topic mainly researched on the application of spatial superresolution under the actual background.This dissertation mainly in the case of uniform linear array of non-circular signals superresolution direction finding algorithm and improved algorithm, and the uniform circular array and N +1 type of circular array case, using the MUSIC (Multiple Signal Classification) algorithm to the performance measurement. Main tasks are summarized as follows:First, we has built the models of uniform linear array, which provide the derivation of super resolution algorithm a convenient with using matrix form to represent array's outputs and manifold. Meanwhile we introduced the definition of the non-circular signal and the definition of non-circular rate, and also give out a mathematical model of non-circular signals, provide a preliminary knowledge for the following non-circular signals superresolution algorithm .Secondly, we use the classical MUSIC algorithm for the traditional narrowband signal model to estimate the direction of arrival(DOA), and give the simulation figures and analysis of results. Then, for non-circular signals, give a NC-MUSIC(Non-circular MUSIC) algorithm, and use it to one kind of largest non-circular rate signal: BPSK signal. And made the estimation of the DOA of the signal, give the simulation figures and the results analyzed. Also, under the same conditions, compared the performance of the MUSIC algorithm and the NC-MUSIC algorithm.Third, because the NC-MUSIC algorithm uses the method of eigenvalue decomposition, whose characteristics of computing are too complicated, I made introduction of a algorithm which directly use covariance matrices to build the noise subspace: SWEDE(Subspace Method without Eigendecomposition) algorithm. I have made simulation experiments, and give the results of analysis. Then, I used the characteristics of non-circular signals for this algorithm, proposed the NC-SWEDE(Non-circular SWEDE) algorithm and the unitary SWEDE algorithm, and also used the BPSK signal for the simulation and analysis of the two algorithms, then compare the performance of the two method. I made the comparison the computational time of the NC-MUSIC algorithm, NC-SWEDE algorithm and unitary SWEDE algorithm to indirectly compare the quantity of calculation of the three methods.Finally, in some practical application conditions, because uniform linear array does not meet the actual demand, I establish the mathematical models of uniform circular array and N+1 type of circular array, also given the formations of the matrix form of the array output and the manifold. Then use the MUSIC algorithm for estimation of DOA of these two types of arrays, give the figures of simulation and compare the merits of the two types of arrays, meanwhile, use NC-MUSIC algorithm on the two types of circular arrays for experimental research, and give the analysis of the simulation.