用L阵实现频率、二维到达角和极化的联合估计

在宽频段内多信号多参量联合估计已成为许多研究课题（例如,未积压辐射源识别,有源对消等）的重要研究内容。对基于子空间方法的多信号频率、二维到达角和极化参量的联合估计方法进行了论述。该方法采用L阵和ESPRIT算法来实现,避免了平面阵的复杂性。计算机仿真结果证实了该算法的有效性。     

频率、二维到达角和极化联合估计

在宽频段内多信号多参量联合估计已成为许多研究课题,例如,。未知辐射源识别,有效对消等等的重要研究内容,信号频率与二维到达角、二维到达角与极化的联合估计已开展研究,但信号频率二维到达角和极化联合估计的研究尚未见报导,本文提出了利用交叉偶极子平面阵和ＥＳＰＲＩＴ算法实现频率、二维到达角和极化联合估计的新方法,分析了算法结构,计算机模拟结果证实了算法的有效性。     

信号的二维到达角和极化估计

本文论述了一种基于子空间方法的多信号二维到达角和极化参量的估计算法。该方法采用了交叉偶极子阵元组成的Ｌ型阵列，利用子阵输出信号数据矩阵中包含的信号空间的旋转不变性质，借助于矩阵方法求解出信号的二维到达角和极化参量的估计值，并自动进行参数的配对，仿真结果证实了该算法的有效性。     

信号的二维到达角和极化估计

本文论述了一种基于子空间方法的多信号二维到达角和极化参量的估计算法。该方法采用了交叉偶极子阵元组成的L型阵列,利用子阵输出信号数据矩阵中包含的信号空间的旋转不变性质,借助于矩阵束方法求解出信号的二维到达角和极化参量的估汁值,并自动进行参数的配对。仿真结果证实了该算法的有效性。     

矢量阵元阵列对Chirp信号极化和到达角的联合估计

针对LFM信号,利用分数阶傅里叶变换构造了一种新的空间极化时频分布:提出了基于L型矢量阵元阵列的二维到达角和极化联合估计算法。理论分析和仿真实验结果都表明,在低信噪比下该方法比已有的极化和到达角联合估计算法具有更好的性能。     

宽频段空间信号频率、二维到达角和极化联合估计

在空间欠采样的条件下,通过巧妙定义稀疏L型阵列的四阶累积量输出矩阵,利用高阶累计量的盲高斯及阵列扩展特性,实现了宽频段空间非高斯信号频率、到达角和极化估计的分离,给出了一个基于四阶累积量 ESPRIT和整数搜索解模糊的任意高斯噪声环境下多个独立空间信号频率、二维到达角和极化的联合估计算法,L型阵列由和坐标轴方向一致的偶极子对组成,数值模拟结果证实了该文方法的有效性。     

多信号极化与到达角估计算法

论述了一种基于子空间方法的多信号极化与到达角估计问题的算法，该方法采用了有子天线均匀线阵，利用子阵列输出信号数据矩阵中包含的信号空间的不变结构用矩阵束方法解出信号极化与到达角的估计值并自动进行参数的配对，避免了参数的多维搜索配对过程。数值仿真的结果证实了该方法的有效性。     

非均匀噪声下频率及二维到达角的联合估计

提出一种适用于L型阵列的频率及二维到达角联合估计新算法.算法通过不同子阵的互协方差矩阵构建平行因子模型,应用三线性交替最小二乘算法求解,无需参数配对及多维搜索,可有效抑制非均匀噪声.均方根误差的仿真结果验证了该算法的有效性.     

一种信号频率及二维到达角联合估计新方法

提出了一种窄带信源频率、俯仰角及方位角联合估计新方法.该方法选择特定序号阵元输出计算的高阶累积量构造4个矩阵,然后在累积量域构造三面阵,并分析了该三面阵低秩分解的唯一性,接着利用分解得到的4个对角阵联合估计信源参数.该方法无须参数配对,无须谱峰搜索,适用于任意加性高斯噪声环境.仿真结果表明该算法是有效的.     

用均匀中心对称阵二维酉ESPRIT方法实现闭环形式二维角估计

本文提出了适用于一类均匀中心对称阵的二维西ＥＳＰＲＩＴ方法，它是一种闭环匠高分辨算法，对信号源方位角和仰角估计实现自动配对。在算法最后阶段构造一个矩阵，其第ｉ个特征值的实部和虚部分别与第ｉ个信号源相对Ｘ轴和Ｙ轴的方向余波一一对应。该算法除了初始变换和最后矩阵特征分解外，整个过程都采用高效的实值计算。并推广应用ＤＦＴ波束空间，可在较小的空间维数上处理，从而减小计算复杂性。最后对算法做了一阶近似渐进性     

提高信号到达角估计精度的新方法

现有的基于矢量天线估计信号到达角的方法都没有利用阵列的孔径信息,这样的估计虽然是无模糊的,但精度不高.该文在现有估计方法的基础上进一步利用阵列的孔径信息,通过迭代搜索的方法改善到达角估计的精度,数值模拟结果证明该方法是有效的.     

分布式嵌套阵列及其DOA估计

提出了一种分布式嵌套阵列天线结构,由两个相互独立的四级嵌套子阵构成。两个子阵间存在一个基线长度,且满足一定条件。对该阵列天线接收到的信号进行高阶累积量和Khatri-Rao积运算可以得到三个完全相同的均匀直线阵列天线结构。针对新得到的阵列天线结构,使用基于空间平滑技术的双尺度酉旋转不变子空间( ESPRIT)波达方向( DOA)估计算法对信号进行DOA估计。该方法可以有效地提高阵列天线的自由度,进而达到提高估计精度的目地。仿真结果证明了基于所提出阵列天线结构的DOA估计方法的有效性。     

Precision of direction of arrival (DOA) estimation using novel three dimensional array geometries

Numerous methods for direction of arrival (DOA) estimation, used in smart antennas have been already reported in previous studies. The precision of DOA estimation depends on the choice of the algorithm and the geometrical configuration of the antenna array. In this work, the performance of new geometrical configurations, i.e. 2D with equal area and 3D with equal volume including circular, square, triangular, hexagonal and star geometries, with equal number of antenna elements, are examined and compared to each other to find the most proper geometry. Monte-Carlo simulations are performed to evaluate the DOA precision of the proposed arrays using the MUSIC algorithm. It is shown that in three cases of comparison including 2D geometries, 3D geometries and 3D rotated geometries, with the star and triangular configurations one achieves better resolution in DOA estimation. It is also revealed that the rotated configurations show lower estimation error compared to normal configurations.     

时—空欠采样下的频率和DOA联合估计算法

针对时—空欠采样下入射信号的频率和波达方向(DOA)估计问题,提出基于谱校正和中国余数定理的联合估计算法。首先利用稀疏分布的传感器阵元构造非均匀线阵,然后对入射信号做多路并行欠采样;借助AM估计器,该算法仅耗费少量样本即可获得精确频率和相位差余数,再结合闭式中国余数定理分别得到频率和DOA估计值。由于谱校正结果可以同时为频率估计器和DOA估计器所利用,因而该算法的样本利用率高。此外,与现有基于中国余数定理的频率估计法相比,该估计器无需对源信号做多次欠采样,耗时更短,更适用于快速时变目标的估计。仿真结果验证了联合估计算法的有效性和高精度,而且该算法在低信噪比情况下亦可达到很高的成功检测率,具有广阔的应用前景。     

柱面共形阵列信源方位与极化状态的联合估计算法

针对柱面共形极化敏感阵列的结构特点建立了其导向矢量模型,以此为基础实现了信源方位与极化状态的联合估计。根据旋转不变子空间思想估计俯仰角和子空间原理、秩损理论,通过对阵列流形矩阵进行特定的形式变换,将其中隐含的信源方位信息与极化信息\     

Efficient 2-D DOA and frequency estimation for L-shaped array via RD-PM

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach.     

Joint angle and Doppler frequency estimation of coherent targets in monostatic MIMO radar

This paper discusses the problem of joint direction of arrival (DOA) and Doppler frequency estimation of coherent targets in a monostatic multiple-input multiple-output radar. In the proposed algorithm, we perform a reduced dimension (RD) transformation on the received signal first and then use forward spatial smoothing (FSS) technique to decorrelate the coherence and obtain joint estimation of DOA and Doppler frequency by exploiting the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. The joint estimated parameters of the proposed RD-FSS-ESPRIT are automatically paired. Compared with the conventional FSS-ESPRIT algorithm, our RD-FSS-ESPRIT algorithm has much lower complexity and better estimation performance of both DOA and frequency. The variance of the estimation error and the Cramer–Rao Bound of the DOA and frequency estimation are derived. Simulation results show the effectiveness and improvement of our algorithm.     

A fast algorithm for joint two-dimensional direction of arrival and frequency estimation via hierarchical space–time decomposition

In this paper, we present a fast algorithm for joint estimation of the two-dimensional (2-D) directions of arrival (DOAs) and frequencies of the incoming signals in wireless communications using a hierarchical space–time decomposition (HSTD) technique. Based on the HSTD, the proposed algorithm makes use of a sequence of one-dimensional (1-D) Unitary estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms to estimate these parameters alternatively in a hierarchical tree structure. Also, in between every other 1-D Unitary ESPRIT algorithm, a temporal filtering process or a spatial beamforming process is invoked to partition the incoming signals into finer groups stage by stage to enhance the estimation accuracy as well as to alleviate the contaminated noise. Furthermore, with such a tree-structured estimation scheme, the pairing of these parameters is automatically determined without extra computational overhead. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works.     

Joint frequency and DOA estimation using spatial-temporal co-prime sampling with off-grid sources

A method of joint frequency and direction of arrival (DOA) estimation using spatial-temporal co-prime sam-pling with off-grid sources was proposed to solve the problem of two-dimensional (2D) mismatches of signals.Firstly,the received signal was sampled by spatio-temporal co-prime sampling technique to construct 2D sparse reconstruction model with the filled coarray.Then the frequency mismatch term and angle mismatch term were incorporated into the re-ceived data model.Based on the above modified model,an improved greedy algorithm was presented to obtain the fre-quency mismatch and angle mismatch by estimating the spectrum leakage terms jointly.So the 2D off-grid sources can be corrected to accurate positions.The method can increase the degrees of freedom (DOF) in the space-frequency domain.Moreover,the method has reduced the amount of computation,and it can improve the frequency and DOA estimation accuracy in the meantime.Simulation results demonstrate the correctness and effectiveness of the proposed approach.