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无线定位的圆-角定位技术中,DOA估计极其重要。本文针对基于TD-SCDMA智能天线预处理后的虚拟均匀线阵MUSIC算法带来的阵列孔径小,抗阵元误差扰动性差的不足,研究了基于模式空间虚拟均匀线阵四阶累量的MUSIC算法,由于虚拟线阵四阶累量MUSIC算法的应用范围局限于独立的信号源的DOA估计,不能用于相关信号源DOA估计,因而提出了基于模式空间虚拟均匀线阵四阶累量的修正MUSIC(FOC-MMUSIC)算法,有效地拓展了阵元孔径,改善了系统抗阵元误差扰动和算法对相关信号源DOA的估计性能。 相似文献
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针对传统相位干涉仪测向法精度不高和MUSIC算法空间谱峰搜索耗时长的问题,提出了一种基于加权最小二乘法和MUSIC算法相结合的均匀圆阵测向技术。首先根据阵列短基线组求解相位模糊,并通过引入中间变量将相位差测量方程转化为线性方程组,然后运用加权最小二乘法对其进行求解,并利用信号到达角与中间变量的关系得到信号到达角初始估计,最后MUSIC算法依据信号到达角初始估计进行空间谱峰搜索,得到高精度信号到达角估计。仿真实验对比了所提方法与现有相位干涉仪及MUSIC算法的角度估计精度和耗时,证实了所提方法的正确性和有效性。 相似文献
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相干信号频率和到达角联合估计的算法 总被引:1,自引:0,他引:1
基于均匀圆阵和四阶累积量,提出了一种相干信号频率和到达角联合估计的新算法。首先,利用计算量较小的PRO-ESPRIT算法和beamspace-ESPRIT算法分别估计广义阵列响应矢量和信号的频率。然后,对广义阵列响应矢量进行模式空间变换,并利用改进的前后向线性预测方法估计出相干信号的到达角。该算法能在色噪声环境下,精确地估计出空间相干信号的频率和到达角,并且无需平滑技术和谱峰搜索,具有计算量小,参数自动配对的特点。计算机仿真结果验证了算法的有效性。 相似文献
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针对传统的二阶统计量角度估计算法在高斯色噪声环境下估计性能急剧下降甚至失效的问题,该文提出一种基于四阶累积量的单基地MIMO雷达相干分布式目标角度估计算法。首先建立单基地MIMO雷达的相干分布式目标信号模型,求取信号的四阶累积量矩阵;利用特征值分解分离出相互正交的信号子空间与噪声子空间,根据多重信号分类(MUSIC)算法原理,获得阵列的空间谱函数,通过谱峰搜索得到分布式目标的中心波达方向。该算法充分利用了四阶累积量对高斯过程的不敏感性,能够很好地抑制高斯色噪声对角度估计的影响。仿真结果证明了该算法的正确性和有效性。 相似文献
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单通道阵列采用单个通道贯续接入各阵元进行采样,与常规阵列相比,单通道阵列减少了硬件成本以及通道幅相特性不一致问题对估计性能的影响,但现有研究均未考虑单通道阵列的阵元切换时间对系统性能的影响。为填补这一空白,首先参照常规阵列,明确了单通道阵列中窄带信号的带宽限制及其与单通道阵列阵元切换时间的关系;再以单通道阵列MUSIC算法为例,同时考虑信号带宽与单通道阵列阵元切换时间对单通道阵列协方差阵特性的影响,从理论上推导了单通道阵列MUSIC算法关于扩展相对带宽的一阶估计误差。最后对单通道阵列MUSIC算法与单通道阵列空间FFT算法在不同阵元切换时间下进行仿真,对两种算法测向误差与阵元切换时间的关系进行分析,验证了理论推导结果的正确性。 相似文献
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针对常规矢量传感器MIMO雷达没有利用发射极化信息导致波达方向(DOA)估计精度较差的问题,该文提出一种克拉美罗界(CRB)最小化的发射极化优化算法。首先建立矢量传感器MIMO雷达的接收信号模型;然后分析固定发射极化矢量传感器MIMO雷达DOA估计算法的不足;接着推导任意发射极化状态下的CRB,计算最小CRB对应的极化状态;最后利用该优化极化状态采用固定极化DOA估计算法得到DOA估计。该算法的DOA估计精度高于固定极化DOA估计算法。且该算法的2维DOA估计可自动配对,发射电磁矢量传感天线位置可任意。仿真结果证明了该算法的有效性。 相似文献
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基于电磁矢量阵列孔径扩展方法的相干目标DOA估计 总被引:1,自引:0,他引:1
该文采用均匀且稀疏分布的电磁矢量矩形阵列,针对相干目标提出了一种有效的2维波达角(DOA)估计算法,该算法通过增加相邻阵元的间隔来扩展阵列的有效孔径,从而提高算法的DOA估计性能。论文首先结合极化平滑算法和传播算子方法得到存在相位周期性模糊的方向余弦估计。为了解决模糊性问题,论文通过协方差矩阵平滑提出一种新的解相干预处理算法,由该算法得到的信号子空间包含矢量阵元的导向矢量,且不存在相位模糊,利用此特点实现去模糊处理,得到目标的DOA估计。仿真结果表明,与基于ESPRIT的孔径扩展算法相比,提出的算法能够实现相干目标的DOA估计,同时无需特征值或奇异值分解,有更低的运算量。 相似文献
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Nested array enables to enhance localisation resolution and achieve under-determined direction of arrival (DOA) estimation. In this paper, we improve the traditional nested planar array to achieve more degrees of freedom (DOFs) and better angle estimation performance. The closed-form expressions for sensor positions of the improved array are given and the optimal array configuration for largest available DOFs is derived. Meanwhile, a computationally efficient DOA estimation algorithm is proposed. Specifically, we utilise two dimensional Discrete Fourier Transform (2D DFT) method to obtain the coarse DOA estimates; Subsequently, we achieve the fine DOA estimates by 2D spatial smoothing multiple signals classification (SS-MUSIC) algorithm. The proposed algorithm enjoys the same estimation accuracy as SS-MUSIC algorithm but with lower complexity because the coarse DOA estimates enable to shrink the range of spectral search. In addition, estimation of the number of signals is not required by 2D DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm. 相似文献
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We consider the problem of direction of arrival (DOA) estimation in the presence of multipath propagation. The sensor elements are assumed to be linear and uniformly spaced. To perform DOA estimation, we combine two existing algorithms which are often used for other purposes. The first algorithm exploits fourth-order cumulants to perform blind estimation of the steering vectors and the second algorithm estimates the directions of arrival using the estimated vectors. We refer to this two step approach as the SV-DOA estimation algorithm. We also present an algorithm independent performance analysis for the DOA estimation problem based on fourth-order cumulants. We summarize the algorithms and present Monte-Carlo simulations demonstrating the effectiveness of the SV-DOA algorithm as well as verifying the performance analysis using an optimal (but computationally expensive) DOA estimation algorithm 相似文献
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Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays 总被引:2,自引:0,他引:2
We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise. 相似文献
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Jianfeng Li Defu Jiang Feng Wang 《Multidimensional Systems and Signal Processing》2018,29(4):1397-1410
Direction of arrival (DOA) estimation for sparse nested MIMO radar with velocity receive sensor array is studied, and an algorithm based on extended unitary root multiple signal classification (MUSIC) is proposed. The nested MIMO radar utilizes sparse transmit array and velocity receive array with nested inter-element distances, which can make the final virtual array to be a long and sparse velocity sensor array. After exploiting unitary transformation to transform the data into real-valued one, an extended root MUSIC based method is developed to decompose the angle estimation into high-resolution but ambiguous and low-resolution but unambiguous DOA estimations, which are automatically paired. Thereafter, the ambiguous estimation is used to recover all possible DOAs, and the unambiguous DOA estimation is used as a reference to resolve the estimation ambiguity problem. Compared to conventional methods, the proposed algorithm requires no peak search, maintains larger aperture and achieves better DOA estimation performance. The simulation results verify the effectiveness of our approach. 相似文献