The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive pro...The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.展开更多
A new method combining space-time preprocessing with multistage Wiener filters（STPMWF）is proposed to improve the performance of space-time adaptive processing（STAP）in nonhomogeneous clutter scenario.The new scheme...A new method combining space-time preprocessing with multistage Wiener filters（STPMWF）is proposed to improve the performance of space-time adaptive processing（STAP）in nonhomogeneous clutter scenario.The new scheme only requires the data from the primary range bin,thus it can suppress discrete interferers efficiently,without calculating the inverse of covariance matrix.Comparing to the original MWF approach,the proposed scheme can be regarded as practical solutions for robust and effective STAP of nonhomogeneous radar data.The theoretical analysis shows that our STPMWF is simple in implementation and fast in convergence.The numeric results by using simulated data exhibit a good agreement with the proposed theory.展开更多
利用球不变随机矢量(Spherically Invariant Random Vector,SIRV)描述非均匀杂波,建立了双基地多输入多输出(Multiple-Input Multiple-Qutput,MIMO)雷达距离扩展目标的信号检测模型,提出了距离扩展目标的两步广义似然比检测(Generalized...利用球不变随机矢量(Spherically Invariant Random Vector,SIRV)描述非均匀杂波,建立了双基地多输入多输出(Multiple-Input Multiple-Qutput,MIMO)雷达距离扩展目标的信号检测模型,提出了距离扩展目标的两步广义似然比检测(Generalized Likelihood Ratio Test,GLRT)算法.首先,根据目标散射系数的两种假设模型,分别推导确定型目标、高斯型目标GLRT检测器的解析表达式,然后利用固定点迭代算法估计杂波协方差矩阵,获得自适应GLRT(AD-GLRT和AG-GLRT)检测器.仿真实验表明:AD-GLRT和AG-GLRT检测器的检测性能均优于非均匀杂波背景、高斯杂波背景下点目标的检测性能,且两者的检测性能相当,并且虚拟阵元数、目标分布的距离单元数,以及信杂比越大,两者的检测性能越好.展开更多
文摘The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.
基金supported by the National Nature Science Foundation of China under Grant No. 60702070
文摘A new method combining space-time preprocessing with multistage Wiener filters（STPMWF）is proposed to improve the performance of space-time adaptive processing（STAP）in nonhomogeneous clutter scenario.The new scheme only requires the data from the primary range bin,thus it can suppress discrete interferers efficiently,without calculating the inverse of covariance matrix.Comparing to the original MWF approach,the proposed scheme can be regarded as practical solutions for robust and effective STAP of nonhomogeneous radar data.The theoretical analysis shows that our STPMWF is simple in implementation and fast in convergence.The numeric results by using simulated data exhibit a good agreement with the proposed theory.
文摘利用球不变随机矢量(Spherically Invariant Random Vector,SIRV)描述非均匀杂波,建立了双基地多输入多输出(Multiple-Input Multiple-Qutput,MIMO)雷达距离扩展目标的信号检测模型,提出了距离扩展目标的两步广义似然比检测(Generalized Likelihood Ratio Test,GLRT)算法.首先,根据目标散射系数的两种假设模型,分别推导确定型目标、高斯型目标GLRT检测器的解析表达式,然后利用固定点迭代算法估计杂波协方差矩阵,获得自适应GLRT(AD-GLRT和AG-GLRT)检测器.仿真实验表明:AD-GLRT和AG-GLRT检测器的检测性能均优于非均匀杂波背景、高斯杂波背景下点目标的检测性能,且两者的检测性能相当,并且虚拟阵元数、目标分布的距离单元数,以及信杂比越大,两者的检测性能越好.
