Theory And Methods Of Sparsity-based Space-time Adaptive Processing

Space-timeadaptiveprocessing(STAP)caneffectivelysuppressthestrongground/seaclutter and improve the moving target detection performance for airborne/spaceborneradar systems. Traditional STAP algorithms require about twice the degrees of freedom(DOFs) of the independent and identically distributed (IID) training snapshots to yieldan average performance loss of roughly3dB, have a high computational complexity andneed a lot of memory elements. However, in real applications, because of the limits ofradarsystemparameters,arraygeometrystructure,internalsystemnon-idealeffects,vary-ing clutter environments and the computational capacity, it is usually hard to satisfy theabove conditions. In this dissertation, we focus on the clutter suppression and movingtarget detection in airborne radar applications. By exploiting the intrinsic clutter sparsityof the space-time returns, we design a series of robust STAP algorithms for the complexand varying clutter environments with lower requirements of IID training snapshots.This dissertation first discusses the difficulties in STAP, elaborates the developmenthistory and current work of the STAP algorithms and sparse recovery algorithms, andanalyzes the advantages and key problems of the sparsity-based STAP algorithms, whichprovides a basis for the following researches.The second chapter studies the sparsity in the STAP and builds the theory frameworkfor the sparsity-based STAP algorithms. On one hand, it analyzes the intrinsic sparsityof the space-time power spectrum and discusses the relationship between the clutter spar-sity and clutter rank. On the other hand, it details the sparsity of the space-time filtersfrom the points of the system DOFs and the clutter DOFs. Then it builds the theoryframework for the sparsity-based STAP algorithms based on sparse filters, space-timepower spectrum sparsity and array geometry knowledge, followed by the derivations orintroductionsoftheprinciplesofthesparse-filter-basedSTAPtechniques, thespace-time-power-specturm-sparsity-based STAP techniques, the array geometry knowledge-aided(KA) STAP techniques and the space-time-power-specturm-sparsity-based STAP tech-niques exploiting prior knowledge of array geometry.The third chapter studies the sparse-filter-based STAP techniques. Considering thesparsity in the space-time filters, this chapter proposes a series of L1-regularized STAP al-gorithmstoovercometheslowconvergenceoftheiterativeadaptivespace-timefilters,in- cluding the L1-norm-based sample matrix inversion STAP algorithm, the L1-norm-basedonline coordinate gradient STAP algorithm, the L1-norm-based recursive least-squaresSTAP algorithm and the L1-norm-based conjugate gradient STAP algorithms. It also de-tails the setting of the regularization parameters in the proposed algorithms and developstwo approaches to adaptively select the regularization parameters’ value. Under condi-tions of certain prior knowledge, these two approaches can effectively select the regular-ization parameters’ value.The fourth chapter studies the array geometry KA-STAP techniques. It first strictlyderives the principle of the array geometry KA-STAP techniques which provides a basisfor this technique. For the uncertainty of the array geometry prior knowledge, we pro-pose the KA-STAP algorithm that exploit the low-rank dominant clutter and the arraygeometry properties (LRGP) and the eigenanalysis-based STAP algorithm. For the highcomputational complexity of the least-squares method, we present an efficient subspacecomputationmethodbasedonGram-Schmidtorthogonalizationbyexploitingthefactthatthe clutter subspace is only determined by the space-time steering vectors. For practicalapplications, a reduced-dimension version of the proposed LRGP-KA-STAP algorithmis also developed. It illustrates that a fast convergence and a robustness to the platformvelocity measured errors, yaw angle measured errors and the inner clutter motion of theproposed algorithms are obtained.The fifth chapter studies the space-time-power-specturm-sparsity-based STAP tech-niques with multiple training snapshots. To improve the performance of the recentlydeveloped weighted least-squares-based iterative adaptive approach (IAA) in STAP forweak or slow targets detection, we propose a novel IAA scheme to adaptively suppressthe ground clutter by using the training data. It shows that the proposed IAA scheme out-performs the conventional IAA scheme over weak or slow targets detection but a lowercomputational complexity. Since it is hard to set the regularization parameters of the cur-rentsparserecoveryalgorithms,weproposeanovelspace-time-power-specturm-sparsity-basedSTAPalgorithmbasedonthecomplex-valuedhomotopytechnique. Comparedwiththe current complex homotopy sparse recovery algorithm and the focal under-determinedsystem solution algorithm, the proposed algorithm provides excellent detection perfor-mance with lower computational complexity and easier parameter settings.Thesixthchapterstudiesthedirectdatadomainspace-time-power-specturm-sparsity-based STAP techniques. We first develop novel space-time-power-specturm-sparsity- basedSTAPalgorithmsthatutilizetheCaponspectrumandtheFourierspectrumweightedl1-norm penalty to further enforce the sparsity and approximate the original l0-norm. Thisweighted methods exhibit better performance than the non-weighted methods. Since theestimated clutter covariance matrix is not stable for the current direct data domain space-time-power-specturm-sparsity-based STAP algorithm, we propose a novel direct data do-main space-time-power-specturm-sparsity-based STAP algorithm utilizing subaperture s-moothing techniques. It uses only the snapshot in the cell under test to generate multiplesub-snapshots and jointly recovers the clutter spectrum using all generated multiple sub-snapshots, which can reduce the variance of the estimated clutter spectrum resulting inimproved signal-to-interference-plus-noise-ratio (SINR).The seventh chapter studies the space-time-power-specturm-sparsity-based STAPtechniques exploiting prior knowledge of array geometry. By using the prior knowledgeof array geometry, we propose orthogonal matching pursuit and homotopy KA-STAP al-gorithms which reduce the dimension of the space-time steering dictionary. Because theproposed algorithms recover the clutter power spectrum in a reduced-dimension space-time steering dictionary, it results in a lower computational complexity and a better spec-trum estimate. Furthermore, the details of the selection of potential clutter array manifoldvectors according to prior knowledge are also discussed for the proposed algorithms.Theeighthchaptermakesasummaryofthedissertation,whileseveralopenproblemsfor the sparsity-based STAP algorithms are proposed.In conclusion, the studies and results in this dissertation provide useful theory di-rections and valuable engineering applications for sparse representation/reconstruction,compressive sensing, system identification, clutter suppression and moving target detec-tion.