Study On STAP For Airborne Phased Array Radar

Space-time adaptive processing (STAP) is a crucial technique applied to clutter suppression and target detection for new generation airborne phase-array radar. However, the tremendous computational complexity poses a primary challenge to implement STAP in practical engineering. Apart from traditional reduced-rank processing, an attractive suggestion for the computational problem is to develop the recursive algorithms of calculating weight vectors, which is computationally efficient and can be laid out in a highly parallel/pipeline structure in hardware. Thus, Chapter 3 to Chapter 5 make a detailed investigation of this, and the proposed fast algorithms can be exploited to solve adaptive weights associated with full-rank processing or fixed reduced-rank processing..Another major challenge for STAP application stems from the nonhomogeneity of practical clutter environments, which can significantly skew the clutter covariance matrix estimate. Therefore, a robust nonhomogeneous detection methodology for censoring the interference-targets with mismatched steering vectors is proposed in Chapter 6. Chapter 7 achieves a new technique of Doppler compensation in airborne forward-looking radar for ground short range clutter. The main contributions of this dissertation are as follows.1) The problem of weight vectors calculations in the case of estimated covariance matrix is investigated. Due to the fact that the covariance matrix is positive-definite Hermitian and its leading principal minors are all nonzero, a recursive algorithm of computing weights with numerical stability property is first developed on the basis of the Hermitian matrix inversion lemma, and then derives a fast algorithm of inversion for such a matrix whose leading principal minors are all nonzero thereby presenting a new approach for computing weights. The implementations for above algorithms involve matrix-vector multiplications, vector inner products and vector outer products, these operations are highly parallelizable, where the number of the iterations required is equal to the dimension of covariance matrix. 2) The derivation of a loaded sample-matrix inverse (LSMI) algorithm based on updating the inverse of the sample covariance matrix is conducted by reconstructing the recursive formulation of covariance matrix. The new algorithm removes the necessity of a covariance matrix estimation and needs the number of samples iterations, where the dominant operations come from matrix-vector multiplications, vector inner products and vector outer products. In addition, an improved iterative process is presented, resulting in significant computational savings.3) The computationally efficient implementation of LSMI algorithm employing the QR decomposition or inverse QR decomposition is introduced. In which the diagonal loading can be inserted by setting only initial Cholesky or inverse Cholesky factor without any addition of computation.4) A recursive sample-matrix inversion (SMI) algorithm realized only by means of a forward analysis stage of multistage Wiener filter (MWF) is developed, which reduces the time-delay by eliminating the backward synthesis stage. Furthermore, several approaches for adding diagonal loading to MWF are presented.5) The effect of a mismatch between the actual steering vector and the assumed desired one for the interference-targets signals in nonhomogeneous clutter environments is analyzed, which results in significant performance degradation or even complete failure for traditional adaptive power residue (APR) method. An enhanced methodology which first performs a strong interference-targets censoring via diagonal loading of covariance matrix with a large constant followed by a remaining weak interference-targets censoring using traditional APR method is presented here, it is robust to the steering vector mismatch of interference-targets. Additionally, an efficient methodology to eliminate the interference-targets from a limited training-sample set is developed.6) A method for Doppler compensation for ground short range clutter of airborne forward-looking radar is proposed by using the vector (matrix) similarity criteria, whereby the compensation values can be evaluated from the received clutter data. The method significantly reduces the sensitivity of compensation values estimations against radar parameter errors and can be performed both in pulse domain and Doppler domain. Moreover, it has the advantage of low complexity and parallel implementation.7) Chapter 8 introduces a signal processing system design associated with sum and difference patterns of amplitude monopulse radar based on ADSP_TS101 chips.The research on the recursive algorithms of computing adaptive weights and the solutions to the clutter nonhomogeneity problem will provide the theory and technique supports for STAP technique application in practical engineering.