Study On Space-Time Adaptive Processing And Target Parameters Estimation Of Airborne Phased Array Radar

Space-time adaptive processing (STAP) provides great potential over improving the performance of the planar array radar on clutter suppression and moving target detection. Fully STAP can obtain the optimal performance in theory. However, the computation load and the implementation complexity of Fully STAP are too expensive to be accepted and it is really very difficult to obtain enough secondary data to estimate the covariance matrix. The focus of successive research is concentrated on the reduced-dimensional algorithms and many reduced-dimensional methods have been proposed. With the development of STAP, clutter suppression in nonhomogeneous environment, such as strong moving targets and strong isolated clutter, nonstationary clutter suppression and target parameters estimation are widely investigated and various methods are developed. The aforementioned aspects are mainly concerned in this dissertation, and they are summarized as follows:Utilizing the characteristic of the kronecker product, fast evaluation of the adaptive direction diagram and the output signal-to-clutter-plus-noise ratio (SCNR) using 1D-FFT are presented, and fast implementation of the two dimensional frequency response and the two dimensional output SCNR using 2D-FFT are also developed.Based on the sliding window range secondary selection method, the sliding window recursive QR decomposition method is presented. The presented method computes adaptive weight by QR decomposition of the secondary data matrix. Since the condition number of a covariance matrix is the square of that of a secondary data matrix, and thus the numerical stability of STAP based on QR decomposition of the secondary data matrix is much better than that of STAP based on covariance matrix inversion. Moreover, the presented method realizes weight recursion by the hyperbolic Householder transformation and greatly reduces the computational load of the computation of the weight vector.Considering the fact that the near-range clutter and moving targets are distinguishable in elevation space, two methods for range-dependent clutter suppression are developed, where the first method is based on the projection matrix method, the second method is based on the robust beamforming method. The former method estimates the elevation angle of the near-range clutter by spectral Capon rooting method, and the projection matrix is then constructed for suppressing the near-range clutter according to the estimated elevation angle. Compared to the nonadaptive method for range dependent clutter suppression, the method is free of priori knowledge. Due to the arbitrary of the moving target, the elevation angle of the moving target can not be accurately determined. The target signal will be treated as an unwanted interference signal by the adaptive processor and therefore will tend to be suppressed. It dramatically degrades the output signal-to-interference-plus-noise ratio (SINR). In order to avoid the target signal cancellation, the latter method is presented for improving the robustness of the elevation adaptive beamformer. Thus, the near-range clutter will be suppressed, while the far-range clutter and the moving target will be preserved. Under the ideal case that no array errors are occurred, the performance of the first method is slightly better than that of the second method. However, under the non-ideal case that array errors are occurred, the performance of the second method is much better than that of the first method.A moving target detection method based on power spectrum suitable to the extremely nonhomogeneous environment is proposed. Different to the general STAP methods, the presented method realizes moving target detection in the spatial-temporal spectrum domain. The secondary data obtained by spatial smoothing and temporal smoothing in single range unit can be considered identically distributed. Therefore, these secondary data can be used for covariance matrix estimation and then the power spectrum is estimated from the estimated covariance matrix. To alleviate the computation load of the power spectrum computation, 2D FFT is proposed to estimate the power spectrum. Utilizing the fact that the mainlobe clutter energy is much stronger than the noise power and the target power, the points with strong energy in the power spectrum can be extracted to fit the clutter ridge. Next, considering the fact that the distance between the moving target and the clutter spectrum is larger than zero, moving target detection can be implemented in the power spectrum. In this method, twice threshold detection are implemented, where one is implemented to extract the clutter with strong energy for clutter ridge fitting, the other is implemented to extract the targets for moving target detection.Two methods of target parameters estimation for uniform linear array (ULA) are developed. The first method estimates the target angle from the clutter-suppressed data using the least squares method. To avoid the grid search, the real polynomial rooting method is employed to convert the grid search to the extreme value search. The second method estimates the target angle from the likelihood function. Similarly, to avoid the grid search, the real polynomial rooting method is also used.Two methods of target parameters estimation for planar array are developed. The first method is the least squares method employing the alternating maximization method and the real polynomial rooting method. The second method is the maximum likelihood method employing the alternating maximization method and the real polynomial rooting method. The first method estimates the target angle from the clutter-suppressed data using the least squares method. To avoid the two-dimensional grid search, the alternating maximization method is used here to convert the joint estimation of the azimuth angle and the elevation angle to the iterating estimation. Furthermore, the real polynomial rooting method is employed to estimate the target angle instead of the grid search. The second method estimates the target angle from the likelihood function. Similarly, to avoid the two-dimensional grid search, the alternating maximization method and the real polynomial rooting method are also utilized.