SINS/视觉组合导航系统融合算法

捷联惯性导航系统(SINS)/视觉组合导航系统的融合算法主要是卡尔曼滤波,卡尔曼滤波实现最优估计的前提是系统的模型必须准确已知。对于SINS/视觉组合导航系统,获取量测信息需经图像处理、特征点提取和匹配等过程,使量测噪声统计模型不完全可知,这会导致卡尔曼滤波器的估计精度下降。因此,该文提出一种改进的自适应两级卡尔曼滤波,根据求解遗传因子的不同方法对传统自适应两级卡尔曼滤波进行改进。改进后的算法分别适用于系统噪声统计模型和量测噪声统计模型不准确可知两种情况,且二者具有统一的滤波框架。仿真结果表明,改进的自适应两级卡尔曼滤波比卡尔曼滤波精度高,有效解决了SINS/视觉组合导航系统因噪声统计模型不准确导致的精度下降问题。     

模糊自适应滤波在捷联惯导初始对准中的应用

传统卡尔曼滤波应用于捷联惯导初始对准中由于模型参数、噪声的统计特性不确定,影响估计效果.而模糊自适应卡尔曼滤波能按照模糊推理原理逐步校正系统的观测噪声协方差阵,具体实现是通过观察残差的理论值是否接近于其实际值,系统调整观测噪声协方差的加权以达到修正观测噪声协方差阵的目的,进而提高系统的对准效率.在噪声统计特性未知时,比较了常规卡尔曼滤波与模糊自适应卡尔曼滤波在初始对准中的应用效果.仿真结果表明,这种算法能有效提高系统的滤波效果,是一种较理想的初始对准滤波方法.     

The research on channel estimation and signal-noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

In this paper, the channel estimation and signal-to-noise ratio (SNR) estimation technique of single-carrier frequency domain equalization (SC-FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE-KF) are proposed. This paper first introduces the SC-FDE system and introduces the principle of MEE-KF, and then, the channel estimation flow based on MEE-KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE-KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC-FDE system has also been greatly improved.     

LTE中一种基于探测参考信号的信噪比估计算法

为了在上行链路支持频率选择性调度,长期演进(LTE)系统定义了探测参考信号(SRS)用于信道质量估计。该文主要研究SRS的信噪比估计方法,针对Boumard方法和传统DFT方法的缺点,提出一种改进的基于DFT的估计方法。该方法通过在时域修正噪声的估计区间,减小高信噪比时有用信号能量泄露对噪声估计的影响,从而获得更准确的信噪比估计。仿真结果表明,所提方法的估计性能优于Boumard方法和传统的DFT方法,提高了高信噪比时的估计精度,在高信噪比区域,平均估计性能提高了约6 dB以上。     

改进的平方根容积卡尔曼滤波及其在POS中的应用

为解决扩展卡尔曼滤波在处理复杂非线性状态估计时,存在收敛速度慢、估计精度低及数值稳定性差等问题,引入一种改进的平方根容积卡尔曼滤波算法(A-SRCKF)。该算法在容积卡尔曼滤波基础上引入矩阵QR分解、Cholesky分解因数更新等技术,避免了矩阵分解、求逆及求导等复杂运算,极大降低了计算复杂度;并针对系统时变及统计特性未知情况下量测噪声协方差阵难以获取问题,通过引入自适应噪声估计器并结合小波卡尔曼滤波思想,构造出加权量测噪声协方差阵,提高了数值精度及稳定性。将A-SRCKF应用于机载定姿定位系统中,仿真结果表明:该算法有效地提升了估计精度,并且运行速度较快。     

基于最优导频的自适应Kalman滤波信道估计方法

针对时变信道中的子载波间干扰(ICI)和噪声的统计模型不准确引起的滤波发散问题,介绍了一种基于最优导频预滤波的自适应Kalman联合算法。该算法通过使用最优导频滤除ICI,获得理想信道初始状态,然后将其作为Kalman滤波初始信息在时域上进行自适应Kalman信道估计。最后仿真实验表明,和传统的基于导频的Kalman滤波(KF)算法相比,该方法能有效抑制KF发散和改善信道估计精度。     

基于协方差加权的卡尔曼滤波融合跟踪算法

针对卡尔曼滤波融合跟踪对系统模型准确度和先验信息精度要求较高的问题,提出一种基于协方差加权的卡尔曼滤波融合方法,利用最小二乘准则作为误差加权的标准,使误差小的传感器加权因子大。基于此,再利用卡尔曼滤波融合,充分保留有用信息,抑制噪声干扰。在目标跟踪应用中,即使噪声统计信息未知且噪声互相关,利用该方法仍能够获得最小均方误差准则下的最优目标状态跟踪估计。     

卡尔曼滤波在雷达偏差估计应用中的改进

为实时跟踪整个雷达网内各雷达的方位偏差和测距偏差的幅度变化,可利用卡尔曼滤波数据存储量小、便于实时处理的特点,用卡尔曼滤波算法对雷达方位偏差和测距偏差幅度进行实时跟踪,并在实际处理的过程中改进噪声计算方法,克服噪声线性化的问题,对实时估计雷达方位偏差和测距偏差非常有效。通过MATLAB仿真得到验证,改进后的雷达方位偏差和测距偏差的估计值比改进前更精确,并且收敛速度提高了2倍,结果表明,此方法既简单又快速、估计值更精确。     

Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks

The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.     

The use of estimation techniques to reduce noncoherent polarimetricmeasurement errors

Polarimetric measurements contain errors owing to random thermal energy in the receiver and system phase noise. These errors can be reduced using estimation techniques that account for general properties of the Mueller matrix. In this paper, we show how Kalman (1960) filter techniques improve and speed up the estimation of a target's Mueller matrix when the noncoherent measurement technique is used. Measurements obtained with a 95-GHz polarimeter demonstrate that fewer independent samples of the backscattered fields are needed to obtain an accurate estimate if Kalman filtering is used than if a pseudo inversion process is used     

基于SC-FDMA系统的噪声估计算法研究

为了提高单载波频分多址接入(SC-FDMA)系统的性能,一种简单且有效的基于探测参考信号(SRS)的噪声估计算法是必要的.针对传统基于离散傅里叶变换(DFT)算法的缺陷,提出了一种改进的基于DFT的算法.另外,在该改进的基于DFT的算法的基础上,又通过增加汉宁窗进行修正,减小了高信噪比下信号能量的泄露.仿真结果表明,在低信噪比下,改进的基于DFT的算法的性能相比传统的算法性能上有4 dB的改善.但是,在高信噪比下,改进的基于DFT的算法的性能逐渐变差,而通过添加汉宁窗却能修正这一缺陷,使其性能得到至少4 dB的改善.     

Speech Enhancement Combining Optimal Smoothing and Errors-In-Variables Identification of Noisy AR Processes

In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests.     

Progressive iterative channel estimation in fast time-varying OFDM systems

Considering the joint channel estimation and data detection in time-varying orthogonal frequency division multiplexing （OFDM） and addressing transmission performance degradation induced by the severe inter-carrier interference （ICI） at very high speed, a new progressive iterative channel estimation scheme is proposed. To alleviate the error propagation of the inaccurate data due to ICI, the measurement subcarriers in the Kalman filter is designed to be extended from pilots subcarriers to all the subcarriers progressively through the iterations. Furthermore, in iteration process, the interference of the non-pilot data to the measurement subcarriers is considered to be part of noise in the modified Kalman filter, which improves the estimation accuracy. Simulation indicates that the proposed scheme improves the performance in fast time-varying situation.     

基于自适应无迹粒子滤波的目标跟踪算法

为解决复杂场景中目标跟踪问题,提出了一种噪声未知情况下的自适应无迹粒子滤波(A-UPF)算法。算法采用改进的Sage-Husa估计器对系统未知噪声的统计特性进行实时估计和修正,并与无迹Kalman粒子滤波器相结合产生优选的建议分布函数,降低系统估计误差的同时有效提升了系统的抗噪声能力。实验结果表明,本文方法对于复杂条件下的目标跟踪问题具有较高的精度和较强的鲁棒性。     

基于小波变换的分形随机信号的卡尔曼滤波

本文基于多尺度卡尔曼滤波方法来估计淹没在加性高斯白噪声中的分形布朗运动.针对每一尺度,给出了相应的动态系统参数和运动模型方程以及更精确的估计算法.并与多尺度维纳滤波进行了对比,计算机仿真结果证明了其优越性.     

机载有源无源传感器稳健融合方法

在机载有源无源情报融合处理中,很多条件很难满足中心式滤波的要求,比如一般情况下过程噪声是不正确的,或者不知道的.给出了几种稳健的Kalman滤波,这些方法主要针对运动方程或者观测方程中噪声信息未知或者噪声信息不准确的情况下,对数据进行融合.如果只是过程的信息未知,提出的自适应滤波,即是用观测信息对运动方程的噪声进行实时估计;如果运动方程和观测方程的噪声不准确,则可以采用H∞滤波进行噪声误差方差估计.     

动力型锂电池SOC与SOH协同估计

锂电池及其应用近年来逐渐成为研究热点。以提高电池管理系统(BMS)对电池荷电状态(SOC)和健康状态(SOH)的估算精确度为目标,在建立二阶Thevenin等效电路模型基础上提出一种能在线协同估算电池荷电状态和健康状态的改进扩展卡尔曼滤波算法。通过分阶段脉冲放电实验,并利用最小二乘法求得模型参数。在动态应力测试工况(DST)下借助Matlab对比分析了改进扩展卡尔曼算法在SOC和SOH估计精确度、错误初值时算法收敛性、算法复杂度等方面的性能。实验表明,利用该算法可以精确估计出各采样点处的SOC和SOH,误差低于1%;且在初值不准确情况下,运行算法可快速收敛至真值附近,算法估算结果的准确性与模型参数的微调无关,鲁棒性较好。     

Iterative and sequential algorithms for multisensor signalenhancement

In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. The authors specifically consider the two-sensor signal enhancement problem in which the desired signal is modeled as a Gaussian autoregressive (AR) process, the noise is modeled as a white Gaussian process, and the coupling systems are modeled as linear time-invariant finite impulse response (FIR) filters. The main approach consists of modeling the observed signals as outputs of a stochastic dynamic linear system, and the authors apply the estimate-maximize (EM) algorithm for jointly estimating the desired signal, the coupling systems, and the unknown signal and noise spectral parameters. The resulting algorithm can be viewed as the time-domain version of the frequency-domain approach of Feder et al. (1989), where instead of the noncausal frequency-domain Wiener filter, the Kalman smoother is used. This approach leads naturally to a sequential/adaptive algorithm by replacing the Kalman smoother with the Kalman filter, and in place of successive iterations on each data block, the algorithm proceeds sequentially through the data with exponential weighting applied to allow adaption to nonstationary changes in the structure of the data. A computationally efficient implementation of the algorithm is developed. An expression for the log-likelihood gradient based on the Kalman smoother/filter output is also developed and used to incorporate efficient gradient-based algorithms in the estimation process     

基于自适应卡尔曼滤波的捷联去耦算法

针对捷联相控阵雷达导引头中弹体姿态干扰弹目视线角速率提取的问题,提出了基于自适应卡尔曼滤波去耦算法,引入合适的遗忘因子优化了滤波的性能,建立了噪声特性递推和预测的数学模型,联立滤波方程和噪声估计方程解决了弹目视线角速率去耦的问题,在误差的允许的范围内提取了弹目视线角速率。最后通过仿真实验表明所提算法在捷联去耦上的有效性以及相对于标准卡尔曼滤波去耦的优良性,提高了提取弹目视线角速率的精度,优化了导弹制导性能,具有较高的工程运用价值。