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Smart Antenna Doa Estimation And Adaptive Beamforming Technology

Posted on:2006-06-30Degree:DoctorType:Dissertation
Country:ChinaCandidate:C Y ChengFull Text:PDF
GTID:1118360155451541Subject:Electromagnetic field and microwave technology
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In this dissertation, we provide the study on DOA estimation technique and adaptive beamforming technique of smart antennas. The main content of the dissertation consists of the gain and phase parameter estimation of array antennas, the mutual coupling coefficient estimation of array antennas, the union estimation approach of DOA of CM signal and array antenna parameter, the second optimization for weight vector of adaptive pattern with nonlinear constraint. Our dissertation is organized as follows. In the research on mutual coupling coefficient estimation of linearly uniform array and circular uniform array, we prove that the mutual coupling coefficient estimation has the least error upper limit rule when direction of calibration source is selected based on least condition number. The approach solves the influence that direction error of calibration source for precision of estimation result. When the direction of calibration source is unknown, we present an iterative method to estimate the gain and phase parameter or the mutual coupling coefficient by using measure data when calibration source is in different direction for circular uniform array. The approach is an auto-calibration algorithm. Hence the direction of calibration source may unknown. For the CM (FM, FSK and PSK) signal, we present the union estimation approach (namely auto-calibration approach) for DOA of CM signal and array antenna parameter. The auto calibration is a circularly iterative method. Compared with the approach in references, the method in the dissertation can ensure the convergence of iterative process when the combination of signal source is very complexity. Moreover, the dissertation proves the linear constraint in references is an especial example of the nonlinear constraint of our method. We introduce diagonal loading into matrix split. So the matrix inverse operation of LCMV approach can be replaced by matrix iterative. The operation complexity is reduced accordingly. Because one of split matrix is diagonal matrix, the condition number is very small. Hence the numerical stability is well. Li C C presents a robust adaptive beamforming approach based on noise subspace, but the value of the key parameterλ can not provide. We give the unique solution prove of parameterλ and by using nonlinear constrained condition. A new RCB approach is presented. The RCB approach is an alterably diagonal loading approach. To ensure the main beam aims at SOI, the RCB approach weaken the restraining ability for interference signals. The approved RCB approach adds the second optimization process based on RCB approach. The new optimization process adjusts the depth and direction of the nulls and the output SINR of array antennas is enhance. Because the demerit that the solution of RCB approach' diagonal loading value is complicated, we present a simple and convenient method to acquire the diagonal loading value. The new approach can replace the RCB approach in practice. When the SNR of SOI exceeds a certain level, the SOI will be view as an interferer and tend to be suppressed by the LCMV beamformer. Hence a beamforming method combining modified beam space and nonlinear constrained condition is presented. The modification of beam space eliminates the component of SOI in a broad extent. The minimum process of the cost function under nonlinear constrained condition adjusts the depth and direction of the nulls and the output SINR of array antennas is enhanced evidently. We introduce the nonlinear constrained condition into blind cyclic adaptive beamforming approach. The R-CAB approach can provide higher output SINR after second optimization.
Keywords/Search Tags:Direction of arrival, Adaptive beamforming, Error calibration, Auto calibration, Linearly constrained minimum variance, Diagonal loading, Beam space, Nonlinear constraint
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