共查询到20条相似文献，搜索用时 234 毫秒
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文中提出当信源为非圆信号时,基于特征矢量稀疏分解进行DOA估计;并在稀疏恢复过程中,比较空间范数变化对误差的影响.该方法对协方差矩阵进行了扩展,在利用L曲线方法自适应得到正则化参数的同时,对空间范数应用进行了推广.不仅提高信息利用率,能够处理相干信号源,而且不需要已知信号源数目,性能优于平滑处理过后的NC-MUSIC算法. 相似文献
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自适应调零天线被广泛应用在卫星通信和舰载雷达通信等领域,对调零天线的DOA均衡处理是保证在高信噪比下实现波达方向估计和信道均衡的关键技术。传统方法采用功率谱估计算法实现对调零跳线的DOA均衡处理,算法在强干扰环境下均衡效果不好。提出一种基于空间谱瞬时值聚类的调零天线DOA均衡算法。首先进行调零天线中的自适应滤波处理和信号模型构建,基于自适应滤波算法估计误差自动调整滤波器参数,计算接收信号的协方差矩阵,接收信号同参考信号的互相关矢量分别满足系统Wiener解。进行调零天线期望信号的空间谱瞬时值信息特征提取和聚类算法设计,实现调零天线的自适应滤波的DOA均衡算法改进设计。仿真结果表明,该算法具有较好的码间抗干扰性能,收敛速度和精度上相比传统算法有了较大提升,提高了调零天线的波达信号识别能力,DOA均衡处理性能提高,展示了较好的应用价值。 相似文献
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对传统的波束域波束形成进行改进,提出一种改进的阵元域波束形成算法检测网络纠缠入侵信号。把传统的波束域旋转矢量的变换到阵元域中改善阵元域自适应算法的性能,利用纠缠入侵信号的特征值大于噪声的特征值这一性能,采用空间协方差矩阵逆的高阶次幂来逼近信号子空间,将求得权矢量投影于改进的阵元域的特征信号子空间。将求得权矢量投影于改进的阵元域的特征信号子空间,实现对网络纠缠入侵信号的检测。仿真实验表明,提出的改进的阵元域波束形成信号检测算法具有较好的自适应检测性能,计算量和信号检测稳健性有明显改善,在网络入侵检测中具有较好的工程实用价值。 相似文献
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基于GPS的动态载体定姿算法具有重要意义。其关键是整周模糊度的快速求解,但采用直接收敛法求解双差整周模糊度的初始化时间过长,在实际中难以应用。LAMBDA算法通过模糊度浮点解及其方差协方差矩阵可以有效地估计整周模糊度,但动态情况下无法直接求得浮点解的方差协方差矩阵。本文提出一种估计方差协方差矩阵的方法,使得LAMBDA算法能够有效地应用到动态姿态测量中。实际算例表明,该算法可将初始化时间缩短到原来的1/2左右,能够高效地用于实时动态姿态解算。 相似文献
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针对相干源方位估计问题,本文在PVFS(Particle Velocity Field Smoothing)算法的基础上,提出一种新的算法。该算法通过对PVFS算法构造出的协方差矩阵进行特征值分解,利用得到的特征值及特征向量构造新的噪声子空间,然后运用子空间原理实现相干源的方位估计。该算法无需已知相干源的信源数目且不会损失阵列孔径,具有较好的相干源方位估计性能,计算机仿真结果验证了本文算法的有效性。 相似文献
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高维度在许多统计学问题中是很常见的。用渐近框架来查验协方差矩阵估计,随着样本容量n的增加,维度p趋向∞。由于p和K对基于模型的协方差矩阵估计器性能的影响,在适当的假设下,证实了这种基于模型的协方差矩阵估计器的收敛速度和渐近正态性。我们将该估计器的性能与样本协方差矩阵的性能进行了对比,明确了该因子法在哪些情况下从本质上提高了或轻微地提高了其性能。 相似文献
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在多径通信环境下,常涉及到估计路径的入射角度(DOA)和相对时延(TDOA)问题。因此,提出通过估计信道响应实现信道参数估计的方法,并最终给出一种无须搜索的闭式解。该算法首先根据最小二乘法估计信道冲击响应,再对估计的信道进行傅里叶变换,将时延流型矩阵映射为一个具有Vandermonde结构的矩阵,使得信道具有双重的Vandermonde结构。最后,基于信道模型的平移不变特性,使用类ESPRIT算法求解广义特征值,直接计算出可以自动配对的入射角度和时延。仿真结果证明该算法在DOA或TDOA相距非常近时也能够得到较好的估计性能。 相似文献
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《Journal of The Franklin Institute》2019,356(7):4076-4096
This paper mainly focuses on the event-based state and fault estimation problem for a class of nonlinear systems with logarithmic quantization and missing measurements. The sensors are assumed to have different missing probabilities and a constant fault is considered here. Different from a constant threshold in existing event-triggered schemes, the threshold in this paper is varying in the state-independent condition. With resort to the state augmentation approach, a new state vector consisting of the original state vector and the fault is formed, thus the corresponding state and fault estimation problem is transmitted into the recursive filtering problem. By the stochastic analysis approach, an upper bound for the filtering error covariance is obtained, which is expressed by Riccati difference equations. Meanwhile, the filter gain matrix minimizing the trace of the filtering error covariance is also derived. The developed recursive algorithm in the current paper reflects the relationship among the upper bound of the filtering error covariance, the varying threshold, the linearization error, the probabilities of missing measurements and quantization parameters. Finally, two examples are utilized to verify the effectiveness of the proposed estimation algorithm. 相似文献
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《Journal of The Franklin Institute》2021,358(16):8754-8771
This paper is concerned with the event-based fusion estimation problem for a class of multi-rate systems (MRSs) subject to sensor degradations. The MRSs under consideration include several sensor nodes with different sampling rates. To facilitate the filter design, the MRSs are transformed into a single-rate system (SRS) by using an augmentation approach. A set of random variables obeying known probability distributions are used to characterize the phenomenon of the sensor degradations. For the purpose of saving the limited communication resources, the event-triggering mechanism (ETM) is adopted to regulate the transmission frequency of the measurements. For the addressed MRSs, we aim to design a set of event-based local filters for each sensor node such that the upper bound of each local filtering error covariance (FEC) is guaranteed and minimized by designing the filter parameter appropriately. Subsequently, the local estimates are fused with the aid of covariance intersection (CI) fusion approach. Finally, a numerical experiment is exploited to demonstrate the usefulness of the developed fusion estimation algorithm. 相似文献
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In this paper, identification of discrete-time power spectra of multi-input/multi-output (MIMO) systems in innovation models from output-only time-domain measurements is considered.A hybrid identification algorithm unifying mixed norm minimization with subspace estimation method is proposed. The proposed algorithm first estimates a covariance matrix from measurements. A significant dimension reduction is achieved in this step. Next, a regularized nuclear norm optimization problem is solved to enforce sparsity on the selection of most parsimonious model structure. A modification of the covariance estimates in the proposed algorithm generates yet another algorithm capable of handling data records with sequentially and intermittently missing values. The new and the modified identification algorithms are tested on a numerical study and a real-life application example concerned with the estimation of joint power spectral density (PSD) of parallel road tracks. 相似文献
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《Journal of The Franklin Institute》2023,360(6):4231-4246
A robust event-triggered distributed fusion algorithm is investigated in this paper for multi-sensor systems with unknown failure rates. A detection technique based on standard Gaussian distributed filtering innovation is designed and applied to judge whether the measurement is failed. This filtering innovation can also be used to construct the event-triggered condition. Specifically, the event condition is not triggered if the innovation is below the lower event-triggered threshold and the measurement is regarded as the failure measurement if the innovation exceeds the higher threshold. In the above two cases, the sensor measurement data is not transferred to the local estimator; otherwise, it will be transferred. Then, the sequential fast covariance intersection (SFCI) fusion algorithm is used for local estimation fusion. Besides, to analyze the estimation performance, sufficient conditions are given to demonstrate the boundness of the local estimation and fusion estimation covariance. Finally, a simulation example is given to show the usefulness of the presented algorithm. 相似文献
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Rodrigo P. Lemos Jonas A. Kunzler Diego F. Burgos B. Mário J. de Souza Hugo V. L. e Silva Yroá R. Ferreira Edna L. Flôres Oliver Sander 《Journal of The Franklin Institute》2019,356(6):3781-3796
SEAD method estimates the direction-of-arrival angles on an uniformly linear array based on the difference between the two largest singular values, what is called differential spectrum. Although it presented an outstanding performance, the ability to indicate the source positions was not elucidated yet. Inspired by the differential spectrum formulation we derived a total differential spectrum and found out that the matrix norm induced by the vector 2-norm of a modified spatial covariance matrix can be used to estimate the direction-of-arrival of multiple plane waves. Indeed we show that matrix norms are estimators and we propose their use instead of the singular value decomposition in SEAD-based methods. We present a general mathematical expression in order to explicit the operating principles of the proposed methods. Consequently, we were able to explain how the relation between the arriving and the search angles produces the larger peaks on the differential spectrum. To evaluate the important role played by matrix norms, a thousand experiments were carried out. They showed that the proposed approach proved to be as accurate as the previous SEAD-based methods, while providing a significant reduction on runtime. It also outperformed well-established methods like MODEX regarding the estimation error. 相似文献
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Rodrigo P. Lemos Jonas A. Kunzler Diego F. Burgos B. Mário J. de Souza Hugo V.L. e Silva Yroá R. Ferreira Edna L. Flôres Oliver Sander 《Journal of The Franklin Institute》2019,356(9):4949-4969
SEAD method estimates the direction-of-arrival angles on an uniform linear array based on the difference between the two largest singular values, what is called differential spectrum. Although it presented an outstanding performance, the ability to indicate the source positions was not elucidated yet. Inspired by the differential spectrum formulation we derived a total differential spectrum and found out that the matrix norm induced by the vector 2-norm of a modified spatial covariance matrix can be used to estimate the direction-of-arrival of multiple plane waves. Indeed we show that matrix norms are estimators and we propose their use instead of the singular value decomposition in SEAD-based methods. We present a general mathematical expression in order to explicit the operating principles of the proposed methods. Consequently, we were able to explain how the relation between the arriving and the search angles produces the larger peaks on the differential spectrum. To evaluate the important role played by matrix norms, a thousand experiments were carried out. They showed that the proposed approach proved to be as accurate as the previous SEAD-based methods, while providing a significant reduction on runtime. It also outperformed well-established methods like MODEX regarding the estimation error. 相似文献
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This paper uses the filtering technique, transforms a pseudo-linear auto-regressive system into an identification model and presents a new recursive least squares parameter estimation algorithm pseudo-linear auto-regressive systems. The proposed algorithm has a high computational efficiency because the dimensions of its covariance matrices become small compared with the recursive generalized least squares algorithm. 相似文献
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提出非齐次等式约束线性回归模型回归系数的一个新的有偏估计,即综合条件岭估计,讨论了综合条件岭估计的性质,在一定的条件下,综合条件岭估计的样本总方差、均方误差、均方误差矩阵均分别小于约束最小二乘估计的相应误差.在综合条件岭估计下,条件岭估计和条件根方估计为其特例,从而统一了条件岭估计和条件根方估计的理论. 相似文献
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《Journal of The Franklin Institute》2022,359(15):8355-8376
For linear state space model, the covariance matrix setting errors of process and measurement noise deteriorate the estimation performance of Rauch–Tung–Striebel (RTS) smoother. To address this problem, the Markov Chain Monte Carlo is utilized to sample the state vector and noise covariance matrices simultaneously in this study. The Gibbs sampler is adopted and the corresponding adaptive RTS smoother is designed. Simulation results confirm the performance of proposed smoother. 相似文献
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This paper is concerned with the distributed H∞-consensus control problem over the finite horizon for a class of discrete time-varying multi-agent systems with random parameters. First, by utilizing the proposed information matrix, a new formula is established to calculate the weighted covariance matrix of random matrix. Next, by allowing every agent to track the average of the neighbor agents, a novel local H∞-consensus performance constraint is presented to cater to the local performance analysis. Then, by means of the proposed definition of the stochastic vector dissipativity-like over the finite horizon, a set of sufficient conditions for every agent is obtained such that the controlled outputs of the closed-loop multi-agent systems satisfy the proposed H∞-consensus performance constraint. As a result, the proposed consensus control algorithm can be executed on each agent in an indeed distributed manner. Finally, a simulation example is employed to verify the effectiveness of the proposed algorithm. 相似文献