Joint DOA And Polarization Estimation Algorithm Researching For Polarization Sensetive Conformal Array Antenna

In this thesis, a joint estimation algorithm of DOA and polarization parameters for polarization sensetive conformal array is studied. This algorithm is helpful to realize the project on the FPGA+DSP architecture.Firstly,in allusion to the difference between vector conformal arrays and traditional arrays,polarized electromagnetic waves and the Euler rotation transformation are introduced briefly, then two mathematical models of receiving electromagnetic signals for vector arrays and conformal arrays are studied based on this.Secondly, in allusion to the problem of choosing a suitable mathematical model for the vector arrays, this thesis has compared two methods of parameter estimation which is the MUSIC algorithm and the vector cross product algorithm. Then, aimed at the Shadowing effect, we find that the method based on submatrix partition will reduce the array aperture artificially. To solve this problem, a method based on direction graph energy detection has been come up with. And the computer simulation proves this method to be correct, and it solves the problem mentioned up there commendablely.Thirdly, this thesis has researched a method which construct dynamic sub-array based on direction graph energy detection and do a four-dimensional search based on received signals from vector conformal arrays to get the estimation of polarization and DOA. But this algorithm has a problem of huge computaion. To solve this problem, the thesis has furtherly studied a method based on not only direction graph energy detection and also the methods of subspace to get the estimation of polarization and DOA. Simulation results show that the algorithm can guarantee the accuracy of the estimation of parameters and at the same time, reduce the amount of computation greatly.In the end, the flow chart and block diagram of this mentioned algorithm are given. And a method based on sine function periodic to reuse the results of two-dimensional search is studied deeply. Theoretically, this method can save 12.5% of computing resources.