Research On Parameter Estimation Algorithm Of Array Antenna Signal Based On Compressive Sensing

Signal parameter estimation is an important part of array signal processing. With the increase of practical requirements, the requirements of parameter estimation system are becoming higher and higher. Therefore, in order to obtain accurate source location,the parameter estimation algorithms have received more attention. As the representative of the parameter estimation algorithm, the traditional MUSIC and ESPRIT algorithm after improvement already have a relatively high estimated resolution and accuracy.However, these algorithms have more demand for signal-to-noise ratio and snapshot conditions, and the algorithm is ineffective in the case of coherent sources. In recent years, people realize the theory of Compressive Sensing, and the estimation of signal parameters is realized by sparse reconstruction. The advantage of the algorithm is that it only needs single or less snapshots of the signal, and has natural decor relation capability. In this paper, the key technologies of this kind of algorithms are discussed and analyzed. The main work is as follows:1. This thesis introduces the theory of the traditional signal parameter estimation,and the relevant theoretical basis of the Compressive Sensing theory. On this basis, the signal estimation model based on the compressive sensing theory is introduced, and the difference and relation with the traditional model are analyzed. Laid the foundation of the theory.2. Aiming at the DOA estimation of scalar arrays, several DOA estimation algorithms based on Compressive Sensing theory are introduced. The algorithm mainly includes the minimization algorithm and the greedy algorithm. The simulation results show that the compressive sensing method has better performance than the traditional estimation algorithm in terms of resolution and of coherent source estimation, the performance of these algorithms is analyzed and compared, and the advantages and disadvantages of the algorithm are briefly analyzed, which provides the theoretical basis for choosing the appropriate algorithm in the practical engineering application.3. Aiming at the multi-parameter estimation of polarized sensitive array signal, the structure of polarized sensitive elements and the received data model of array signals are introduced, and the compressive sensing theory is extended to the multi-parameter estimation of polarized sensitive array signal. The receiver data model of the signal is reconstructed, and the sparse representation and multi-parameter estimation algorithm of the two signals are introduced according to the different polarized elements. The airspace arrival angle of the signal and the estimation of the polarization information are realized, and through simulation, the two algorithms estimation performance have been analyzed.4. In this thesis, we introduce the signal receiving data model of the mutual array.For the case of the unknown mutual information, we introduce two algorithm using the excitation matrix and the array oriented vector transformation. The sparse representation of the signal and the source location algorithm are used to estimate the spatial arrival information of the source signal in the case of unknown mutual information. Finally, we compare the estimation performance of the algorithm by simulation.