Study On ∑Δ-Beam Space-Time Processing Techniques For Moving Target Detection

Effective ground clutter suppression is the major task for the airborne radar moving target detection (MTD). Since conventional airborne radar clutter suppression techniques such as single antenna moving target indication and pulse-Doppler radar fail to detect slowly moving targets masked by mainlobe clutter, it is necessary to investigate multi-channel space-time processing techniques to improve the MTD performance of both airborne moving target indication (AMTI) and airborne synthetic aperture radar (SAR) ground moving target indication (GMTI). This dissertation focused on the research ofΣΔ-beam space-time processing techniques for MTD. It concerns the respects as,ΣΔ-beam adaptive displaced phase center antenna (ΣΔ-ADPCA),ΣΔ-beam space-time adaptive processing (ΣΔ-STAP), andΣΔ-beam interferometric SAR/GMTI.Chapter 1 is the introduction. The histories of airborne radar MTD techniques are briefly outlined. Then, the latest development of airborne radar AMTI and GMTI in China and abroad is reviewed. The research background and main contents are also addressed.In chapter 2, theΣΔ-ADPCA is studied. Firstly, the principle of the time domainΣΔ-ADPCA is analyzed, and a novel algorithm is developed to get the optimum gain ratio. Since the clutter suppression performance of time domainΣΔ-ADPCA is seriously degraded in practice, the frequency domainΣΔ-ADPCA is proposed, which can adaptively calculate the optimum gain ratio within each sub-beam via the Doppler beam-sharpen, and can achieve an improved motion compensation and robustness. Simulation results fully validate the proposed methodology.Reduced dimension and reduced rankΣΔ-STAP is studied in chapter 3. Although the cross-spectral metric (CSM) can provide the upper limit of rank-reduction in theory, it is prohibited in practical implementation since the clutter and noise covariance is unknown. The effective algorithm is minimum norm eigencanceler (MNE). To reduce the high computation cost of MNE to get the clutter subspace via the single value decomposition (SVD), a modified projection approximated subspace tracking deflation (MPASTd) algorithm is proposed to recursively estimate the desired subspace. The proposed algorithm can significantly reduce the computational cost, and meanwhile achieve commensurate convergence property, which provides a feasible solution for MNE real-time processing.In nonhomogeneous environment, the independent and identically distributed (IID) samples are significantly reduced. In chapter 4, combined with multistage wiener filter (MWF), space-time autoregressive filter (STAR) and direct data domain processing (DDD), three different nonhomogeneousΣΔ-STAP algorithms are proposed, which work in different conditions, respectively. FRACTA is the nonhomogeneous STAP algorithm proposed by U.S. naval research laboratory. The nonhomogeneousΣΔ-STAP based on MWF can eliminate outliers via the two stages hybrid nonhomogeneous detector (NHD). That is, the training data can be firstly censored by the mainbeam output and then followed by the adaptive power residual (APR) detection. Then, the adaptive weight is computed via the reduced rank MWF. In addition, the modified Concurrent Block Processing (CBP) is applied to the processing data. Compared with FRACTA, the proposed algorithm has the advantage of less computation cost and fast convergence speed. If there were less IID samples and the NHD was useless, an outlier resistantΣΔ-STAR (ΣΔ-ORSTAR) algorithm is proposed. An approach for determining the outliers'Doppler frequency is proposed, which is based on the local maximum norm value of the weight-vector. Thus, the outliers can be eliminated in turn according to the estimated Doppler frequency.ΣΔ-ORSTAR can efficiently alleviate the effect of the outliers for decreasing the detection performance. In the severely nonhomogeneous environment, multiple constrains in Doppler domain are applied toΣΔ-DDD, which can enhance the robustness and dramatically reduce the computational cost. The validity of each algorithm is demonstrated via Monte Carlo simulation.In chapter 5, the principle ofΣΔ-beam interferomentric SAR/GMTI is studied, and an improved scheme incorporated with system error calibration is proposed. Firstly, it compensates the deterministic difference betweenΣSAR image andΔimage via the spatial cancellation weight. Then, the amplitude ratio is used to reject the outliers in each sub-image. Lastly, the system errors can be blindly calibrated by the signal subspace processing (SSP), and the clutter cancellation can be achieved. The proposed approach is proved to be robust and feasible via simulation results.The work of the whole thesis is concluded in chapter 6, and the issues to be further studied are pointed out as well.