基于K-矩阵的捷联惯导系统初始四元数提取

捷联惯导系统的姿态算法是其导航算法的关键环节,应用四元数来描述运载体姿态的变化在实际工程中应用广泛,其中的初始四元数提取精度直接影响到导航解算精度。针对这个问题,在传统提取算法的基础上,得到了基于K-矩阵的初始四元数提取算法,并对正交和非正交初始姿态矩阵的四元数提取进行了对比分析。初始姿态矩阵算例的提取验证表明,基于K-矩阵的提取算法运算量较小,且对于非正交姿态矩阵能有效提高四元数提取的精度,为改进姿态解算的初始四元数提取算法提供了参考。     

机动状态下航姿系统姿态估计算法研究

载体机动时,低精度航姿系统无法准确敏感重力矢量,由加速度计输出计算的水平姿态误差较大.为了减弱运动加速度的影响,提高机动状态下水平姿态估计精度,提出一种自适应卡尔曼滤波水平姿态估计算法.以姿态四元数和陀螺漂移为状态量,四元数姿态更新微分方程为状态方程,加速度计输出为量测量建立伪量测方程.根据加速度计量测更新残差对量测噪声方差阵进行实时估计和动态调节,较好地解决了机动状态下航姿系统水平姿态估计问题,提高了水平姿态估计精度.跑车试验验证了算法的有效性.     

四元数算法在姿态矩阵解算中的研究

SINS是直接利用计算机来模拟载体的姿态矩阵,然后根据模拟出的姿态矩阵解算出载体的姿态和航向信息。姿态矩阵的计算是捷联惯导算法中最重要的一部分,也是捷联式系统所特有的。所以,对于姿态矩阵的研究是非常必要的,文章以四元数算法为基础,主要研究整体-四元数和分解-四元数算法。通过建立线性化误差模型,将这两种算法的精度进行比较分析和仿真对比,结果表明分解-四元数算法比整体-四元数算法精度更高。     

基于最优四元数估计的摇摆基座粗对准技术

针对摇摆基座的粗对准问题,提出了一种精度更优的粗对准方法。根据三维坐标矢量与旋转四元数间的内在关系,基于Wahba问题的求解原理,将双观测矢量的最优姿态阵求取问题归结为一个典型三角函数最大值的求解问题。阐述了Wahba问题的求解原理,分析了Wahba问题与最优四元数估计法的关系,剖析了最优四元数估计算法的复共线性,设计了基于最优四元数估计的摇摆基座粗对准方案,并与传统TRIAD算法和最优TRIAD算法进行了应用比较。蒙特卡洛500个样本的仿真结果表明,采用基于最优四元数估计的粗对准法的方位姿态估计精度远优于TRIAD算法和最优TRIAD算法,能使方位失准角的变化幅值控制在角分级,在此基础上能更好实现摇摆基座下惯导系统精对准。     

基于四元数的四轴无人机姿态的估计和控制

介绍一种只需少量计算就可实现四轴无人机姿态的估计和控制的新颖方法。整个算法采用四元数表示,四元数运算基于惯性测量单元(IMU)的加速度和陀螺仪传感器。姿态估计使用四元数代替三角函数,允许用加速度数据分析推导和优化梯度下降算法来减少由陀螺仪传感器产生的误差。姿态控制将当前姿态与目标姿态的姿态误差反馈到PD控制器来实现想要的目标姿态。姿态估计中用到的梯度下降算法通过四元数对应的欧拉角的均方误差验证,静态均方误差小于0.945°。实际的飞行测试可以实现一个稳定的飞行,同时验证了姿态控制算法。     

双矢量定姿在煤矿掘进机姿态测量中的应用

针对现有巷道掘进机的姿态测量手段中普遍存在的高成本和误差累积等问题,文中提出了一种基于双矢量定姿原理的姿态测量算法。通过在巷道掘进环境中分别对重力矢量和光矢量进行构建与感知,利用惯性倾角测量和双目视觉测量技术,基于矢量元素分别在导航坐标系和掘进机载体坐标系中的数学表达,可实现掘进机载体坐标系相对于巷道导航坐标系的姿态解算。利用倾斜仪和双目相机组成的测量装置对指示激光和重力矢量进行测量,结合双矢量定姿算法即可完成掘进机机身的姿态解算。文中设计了静态重复性精度测量实验,结果表明该方法的姿态角重复性测量精度为0.066 2°。利用蒙特卡洛方法对可能会引入的误差源进行仿真分析,结果表明误差对方位角、俯仰角以及滚转角的影响分别为0.786 4°、0.454 8°和0.476 5°。     

降维算法设计低阶线性Kalman滤波器

基于四元数微分方程,采用四阶龙格-库塔法处理输出数据,建立了递推关系式,并以四元数作为系统的状态矢量,构成一种四维Kalman滤波器。最后,在DM3730 Cortex-A8处理器平台上对算法进行了实现和实验验证。实验结果表明,经过降维处理后的算法,显著降低了算法的实现复杂度和计算量,滤波效果及实时性能均达到了预期的目标,具有较强的实用性。     

一种适用于大型阵列的递推干扰抑制算法

天线阵列在雷达与卫星通信中有着广泛的应用,它可以通过波束形成来抑制干扰.对于具有较多阵元数的大型阵列,普通的波束形成算法计算量很高.本文给出一种适用于大型阵列的递推干扰抑制算法,它是在波束空间分解最大信干噪比准则下基于干扰矢量进行变维递推处理,所需递推步数为干扰数,且每步递推中数据矩阵的维数很小,因而所需的总计算量较低.阵元数越多该递推算法的计算效率越高.仿真结果表明,该递推算法可以有效实现干扰抑制.     

微型姿态方位参考系统的设计

微型姿态方位参考系统基于stm32单片机和MEMS传感器的实时姿态测量系统。由stm32通过iic总线采集陀螺仪、加速度计、电子罗盘原始数据,利用互补滤波算法进行数据融合,采用四元数进行姿态更新并通过串口或spi总线实时输出姿态数据。     

基于STM32的姿态测量系统设计

以无人机航姿测量系统小型化、量轻化为背景,设计了一种基于微惯性单元MEMS的姿态测量系统。系统以STM32F103C8T6为主控制器,通过I2C总线分别采集惯性测量单元MPU6050和数字罗盘HMC5883L的测量数据,利用数据融合算法解算无人机当前姿态。对于MEMS温漂和噪声干扰的问题,提出了一种基于四元数的互补滤波算法,对测量的姿态数据进行补偿修正。实验结果表明,该姿态测量系统简单可靠、性能稳定、精确度高,成功完成了姿态的最优控制。     

A new block-based motion estimation algorithm

The conventional motion estimation algorithms used in digital television coding can roughly be classified into two categories, namely the block-matching method and the recursive method. Each of them has its own strong points. In this paper, a new type of block-based motion estimation algorithm is presented, which is based on the block-recursive (gradient) method and makes use of some of the merits of the block-matching method. For a moderate translational motion, motion estimation with a subpel precision can conveniently be obtained with only a couple of recursive searches, and for a violent or complicated motion which cannot be estimated by any block-based algorithm, the local minimum of prediction errors can always be found. Our experiments show that the proposed algorithm is efficient and reliable, and obviously superior to the conventional block-recursive algorithms and the fast block-matching algorithms. The performance of the proposed algorithm tends almost to the optimum of the full search algorithm with the same estimation precision, but the computational effort is much less than that of the full search algorithm.     

EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods     

Givens rotation based least squares lattice and related algorithms

The author presents a general and systematic approach for deriving new LS (least squares) estimation algorithms that are based solely on Givens rotations. In particular, this approach is used to derive efficient Givens-rotation-based LS lattice algorithms-the Givens-lattice algorithms. By exploiting the relationship between the Givens algorithms and the recursive modified Gram-Schmidt algorithm, it is shown that the time and order update of any order-recursive LS estimation algorithm can be realized by employing only Givens rotations. Applying this general conclusion to LS estimation of time-series signals results in the Givens-lattice algorithms. Two Givens-lattice algorithms, one with square roots and the other without, are presented. It is shown that the Givens-lattice algorithms are computationally more efficient than the fast QR algorithm of Cioffi (1987). The derivation of other Givens rotation-based LS estimation algorithms and their systolic array implementations are discussed     

Low Complexity Linear Channel Estimation for MIMO Communication Systems

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.     

测量噪声背景下微弱正弦信号参数估计的互功率谱方法

本文首次把近代谱估计方法引入到互谱估计中，从理论上证明了互相关函数的Ｙｕｌｅ－Ｗａｌｋｅｒ方程，并在此基础上提出了互谱参数谱估计的矩估计方法和Ｌｅｖｉｎｓｏｎ递推估计方法。该方法可以有效地克服传统的互谱ＦＦＴ算法和互周期图法存在的谱分辩率低，谱估计方差大等缺点。文中还给出了信噪比为－３０ｄＢ的正弦信号参数估计的仿真实例。     

A class of recursive maximum-likelihood algorithms

This letter features the derivation of a new recursive maximum-likelihood (RML) algorithm. A simplified version of this algorithm is the same as the RML algorithm proposed in [1]. The relative performance of these algorithms is studied with the help of an example in spectral estimation.     

A fast recursive algorithm for the maximum likelihood estimation ofthe parameters of a periodic signal

Describes a new recursive algorithm for the estimation of the parameters of a periodic signal in additive Gaussian white noise. These parameters are the period, and the complex amplitudes of the harmonics present. The proposed algorithm is based on recursive maximum likelihood (ML) algorithms for incomplete data as described by Titterington (1985) and others. These algorithms are of complexity O(NM), where N is the number of harmonics, and M is the signal length. The performance of the method is compared to that of the extended Kalman filter with the aid of simulations     

Recursive Parameter Estimation Algorithms and Convergence for a Class of Nonlinear Systems with Colored Noise

Parameter estimation is important for controller design of linear systems and nonlinear systems. The parameters of the systems can be estimated through some identification algorithms. This paper presents a recursive generalized extended least squares algorithm and a generalized extended stochastic gradient (GESG) algorithm for identifying the parameters of a class of nonlinear systems. Furthermore, a multi-innovation GESG algorithm is derived to improve the estimation accuracy. The simulation example is provided to test the effectiveness of the proposed algorithms.     

QR factorization based blind channel identification withsecond-order statistics

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the received signal. In this paper, we address new algorithms based on QR factorization of the received signal directly without calculating the correlation matrix. This renders the QR factorization-based algorithms more robust against ill-conditioned channels, i.e., those channels with almost common zeros among the subchannels. First, we present a block algorithm that performs the QR factorization of the received data matrix as a whole. Then, a recursive algorithm is developed based on the QR factorization by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithms are computationally more efficient. The computation in each recursion of the recursive algorithm is on the order of O(m²) if only equalization is required, where m is the dimension of the received signal vector. Our recursive algorithm preserves the fast convergence property of the subspace algorithms, thus converging faster than other adaptive algorithms such as the super-exponential algorithm with comparable computational complexities. Moreover, our proposed algorithms do not require noise variance estimation. Numerical simulations demonstrate the good performance of the proposed algorithms     

Recursive Relations of the Cost Functions for the Least-Squares Algorithms for Multivariable Systems

In this paper, we focus on the recursive computation of the cost functions for the least-squares-type algorithms for multivariable linear regressive models. It is shown that the proposed recursive computation formulas for the cost functions can also be extended to the estimation algorithm of the multivariable equation error models, e.g., the controlled autoregressive models. The simulation results indicate that the proposed algorithm is effective.