基于降维波束空间的实值ESPRIT单基地MIMO雷达测角算法

单基地多输入多输出(MIMO)雷达的波达方向(DOA)估计问题是近年来研究的热点。高维度的MIMO雷达数据,导致传统旋转不变性参数估计技术(ESPRIT)算法需要付出较大的运算代价。在低信噪比、低快拍数的条件下,传统ESPRIT算法性能会严重下降。为了克服传统ESPRIT算法的以上缺点,该文提出一种降维波束空间的实值ESPRIT算法。该算法通过转换矩阵,将高维度MIMO雷达数据转换到低维度的数据,从而去除数据中的冗余。然后再将低维数据变换到波束空间,构造实值旋转不变性等式,用以估计目标的角度。仿真结果表明,在低信噪比和低快拍数时,相比于传统ESPRIT算法,该文所提方法具有更好的角度估计性能和更少的运算量。     

波束空间的双基地MIMO雷达定位方法

提出了一种基于波束空间的双基地多输入多输出(MIMO)雷达目标定位方法——B-ESPRIT算法。该算法可重构受波束空间变换破坏的发射和接收阵列旋转不变特性,从而可利用ESPRIT方法来得到对目标收发方位角的估计值。由于降低了处理数据的维数,所提的B-ESPRIT算法相比常规的阵元空间ESPRIT(E-ESPRIT)算法具有更小的运算量。此外,还详细分析讨论了算法的波束增益与目标角度的关系,并通过计算机仿真证明了分析的正确性。     

Multidimensional multirate DOA estimation in beamspace

This paper presents a beamspace version of ESPRIT for uniform rectangular arrays that supports closed-form 2-D angle estimation, automatically couples the two components of the source directions, and works with any front end beamformer. The proposed algorithm is based on the observation that beamspace noise eigenvectors can be transformed to vectors that are bandpass and have spectral nulls at the inband source locations. This facilitates multirate processing (involving modulation to baseband, filtering, and decimation) and yields a space with dimensionality equal to the number of beams used to probe the subband rather than the number of elements in the sensor array. The MUSIC algorithm can be applied to this noise subspace. Alternatively, a transformation matrix can be computed a priori, which maps the beamspace signal eigenvectors to the corresponding signal subspace that has the ESPRIT structure. The TLS-ESPRIT algorithm is then modified to obtain the two directions for each source from a single eigenvalue-eigenvector pair. Hence, they are automatically coupled     

Closed-form 2-D angle estimation with rectangular arrays in elementspace or beamspace via unitary ESPRIT

The UCA-ESPRIT is a closed-form algorithm developed for use in conjunction with a uniform circular array (UCA) that provides automatically paired source azimuth and elevation angle estimates. The 2-D unitary ESPRIT is presented as an algorithm providing the same capabilities for a uniform rectangular array (URA). In the final stage of the algorithm, the real and imaginary parts of the ith eigenvalue of a matrix are one-to-one related to the respective direction cosines of the ith source relative to the two major array axes. The 2-D unitary ESPRIT offers a number of advantages over other proposed ESPRIT based closed-form 2-D angle estimation techniques. First, except for the final eigenvalue decomposition of a dimension equal to the number of sources, it is efficiently formulated in terms of real-valued computation throughout. Second, it is amenable to efficient beamspace implementations that are presented. Third, it is applicable to array configurations that do not exhibit identical subarrays, e.g., two orthogonal linear arrays. Finally, the 2-D unitary ESPRIT easily handles sources having one member of the spatial frequency coordinate pair in common. Simulation results are presented verifying the efficacy of the method     

Multiple cluster beamspace and resolution-enhanced ESPRIT

This paper addresses approaches to enhancement of the resolution of one or more clusters of closely spaced emitters. The TLS-ESPRIT algorithm is considered, as applied in beamspace and in element space, in conjunction with resolution enhancement. Beamspace (BS)-ESPRIT employs a matrix beamformer as a preprocessor to map the sensor space into a lower dimensional beamspace. Resolution enhanced (RE)ESPRIT employs a matrix beamformer as a left weighting of the signal subspace eigenvector matrix for enhancing the resolution of ESPRIT. This paper specifically discusses several types of these beamformer matrices, which differ in the way that they treat source clusters other than of interest. Our objective is to clearly define approaches and identify their relative merits, through discussion and illustrative simulation, so as to provide an understanding of how to proceed in designing an ESPRIT algorithm     

Closed-form multiple invariance ESPRIT

The multi-least-squares fitting and regularization formulations of the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) problem are herein presented to provide a new framework for developing the closed-form multiple invariance ESPRIT (MI-ESPRIT) algorithm. The resulting two closed-form MI-ESPRIT algorithms incorporate all invariances in arrays composed of several (more than one) equally translated identical subarrays, and maintain the computational advantage of the standard single invariance ESPRIT. This work was supported by the National Nature Science Foundation of China (no. 60272025).     

Multiple invariance ESPRIT

A subspace-fitting formulation of the ESPRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPRIT algorithm is developed and the asymptotic distribution of the estimates is obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPRIT with that of several other approaches. The excellent quality of the MI ESPRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient     

波束-多普勒酉ESPRIT多目标DOA估计

高分辨波达方向(DOA)估计是地基/空基预警雷达实现主波束内多目标精细跟踪需要解决的关键问题。针对上述问题,该文提出一种波束-多普勒酉ESPRIT多目标DOA估计算法。该方法首先通过时域平滑技术构造多个快拍。然后采用中心共轭对称傅里叶变换矩阵将数据变换至波束-多普勒域,同时保留旋转不变结构。最后采用实值ESPRIT算法估计目标的DOA。所提方法充分利用了信号的时域信息来改善空域参数估计性能,同时具有较低的计算复杂度。实验结果证明了所提方法的有效性。     

Closed-Form Blind 2D-DOD and 2D-DOA Estimation for MIMO Radar with Arbitrary Arrays

In this paper, we study the problem of four-dimensional angle estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays, and propose a close-form joint two-dimensional direction of departure and two-dimensional direction of arrival estimation algorithm. Our work is to extend the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to angle estimation in MIMO-radar with arbitrary arrays. The algorithm can achieve automatically paired four-dimensional angles, requires no peak searching, has low complexity, and does not need to compensate for the phase. Furthermore, the proposed algorithm has much better angle estimation performance than the interpolated ESPRIT algorithm and propagator method. We also analyze and derive the complexity of the algorithm and the Cramer–Rao bound. The simulation results verify the effectiveness of the algorithm.     

任意几何结构阵列下的空间信号频率估计

该文论述了一种基于波束空间ESPRIT并适用于任意几何结构阵列的空间信号频率估计算法。在把阵元空间数据映射到DFT空间后,提取出不依赖于阵列几何结构的频率矢量,然后运用BeamspaceESPRIT的概念并通过一个间接的方法估计出信号频率。计算机模拟结果证实了该算法对不同阵列获取的信号的测频有效性并显示出良好的高分辨、高精度性能。     

Unitary ESPRIT: how to obtain increased estimation accuracy with areduced computational burden

ESPRIT is a high-resolution signal parameter estimation technique based on the translational invariance structure of a sensor array. Previous ESPRIT algorithms do not use the fact that the operator representing the phase delays between the two subarrays is unitary. The authors present a simple and efficient method to constrain the estimated phase factors to the unit circle, if centro-symmetric array configurations are used. Unitary ESPRIT, the resulting closed-form algorithm, has an ESPRIT-like structure except for the fact that it is formulated in terms of real-valued computations throughout. Since the dimension of the matrices is not increased, this completely real-valued algorithm achieves a substantial reduction of the computational complexity. Furthermore, Unitary ESPRIT incorporates forward-backward averaging, leading to an improved performance compared to the standard ESPRIT algorithm, especially for correlated source signals. Like standard ESPRIT, Unitary ESPRIT offers an inexpensive possibility to reconstruct the impinging wavefronts (signal copy). These signal estimates are more accurate, since Unitary ESPRIT improves the underlying signal subspace estimates. Simulations confirm that, even for uncorrelated signals, the standard ESPRIT algorithm needs twice the number of snapshots to achieve a precision comparable to that of Unitary ESPRIT. Thus, Unitary ESPRIT provides increased estimation accuracy with a reduced computational burden     

基于高阶ESPRIT的单声矢量水听器近场声源定位

为避免传统近场定位算法需要三维搜索,计算量大的问题,该文结合高阶累积量提出一种适用于单矢量水听器近场声源定位的旋转不变子空间(ESPRIT)算法。首先通过定义一系列的四阶累积量矩阵,获得了3个不变性矩阵,然后从这些不变矩阵中提取近场源的位置信息,该方法可以得到目标的方位角、俯仰角和距离的封闭形式的解。最后通过仿真验证了本算法的有效性。     

Reduced-Dimensional ESPRIT for Direction Finding in Monostatic MIMO Radar with Double Parallel Uniform Linear Arrays

In this paper, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with double parallel uniform linear arrays is studied, and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through a series of reduced-dimensional transformations, the proposed algorithm has very low complexity due to the low dimension. Meanwhile, the estimation performance of the proposed algorithm is slightly improved compared to the conventional ESPRIT, especially in low signal-to-noise ratio. Furthermore, the algorithm can estimate azimuth and elevation angles without additional pair matching in monostatic MIMO radar. Error analysis of the angle estimation and Cramér–Rao bound are derived. Simulation results verify the usefulness of our algorithm.     

用均匀中心对称阵二维西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,它是一种闭环形式的高分辨算法,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.并推广应用于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能分析.仿真结果说明了算法是有效的.     

基于MI-MUSIC的分布式阵列波达方向估计方法

针对分布式阵列的波达方向估计,提出了一种基于多尺度旋转不变(MI)MUSIC的波达方向估计方法。首先对分布式阵列进行子阵划分,构造具有多尺度旋转不变性的子阵;然后利用酉ESPRIT算法得到精度低但无模糊的粗估计,采用MI鄄MUSIC算法得到一组精度高但包含模糊的精估计;最后以粗估计为参考解精估计的模糊,从而得到高精度无模糊的波达方向估计。所提方法具有分布式阵列及MI-MUSIC的优点,对子阵内部阵元位置误差不敏感,且精度较双尺度ESPRIT高。计算机仿真结果验证了该方法的有效性,也验证了分布式阵列DOA估计中存在基线模糊门限与信噪比门限。     

Exploiting arrays with multiple invariances using MUSIC and MODE

This paper describes several new techniques for direction of arrival (DOA) estimation using arrays composed of multiple translated and uncalibrated subarrays. The new algorithms can be thought of as generalizations of the MUSIC, Root-MUSIC, and MODE techniques originally developed for fully calibrated arrays. The advantage of these new approaches is that the DOAs can be estimated using either a simple one-dimensional (1-D) search or by rooting a polynomial, as opposed to the multidimensional search required by multiple invariance (MI)-ESPRIT. When it can be applied, the proposed MI-MODE algorithm shares the statistical optimality of MI-ESPRIT. While MI-MUSIC and Root-MI-MUSIC are only optimal for uncorrelated sources, they perform better than a single invariance implementation of ESPRIT and are thus better suited for finding the initial conditions required by the MI-ESPRIT search     

均匀线列阵下的快速TLS-ESPRIT算法

ＴＬＳ－ＥＳＰＲＩＴ算法是一种方位估计算法,由于采用了全局最小二乘估计,其在低信噪比下的估计性能较ＥＳＰＲＩＴ算法有所提高,但算法略显复杂。本文针对均匀线列阵,构成了ＴＬＳ－ＥＳＰＲＩＴ的简化算法,使其运算量明显减少,同时还可给出源功率的估计。文中给出了算法的结构及计算机仿真结果。     

基于均匀圆阵的二维ESPRIT算法研究

在深入研究均匀圆阵扩展阵列流形的基础上，针对某些均匀圆阵的扩展阵列中含有旋转不变性的子阵列组以及中心对称性质，提出了2种基于均匀圆阵的二维ESPRIT算法，第一种算法减少了一次奇异值分解，第二种算法避免了参数配对。仿真结果验证了算法的正确性和有效性，并且在高斯色噪声条件下，其测向性能均优于MUSIC算法和最大似然算法。     

Discrete wavelet transform: data dependence analysis and synthesisof distributed memory and control array architectures

We perform a thorough data dependence and localization analysis for the discrete wavelet transform algorithm and then use it to synthesize distributed memory and control architectures for its parallel computation. The discrete wavelet transform (DWT) is characterized by a nonuniform data dependence structure owing to the decimation operation it is neither a uniform recurrence equation (URE) nor an affine recurrence equation (ARE) and consequently cannot be transformed directly using linear space-time mapping methods into efficient array architectures. Our approach is to apply first appropriate nonlinear transformations operating on the algorithm's index space, leading to a new DWT formulation on which application of linear space-time mapping can become effective. The first transformation of the algorithm achieves regularization of interoctave dependencies but alone does not lead to efficient array solutions after the mapping due to limitations associated with transforming the three-dimensional (3-D) algorithm onto one-dimensional (1-D) arrays, which is also known as multiprojection. The second transformation is introduced to remove the need for multiprojection by formulating the regularized DWT algorithm in a two-dimensional (2-D) index space. Using this DWT formulation, we have synthesized two VLSI-amenable linear arrays of LPEs computing a 6-octave DWT decomposition with latencies of M and 2M-1, respectively, where L is the wavelet filter length, and M is the number of samples in the data sequence. The arrays are modular, regular, use simple control, and can be easily extended to larger L and J. The latency of both arrays is independent of the highest octave J, and the efficiency is nearly 100% for any M with one design achieving the lowest possible latency of M