【作者】 张皓；

【导师】 胡航；

【作者基本信息】 哈尔滨工业大学 ， 信息与通信工程， 2007， 硕士

【摘要】 对自适应数字波束形成(ADBF)技术的研究进行了三十余年,提出了许多有效的方法,但这些方法基本上都是基于阵元级的。ADBF技术在相控阵雷达中具有重要应用,此时阵列常常包含几百个至几千个阵元,通常采用子阵结构以减少软硬件成本,为此需要采用子阵级ADBF方法。现代相控阵雷达通常采用单脉冲测角算法来估计目标角度。单脉冲估计是一种对被检测的目标精确定向的技术,它基于和、差波束输出进行测角。存在干扰时,如果不对其进行有效的抑制,单脉冲估计的性能将急剧下降,无法检测与跟踪目标。需利用ADBF来消除干扰保持目标检测与角度估计的精确性。差波束作为单脉冲测角技术中不可缺少的一部分,同样需要进行自适应。因此,对子阵级差波束ADBF研究很有意义。首先,我们构造子阵级差波束ADBF信号模型,并将其推广到二维。针对差波束将上述提出的信号模型与常规线性约束最小方差(LCMV)方法相结合得到子阵级LCMV方法,在各种干扰情况下该方法都可以实现对干扰的很好抑制,但其局限性是自适应方向图的旁瓣电平较高。为控制自适应方向图的旁瓣电平,我们讨论几种可应用于差波束的方向图控制方法,包括归一化方法,最优失配检测法及基于子空间投影方法。将基于子空间投影方法和常规的子阵级LCMV方法相结合而得到的结合方法可有效地抑制自适应方向图的旁瓣电平,同时降低了自适应性能损失。当存在主瓣干扰时,基于LCMV方法虽然能在干扰方向上形成凹口但破坏了单脉冲比,使单脉冲测角能力急剧下降。因此我们研究子阵级主瓣干扰下自适应波束形成方法以保持单脉冲比。包括约束ADBF算法,主瓣干扰消除算法。我们进一步把主、旁瓣干扰消除技术相结合,在消除主、旁瓣干扰的同时保持单脉冲测角性能。为抵消结合方法的负作用,在旁瓣干扰消除时引入主瓣保形技术,使其只消除旁瓣干扰而保持主瓣形状。同时,本课题还分析了超分辩空间谱估计技术中存在阵元位置误差时,真实阵列流形和直接简化阵列流形的测向性能。仿真结果证明了所提出的方法是正确且有效的。更多还原

【Abstract】 The study on ADBF (Adaptive Digital BeamForming) approaches has been carried through for more than thirty years and lots of effective approaches have been proposed, but they are basically based on element level. ADBF techniques have important applications in phased array system, now the array usually comprises thousands of elements, so it usually adopts subarray configuration. Thus we need adopt ADBF methods at subarray level.Monopulse angle estimation is usually used in modern phased array radar. Monopulse is a radar technique through which the angular location of a target can be exactly determined. This technique can estimate the angle based on outputs of sum and difference beams. The performance of monopulse angle estimation degrades severely when there are jammers. If not effectively countered, electronic jamming can prevent successful radar target detection and tracking. Thus, as an important component of monopulse angle estimation, the difference beam also needs adaptive technique to cancel the jammer to maintain target detection and tracking exactly. Study on adaptive difference digital beamforming at subarray level has practical and academic value although it was rarely carried through.Firstly, based on the structure of the subarray, we construct the difference signal model at subarray level. By generalizing it to 2-D, we construct 2-D signal model at subarray level, this model has no limitation to the array configuration and can be applied to any plane array. The above models are appropriate to overlapped or non-overlapped subarrays.Combining the above signal model and the conventional LCMV method at element level we present a LCMV method at subarray level. In all kinds of jammer scenarios, the algorithm can suppress jammers well, but the disadvantage is that the SLL (SideLobe Level) of adaptive patterns is much higher.Quiescent pattern control is important for radar systems equipped ADBF and spatial adaptivity. We present some algorithms: the normalization algorithm; the MOD algorithm by introducing mismatched control vector and the SSP algorithm by partitioning subspaces to reduce the dimensions of adaptive process.The approach combining SSP and conventional approach can suppress SLL of adaptive pattern effectively and reduce adaptive performance loss compared with SSP.In monopulse angle estimation system, the LCMV approach can null the jammer effectively when there are jammers. But the monopulse technique for DOA estimation degrades severely when there is jammer in mainlobe. We study on ADBF approach for canceling MLJ (MainLobe Jammer) at subarray level while preserving the radar’s ability to estimate the target angle accurately using monopulse techniques. Then we combine an SLC (SideLobe Cancellation) approach with MLC (MainLobe Cancellation). A mainlobe maintenance (MLM) technique or constrained adaptation during the sidelobe cancellation process is imposed so that the results of the SLJ (SideLobe Jammer) cancellation process do not distort the subsequent mainlobe cancellation process.Simultaneous, this paper study on DOA ability, when there is sensor position uncertainty in superresolution spatial spectrum estimation algorithm.We simulate and analyze all the presented algorithms. The simulation results demonstrate the efficiency of the algorithms.更多还原