自回归状态空间模型下时变声速剖面跟踪方法

讨论了一种适用于浅海的时变声速剖面跟踪方法。该方法将时变水体声速剖面的反演问题建模为由描述声速剖面时变特性的状态方程与包含声压场局部测量信息的测量方程组成的状态-空间模型,提出利用自回归分析拟合方法将声速场扰动建模为高阶自回归演化模型,并通过集合卡尔曼滤波序贯地估计时间演化的海洋声速场。利用2001年亚洲海实验环境与声速测量数据,仿真分析了基于高阶自回归演化模型的时变声速剖面集合卡尔曼滤波估计方法。结果表明,相比于利用传统随机游走状态演化模型的估计方法,该改进方法可有效降低声速的跟踪误差,并且在较低信噪比条件下仍具有较好的跟踪性能。     

温盐深剖面仪与Argo数据的冬季印度洋声速剖面分析

通过某一次在东南印度洋调查工作中获得的17站位温盐深剖面仪(CTD)数据,因其表层0～10 m数据缺失,选择利用卫星遥感的海表面温度产品与中国Argo实时资料中心提供的全球海洋Argo网格数据集(BOA_Argo)中逐年月平均数据做比较,发现BOA_Argo数据集反推得到的表层温度是比较准确的.再将CTD数据与对应时间...     

信号在时变等离子体中的传播特性

高速飞行器等离子鞘套由于飞行姿态调整、湍流、非均匀烧蚀等因素的影响,使其等离子体参数存在时变特性,针对这种传输介质的快速时变特性引起的电波幅度、相位上的寄生调制效应,本文利用大面积辉光放电等离子体产生装置,搭建了等离子体中信号传输实验系统,进行了S频段的单频信号与调制信号传输实验,观测验证了调制效应的存在,且其调制频率与等离子体变化频率具有一致性,进一步分析了等离子参数与寄生调制效应的关系,理论和实验结果表明:即使当载波频率大于等离子频率时,时变等离子引起的寄生调制效应也会使传输信号的星座图发生旋转,造成其判决容差裕度变小,通信可靠性下降,并且载波频率越接近等离子频率时,其寄生调制效应越强烈。     

单片机在测量声速实验中的应用

单片机在测量声速实验中的应用郭立群，李将录，万新（哈尔滨师范大学物理系１５００８０）（哈尔滨塑料十二厂）一、引言目前各级各类学校的普通物理实验课普遍开设声速测量实验．声速测量的一般方法有行波法、驻波法、共鸣法等间接测量方法．这些方法都很巧妙，但误差较...     

扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演. 在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程. 在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验. 实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证. 关键词： 大气波导 雷达回波 扩展卡尔曼滤波 不敏卡尔曼滤波     

卡尔曼滤波在激光跟踪测量系统中的应用

激光跟踪测量系统对于测量运动目标空间位置是行之有效的,但在测量过程中,各种干扰噪声的影响会降低测量精度。采用卡尔曼滤波来减小噪声的影响以提高测量精度。介绍了激光跟踪测量系统,建立了状态方程和测量方程,给出了卡尔曼滤波算法,仿真结果表明,运用卡尔曼滤波大大提高了测量系统的精度。     

导数卡尔曼滤波法在多组分体系中的应用

采用卡尔曼滤波法、导数卡尔曼滤波法对一含有3种稀土元素与间乙酰偶氮氯膦显色的混合体系光谱进行剖析,并对此2种滤波法的解析结果作比较,结果表明,在本体系中,导数卡尔曼滤波法的定性、定量效果明显优于卡尔曼滤波法。     

探究性学习在声速测量实验课中的运用

为了进一步提高大学物理实验课堂教学的质量,根据探究性学习的特点,构建声速测量实验课的实验教学过程,采用创建探究方向、拓展探究方向和加大探究自由度及激励机制,从而提高了大学物理实验课堂教学的自主性、实践性、开放性和激励性。     

线性加速度计在压电陀螺卡尔曼滤波技术中的应用

针对光电稳定平台常用的压电陀螺随机游走噪声大的缺点,提出采用基于线性加速度计的卡尔曼滤波技术对其进行信号滤波。利用卡尔曼滤波理论,建立了压电陀螺角速率状态观测方程,采用线性加速度计测量平台惯性角加速度,由此对陀螺信号进行了滤波。实验结果表明：采用线性加速度计能够在不影响陀螺带宽的前提下将压电陀螺的随机游走噪声水平由原有的0.005（°）.s-1/槡Hz降低到0.001 25（°）.s-1/槡Hz,提高了光电平台的稳定精度。     

线性加速度计在压电陀螺卡尔曼滤波技术中的应用

针对光电稳定平台常用的压电陀螺随机游走噪声大的缺点,提出采用基于线性加速度计的卡尔曼滤波技术对其进行信号滤波。利用卡尔曼滤波理论,建立了压电陀螺角速率状态观测方程,采用线性加速度计测量平台惯性角加速度,由此对陀螺信号进行了滤波。实验结果表明:采用线性加速度计能够在不影响陀螺带宽的前提下将压电陀螺的随机游走噪声水平由原有的0.005(°).s-1/槡Hz降低到0.001 25(°).s-1/槡Hz,提高了光电平台的稳定精度。     

强非线性时间演化声速剖面的序贯反演

受海面波浪起伏、降雨和内波等海洋动力学过程的影响,浅水声速剖面的时间演化具有高度非线性,针对该问题提出使用改进的粒子滤波方法进行声速剖面序贯反演.该方法通过建立声速剖面的经验正交模型(EOF)以及描述声速剖面时间演化特征的状态空间模型,将声速剖面反演问题建模为状态跟踪问题,利用不敏粒子滤波(UPF:Uncented Particle Filter)算法进行声速剖面序贯反演。仿真试验通过实测声速剖面数据和先验地声参数信息产生接收声场数据,再利用模拟声场数据估计声速剖面的时间变化.结果表明,相比于集合卡尔曼滤波(EnKF:Ensemble Kalman Filter),在计算效率等同的情形下,该方法可以在状态参数的时间跳变点保持良好的跟踪性能,一定程度上克服了现有反演算法在跳变点发散的问题,可以有效提高声速剖面反演精度,尤其在声速剖面时变性较强时具有显著优势.     

Tracking of time-evolving sound speed profiles with an auto-regressive state-space model

An approach for time-evolving sound speed profiles tracking in shallow water is discussed.The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem,which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements.In the paper,auto-regression(AR) method is introduced to obtain a high-order AR evolution model of the sound speed field time variations,and the ensemble Kalman filter is utilized to track the sound speed field.To validate the approach,the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data.Compared with traditional approaches based on the state evolution represented as a random walk,simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed,and still keep good tracking performance at low signal-to-noise ratios.     

Robust tracking of moving sound source using multiple model Kalman filter

In this paper a novel method for tracking an active speaker in a noisy and reverberant environment by means of a spatially distributed microphone array is presented. Firstly, a sound source localization algorithm based on time delays of arrival (TDOA) in microphone pairs provides observed position estimates. Then these remarkably noisy estimates are filtered by a multiple model Kalman filter (MMKF) in order to obtain a smoothed trajectory of the speaker’s movement. Compared with the traditional Kalman filter (KF), simulated results prove the MMKF is more robust and effective in noisy environments.     

浅海时变声速环境下的自适应匹配场定位算法实现*

针对浅海环境下声速剖面失配引起的匹配场处理器失配问题,提出了一种自适应匹配场定位算法在声速剖面时变环境下的实现方式。将先验声速剖面集简化为经验正交函数表示,结合蒙特卡洛方法与环境扰动约束算法对当下时刻的目标声源进行匹配场定位。本文以某次试验获取的连续20小时的声速剖面数据为研究对象,通过仿真试验对该算法进行验证,结果表明：在先验声速剖面集的半小时之后,利用自适应算法的距离和深度定位成功率较常规匹配场算法有较大提升,其中,深度正确定位概率相对较低。     

A generalized polynomial chaos based ensemble Kalman filter with high accuracy

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.     

基于广义M估计的鲁棒容积卡尔曼滤波目标跟踪算法

为减小测量异常误差对非线性目标跟踪系统的影响, 提出了一种基于广义M估计的鲁棒容积卡尔曼滤波算法. 首先将非线性测量方程等价变换, 利用约束总体最小二乘准则构建广义M估计极值函数, 在不进行线性化近似的前提下将其引入到容积卡尔曼滤波求解框架中. 然后根据Mahalanobis距离构建异常误差判别量, 利用卡方分布的置信水平确定判决门限, 并建立改进的三段Huber权函数, 使其能够降低小异常误差权值, 剔除大异常误差. 理论分析表明, 该方法具有无需求导、跟踪精度高、实时性好等优点, 且无需已知异常误差的统计特性; 实验结果表明, 所提算法能够有效减小异常误差的影响, 在实际非线性物理系统中具有广阔的应用空间.     

基于集合卡尔曼变换的东中国海声学 敏感区判定方法*

为改善海洋与水声环境预报质量,针对常规观测成本高、资料利用率低等问题,将适应性观测方法应用于海洋声学领域。结合海洋-声学耦合模式与集合卡尔曼转换敏感区诊断方法,以东中国海宫古海峡北部区域为验证区,计算并分析不同条件下海洋环境敏感区与声学敏感区分布,通过观测系统模拟试验验证适应性观测对验证区预报的提升效果。结果表明,两种敏感区位置随时间间隔增加均向验证区上游移动,海洋环境敏感区相比于声学敏感区分布更为集中,且平移特征更明显;对海洋环境敏感区和声学敏感区添加适应性观测均能提升海洋与水声环境的预报质量,提升效果随时间间隔增加而减小,在某种类型敏感区的适应性观测对相对应参数的预报质量提升效果优于对其他类型敏感区进行观测的效果。     

Determination of equivalent sound speed profiles for ray tracing in near-ground sound propagation

The determination of appropriate sound speed profiles in the modeling of near-ground propagation using a ray tracing method is investigated using a ray tracing model which is capable of performing axisymmetric calculations of the sound field around an isolated source. Eigenrays are traced using an iterative procedure which integrates the trajectory equations for each ray launched from the source at a specific direction. The calculation of sound energy losses is made by introducing appropriate coefficients to the equations representing the effect of ground and atmospheric absorption and the interaction with the atmospheric turbulence. The model is validated against analytical and numerical predictions of other methodologies for simple cases, as well as against measurements for nonrefractive atmospheric environments. A systematic investigation for near-ground propagation in downward and upward refractive atmosphere is made using experimental data. Guidelines for the suitable simulation of the wind velocity profile are derived by correlating predictions with measurements.