A Study Of Parameter Estimation Algorithm For Near-field And Strong/Weak Targets

The traditional high-resolution algorithms for the DOA's estimation are generally proposed based on the plane-wave model of far-field sources. However, when the signal sources are close to the array and located in the Fresnel region of the array aperture, the model of the near-field sources should be adopted according to the spherical wave-fronts, and then the parameter estimation of near-field source is necessary. Accurate parameter estimation of near-field source is studied in this thesis. Firstly, the basic theories of the signal sources located in the near/far field and the DOA estimation are introduced. Secondly, a novel rotating interferometer-based algorithm for the 3-D parameter estimation of near-field source is proposed, which uses the array data of rotating interferometer in different spatial positions to obtain the phase difference sequence and then unwraps the phase ambiguity via the digital integration operation. As a result, the elevation and azimuth angles as well as the range estimation for the near-field source can be accomplished. In addition, we can also estimate the parameters of azimuth angle and range by exploiting the phase properties of two correlation sequences constructed by the centrosymmetric array data of rotating interferometer. Finally, MH-NLS method based on the spaceborne rotating interferometer is introduced to locate the ground target. Simulation processing results have verified the effectiveness and validity of the proposed algorithm.In addition, when the radar detects dense strong/weak targets, due to the limited resolution and the submerging effect of strong targets, the traditional FFT-based algorithms can not effectively estimate the velocities of these targets. To deal with these issues, a modified Capon(MCapon) algorithm based on iterative adaptive algorithm(IAA) is proposed to achieve the high-resolution velocity estimation of strong/weak targets with the close centers. The proposed algorithm first applies Keystone transform to correct the range walks of multiple moving targets, and then IAA is applied to obtain the reconstructed covariance matrix. After eigenvalue decomposition, an MCapon detector is proposed to focus multiple moving targets, which keeps the outputs with the same amplitude, i.e., the constant 1. Finally, the high-resolution velocity estimation is achieved, and the robustness is improved in the meantime. Theoretical analysis and simulation results indicate that the proposed method can achieve a high-resolution estimation of radial velocities of the dense strong/weak targets located in one range gate, which validate that the proposed algorithm outperforms the traditional FFT-based algorithms and the subspace projection algorithms.