一种基于多级维纳滤波的改进空时抗干扰算法

传统的空时自适应处理（STAP）算法不仅计算量大, 而且不能抑制与导航信号来自同一方向的窄带干扰。针对此问题, 提出了一种改进的STAP抗干扰算法, 将时频域结合的IIR格型陷波器与改进MSNWF的功率倒置算法相结合, 通过IIR格型陷波器预测并抑制窄带干扰, 通过改进MSNWF抑制宽带干扰并实现降维处理。这种新的算法不仅能更有效地抑制窄带和宽带干扰, 而且提升了计算速度和输出信干噪比（SINR）。     

基于CG-MSNWF的空时自适应抗干扰算法研究

针对多级维纳滤波MSNWF(Multi-Stage Nested Winner Filter)算法的计算量大,在处理高维数据时不能满足实时性的需要问题,采用基于共轭梯度CG(Conjugate Gradient)的多级维纳滤波算法—CG-MSNWF。在相同的干扰抑制性能条件下,该算法相比于MSNWF不需要后向迭代过程,降低了计算量和对硬件存储器的要求,提高了算法的收敛速度,满足了实时性的需求。仿真结果证实了算法的实效性。     

一种空时联合的自适应波束形成算法

为了进一步提高传统最优空时滤波算法的性能,提出了一种新的空时联合的自适应波束形成算法。在给出算法的信号模型和算法原理的基础上,详细地阐述了该算法实现空时联合自适应滤波的机理,然后利用计算机仿真较全面地分析和比较了算法的性能。理论分析和仿真结果表明,该算法在提高输出信干噪比和削弱指向误差影响等方面明显优于传统的最优空时滤波方法。     

基于非均匀检测的机载雷达空时自适应处理方法

提出了一种新的非均匀杂波环境下的数据样本选取方法－－关联维数（ＣＤ）非均匀检测器,研究了非均匀杂波环境中改进干扰协方差矩阵的估计和自适应权的形成问题。仿真结果表明,非均匀检测器与空时自适应处理（ＳＴＡＰ）方法相结合,可显著地改进机载雷达的检测性能。     

主动声纳空时自适应混响抑制方法

为了提高主动声纳混响抑制能力,研究了空时自适应混响抑制方法.分析了运动(静止)平台混响多普勒频率的空时二维分布,并在此基础上研究了降雏空时自适应(时空级联处理)抑制运动(静止)平台混响方法.理论分析与湖试数据研究表明:时空级联处理方法对运动平台还是静止平台都具有较好的混响抑制性能,特别是其处理的优势在于抑制运动平台的混响.在高速运动的声纳中,利用空时自适应方法抑制混响,提高检测能力具有现实意义.     

降维空时自适应处理研究

降维是空时自适应处理(STAP:SPACE-TIME ADAPTIVE PROCESSING)实用化的重要手段,基于杂渡协方差矩阵特征分解的降维是一类重要的降维方法,但是特征分解的大计算量限制了这种方法的使用.本文详细分析了杂波协方差矩阵的特征值分布与降维空间的关系,提出了一种利用线性齐次方程组求解得到全部最小特征值对应的特征向量的方法,从而既可以在噪声子空间降维又避免了矩阵的特征分解.分析了信噪比尺度在特征空间和解空间的分布情况.最后,利用一组Mountain Top 真实数据(t38pre01v1)验证了方法的正确性.     

主动声纳空时自适应混晌抑制方法

为了提高主动声纳混响抑制能力,研究了空时自适应混响抑制方法。分析了运动（静止）平台混响多普勒频率的空时二维分布,并在此基础上研究了降维空时自适应（时空级联处理）抑制运动（静止）平台混响方法。理论分析与湖试数据研究表明：时空级联处理方法时运动平台还是静止平台都具有较好的混响抑制性能,特别是其处理的优势在于抑制运动平台的混响。在高速运动的声纳中,利用空时自适应方法抑制混响,提高检测能力具有现实意义。     

浅海主动声呐空时自适应混响抑制方法

混响伴随着主动声呐系统发射信号而产生的干扰,当声呐平台有一定的运动速度时,不同方位对应的混响具有不同的多普勒频移,使得混响在频域范围内形成一定带宽的干扰,与信号频谱发生混叠,采用常规的运动平台自身运动补偿技术单纯地从频域抑制混响效果很不理想。针对该问题,采用一种空时自适应处理方法利用混响空时分布特性,在最小化干扰的约束条件下求得自适应加权系数,使目标信号不受损失同时有效地抑制混响。通过仿真结果证明了该方法的有效性。     

《空时自适应处理原理》（第3版）已经出版

由Richard Klemm博士撰写、南京电子技术研究所翻译的《空时自适应处理原理》（第3版）专著已由高等教育出版社于近期出版。该书详细介绍了空-时自适应处理的基本原理,重点介绍了机载或天基相控阵雷达中的杂波抑制问题,特别涵盖了对地观测、警戒、侦察领域中机载或天基MTI雷达对慢动目标的检测原理,尤其是其中的杂波抑制技术。主要内容包括信息处理、杂波模型、阵列处理、带宽影响、     

机载MIMO雷达两级降维空时自适应处理方法

针对机载多输入多输出( Multiple-input multiple-output,MIMO)雷达杂波抑制问题,提 出一种两级降维空时自适应处理 方法。首先利用多普勒滤波对杂波信号进行时域降维处理;然后将空域发射 接收二维波束 形成权矢量重构为发射权矢量和接收权矢量Kronecker积形式,并将高维权矢量转化为两个 低维权矢量进行分别求解,最后进行权矢量合成。该算法能够有效降低训练样本数需求与运 算复杂度,在小样本条件下具有良好的杂波抑制性能,因此更具有实际应用价值。仿真结果 验证了算法的有效性。     

Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing

The eigenanalysis canceler(EC)method which is suitable for nonhomogeneous clutter can suppress clutter efectively by discarding the eigenvectors of small eigenvalues,which is a well-known subspace-based space-time adaptive processing(STAP)method.However,the computational complexity of conventional EC STAP method is huge due to the eigenvalue decomposition.Moreover,the corresponding performance would be degraded signifcantly by the subspace leakage phenomenon,since the clutter subspace is not strictly confned to a low-rank subspace any more.Therefore,an improved EC STAP method based on the data-independent clutter subspace estimation is proposed to reduce the computational complexity,where the clutter subspace is rapidly constructed by sampling the prolate spheroidal wave functions(PSWF)non-uniformly.In order to deal with the subspace leakage phenomenon,the proposed EC-PSWF STAP method is modifed based on the covariance matrix taper(CMT)to obtain the covariance matrix by re-establishing the noise floor.The corresponding performance of proposed method is evaluated by using the numerical simulation.     

基于RSS空时处理的指纹定位算法

为提高移动台定位算法的精度,研究了基于接收信号强度(received signal strength,RSS)空时处理的指纹定位算法。该算法包含指纹数据库的建立和指纹比对,在指纹数据库建立过程中,利用空时处理的方法克服无线信号的衍射﹑散射等影响;指纹比对时,研究了相同邻小区个数的选择对定位误差的影响,得出相同邻小区个数的最优选择。仿真结果表明,算法减小了RSS的波动性,具有低运算复杂度和高精度。     

Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing

The conventional space-time adaptive processing(STAP) method such as the typical sample matrix inversion(SMI)-based STAP method is difficult to implement for a practical system because intense computational complexity arises in calculating the inversion of a space-time covariance matrix directly.According to the block Hermitian matrix property of space-time covariance matrix,a new pulse-order recursive method is proposed in this paper to calculate the inverse covariance matrix for the STAP adaptive weight,which can reduce the computational complexity significantly.The proposed method requires initially calculating the inverse covariance matrix of the first pulse-order recursively based on the block Hermitian matrix property.In the following,the inversion of space-time covariance matrix is obtained recursively based on the previous pulse-order inverse covariance matrix.Next,the STAP adaptive weight is calculated based on the inversion space-time covariance matrix previously obtained.Compared with the conventional SMI-based STAP algorithms,the computational complexity of the proposed method is reduced to more than 50% for the same clutter suppression performance.This method can be applied to practical systems benefiting from small computational complexity and stable clutter suppression performance.     

自适应阵列智能天线抗干扰性能研究

分析了自适应阵列智能天线的基本原理,给出了基于复数LMS算法的自适应阵列智能天线波束形成方法,同时对该智能天线抗干扰性能进行了Matlab仿真,理论分析和仿真结果表明:自适应阵列智能天线阵能够实时地调整天线方向,使天线的主波束对准期望信号方向,零陷对准干扰方向从而抑制干扰信号,在干扰和低信噪比环境下,接收端使用智能天线可以大大降低误码率,该智能天线具有较强的抗干扰性能。     

基于Gray-Markel结构的多点自适应陷波算法研究

为了有效抑制短波通信中的窄带干扰,提出了一种改进的格型自适应多点陷波算法。该算法在Gray-Markel结构陷波器基础上,采用LMS-Newton迭代不断地更新陷波器参数。实验结果表明,算法具有比较理想的收敛性和鲁棒性。     

Reduced-rank space-time adaptive detection for airborne radar

For airborne radar,there are usually insufficient independent and identically distributed(IID)training data because of geometric considerations and terrain variations.The rank reduction technique is one of the most effective approaches to circumvent this problem.In this study,we investigate four reduced-rank spacetime adaptive detectors for airborne radar,namely,the reduced-rank sample-matrix-inversion(RR-SMI),the reduced-rank adaptive matched filter(RR-AMF),the reduced-rank adaptive coherence estimator(RR-ACE),and the reduced-rank generalized likelihood ratio test(RR-GLRT).Their asymptotic analytical probabilities of detection(PD’s)and false alarm(PFA’s)are all derived.These detectors all asymptotically attain a constant false alarm rate(CFAR).It is shown that these four reduced-rank detectors exhibit detection performance which is better than or comparable to that of two existing reduced-rank detectors,proposed by Reed and Gau(RG1and RG2).Moreover,these four reduced-rank detectors are more robust to change in power of clutter and noise than RG1 and RG2.     

改进的自适应邻域的多目标进化算法

提出了一种新的自适应邻域的多目标进化算法,该算法采用自适应邻域的方法维护群体的分布性。探讨了根据当前群体情况进行自适应改变邻域半径,避免了传统邻域策略所引起的邻域半径的取值影响群体分布性的问题。另外,利用自适应邻域半径和拥挤距离进行密度估计,使密度小的个体得到保留。实验结果表明,所讨论的方法是有效的,在保持群体分布性上优于NSGAII和NMOEA。     

A fast-converging space-time adaptive processing algorithm for non-Gaussian clutter suppression

The normalized fractionally-lower order moment (NFLOM) algorithm differs from the normalized least mean square (NLMS) algorithm in that it minimizes the lower order moment (p<2) of the error rather than the variance (p=2). This paper first evaluates the performances of the NFLOM for space-time adaptive processing in heavy-tailed compound K clutters in terms of the excess mean square error (MSE), misalignment, beampatterns, and output signal-to-interference-and-noise-ratio (SINR). The results show that the MSE curve of a small-order NFLOM exhibits faster convergence but higher steady-state error than a large-order NFLOM. Second, this paper proposes a new variable-order FLOM algorithm to dynamically change the order during adaptation, thus achieving both fast initial convergence and low steady-state error. The new algorithm is applied to STAP for Gaussian and non-Gaussian clutter suppression. The simulation results show that it achieves the best compromise between fast convergence and low steady-state error in both types of clutters.