声速不确定条件下的运动水下航行器自定位

传统利用水下声学定位系统的航行器自定位存在两方面问题, 一方面没有考虑测量周期内航行器的运动, 另一方面没有考虑水声声速的不确定。为解决上述问题, 构建了航行器运动状态下的时间测量定位模型, 并对声速不确定性进行建模。分别推导了基于加权最小二乘的运动航行器定位方法和基于最大似然估计的声速更新方法。利用所提模型和时间测量, 不仅可以估计航行器位置, 还可以更新声速。仿真结果验证了本文所提方法在各种参数设置下均优于现有方法, 并且在时间测量噪声不大时可以达到克拉美罗下界(Cramer-Rao lower bound, CRLB), 在时间误差不大时, 声速更新结果显著提高。     

基于倒谱分析的水声信号被动定位时延估计算法研究

时延估计精度是决定定位精度的重要因素.传统被动定位时延估计往往采用广义互相关法,而相关器需要较宽的频带才能获得较好的时延测量精度,对于窄带信号的处理往往差强人意.采用的倒谱分析方法对宽带信号与窄带信号不仅具有良好的估计性能,且在信噪比满足一定要求的前提下,对窄带信号倒谱估计器在广义倒谱相关函数中比相关器在广义互相关函数中具有更尖锐的峰,从而提高了时延估计精度.通过仿真及对实测数据的分析,说明本方法的有效性.     

病态场景下多传感器系统误差的岭估计方法

多传感器系统误差估计是数据融合系统获得性能优势的关键前提之一。针对病态场景下传统系统误差估计方法数值不稳定的问题,对目标密集型和传感器密集型两种典型病态场景进行了理论分析,提出了多传感器系统误差的岭估计方法,以牺牲估计器无偏性的代价来改善估计结果的数值稳定性。通过引入条件数约束,给出了岭参数的最优取值方法。仿真结果表明,所提岭估计器在良态场景下与传统最小二乘估计器性能保持一致;在目标密集型场景下,与传统方法相比有显著性能优势;在传感器密集型场景下,对距离系统误差的估计性能有明显改善。     

一种车辆状态的非线性估计方法研究

应用Dugoff轮胎模型建立了车辆的非线性动力学方程.在此基础上,提出了一种纵向车速、横向车速和横摆角速度的非线性估计器设计方法,并基于Lyapunov稳定性理论给出了保证估计器收敛的充分条件.最后,在一个高精度的车辆动力学实时仿真环境中,将本方法与其它估计方法在不同路面条件下进行了多种驾驶工况的对比研究,并检验了本方法在丰辆参数不准确或车载传感器信号丢失时的估计效果.仿真结果表明,本方法具有较好的估计性能.     

移动通信信号到达时间差定位的估计

到达时间差(TimeDifferenceofArrival,TDOA)位置估计是无线定位的一项关键技术.首先介绍了TDOA定位估计原理,阐述了基站任意分布时(以三基站为例)的二维定位估计,以及在特殊情况下,即基站线性分布时的定位估计.最后,对三基站TDOA定位估计及其定位精度进行了仿真,为第三代移动通信无线定位系统的研究和开发提供了理论基础.     

残留频偏条件下码辅助的迭代载波同步算法

LDPC编码系统,接收端在译码前需要对频偏和相差进行估计,使残留频偏和相差在译码器收敛的允许范围之内,但在低信噪比条件下,即使在收敛范围内,较大的残留频偏和相差也会对LDPC编码系统性能有明显的恶化,所以必须结合迭代译码系统,对信号的残留频偏和相差进行进一步的估计。基于EM算法推导了频偏联合相差的迭代估计算法,给出了一种简单有效的残留频偏估计方法,并以此为基础,结合LDPC迭代译码输出软信息的统计特性,提出了一种码辅助的迭代载波同步算法,仿真结果表明,只要残留频偏和相差在迭代估计器收敛范围内,提出的算法可以使LDPC编码系统的误比特率接近理想同步条件下的译码性能。     

一种新的正弦信号频率估计方法

将二阶陷波器进行级联,构造一个具有多个陷波频率的自适应陷波器,实现对多个信号频率的估计。各个二阶陷波器的参数采用LMP(least mean power)算法同时进行优化,并利用该参数得到信号的频率。仿真结果表明,该方法在不同信噪比下对于稳定的单频、多频信号和频率变化信号具有较好的估计结果;噪声干扰在一定程度上影响频率的估计精度和估计速度。     

基于自适应旁瓣相消的干扰源方位估计方法

提出了一种基于自适应旁瓣相消器的压制式干扰源方位估计方法。首先,利用自适应旁瓣相消器对压制式干扰信号进行抑制处理并得到一组收敛权值;然后,利用收敛权值计算出接收阵列的方向图;最后,在接收阵列方向图上寻找较深的谷值点对应的方位来估计干扰源的方位。所提方法估计精度高且易于工程实现。针对典型卫星导航接收机阵列,通过计算机仿真验证了所提方法的有效性。     

基于FPGA的改进型最小均方自适应时延估计器

提高时延估计器的运算速度和时延分辨力是实现垂直线阵水下被动声目标快速、精确定位的关键。将最小均方自适应时延估计算法中原有的按“滤波-误差更新-权系数更新”严格依次递进的顺序迭代运算,优化为只包含误差更新和权系数更新操作的全并行乘/加运算。改进后的自适应时延估计器结构紧凑,并未改变其误差收敛特性。消声水池缩比试验表明,基于现场可编程逻辑门阵列(field programmable gate array, FPGA)实现的改进型最小均方自适应时延估计器能够在不需要流水线或时序控制单元的前提下提高数据处理速度,且具有一定的抗噪声干扰性能。     

目标状态估计器研究

全面系统地研究了基于空载雷达测量体制下的机动目标状态估计器的设计问题，重点对坐标系及目标状态变量的选择进行了讨论，并将每一种设计方法得到的目标状态估计器联入综合火力／飞行控制系统中进行了Ｍｏｎｔｅ Ｃａｒｌｏ仿真，对仿真结果进行了比较，给出了相应的结论。     

Research for the Algorithm of Nonlinear Two Stage Least Squares Estimator

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Decentralized model reference adaptive sliding mode control based on fuzzy model

1.INTRODUCTIONIn recent years,decentralized control of interconnect-ed systems has become an i mportant and challengingtopic[1~3].However,it is usually difficult to modelthe systemexactly due to the complex real environ-ment.In the past several years,active research hasbeen carried out in controller design based on univer-sal approxi mators,such as fuzzy control and neuralnetwork control[4~6].For a class of SISO nonlinearsystems,adaptive fuzzy control approaches were pro-posed based on …     

ADAPTIVE SYSTEMS THEORY： SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system‘s structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some “critical values“ and establishing some “Impossibility Theorems“ for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.     

基于相对信息观测量的INS/USBL非线性组合导航方法

针对传统惯性导航系统/超短基线定位系统(inertial navigation system/ ultra short base line, INS/USBL)组合导航利用绝对位置做观测信息存在导航精度较低,且噪声异常引起抗干扰能力弱的问题,提出基于相对信息观测量的INS/USBL非线性组合导航方法。以INS解算的应答器相对于INS在基阵坐标系下的入射角、斜距信息与超短基线输出的入射角、斜距信息之差作为观测量建立量测方程。在改进Sage Husa算法基础上采用容积规则,设计一种适用于非线性系统的自适应容积卡尔曼滤波估计器。仿真结果表明,该方法定位精度较传统方法提升2.4倍,在噪声异常情况下,滤波收敛,组合导航性能稳定。     

对运动干扰源的时差估计

讨论了用窄带接收机测量宽带噪声干扰信号到达时差时 ,干扰源、转发站和接收机的相对运动对时差测量最大似然估计和直接相关估计精度的影响 ,并给出了一种适用于运动干扰源的最大似然估计器的实现方法。     

卫星自主轨道估计方法及其高阶非线性滤波器设计

卫星的自主轨道确定是卫星自主生存的必要条件。采用星载常用敏感器对自然天体进行观测,并对观测量进行适当转换,得出观测方程为线性化方程,计算量明显减小且具有更直观的物理意义;利用星光折射时星光矢量所处特殊位置计算地心距,增加观测方程信息量;二体轨道动力学模型的一阶递推近似引入系统模型误差是影响滤波器性能的关键因素之一,应用一阶导数函数值的线性组合替代高阶导数的函数值可以使微分方程的线性化误差大为降低。在滤波器的设计过程中,应用此方法对状态方程进行处理,极大提高了滤波器状态方程的递推精度。仿真结果表明,在相同条件下这种高阶非线性轨道估计器的滤波性能远优于扩展卡尔曼滤波技术的一阶轨道估计器。     

Neural network-based H∞ filtering for nonlinear systems with time-delays

A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.     

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using Bsplines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.     

Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions

The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems.However,most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system,few literature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions.According to this problem,a new methodology which relaxes quite a restrictive assumption that the mode transition process must satisfy Markov properties is proposed.In this method,a general approach is presented to model the state dependent transitions,the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline.Then maximum a posterior estimation is obtained by using the Bayesian theory.The efficacy of the estimator is illustrated by a simulated example.     

非线性离散动态大系统的DISOPE关联预测递阶算法

提出了一种非线性离散动态大系统系统优化与参数估计集成的关联预测递阶算法 .在各子系统模型与实际存在差异的情况下 ,将动态系统系统优化与参数估计集成 ( DISOPE)方法与关联预测法相结合 ,得到一个上级协调 ,下级进行各子系统优化与参数估计集成的双环迭代算法 ,从模型出发通过迭代运算能得到实际系统在存在模型 -实际差异时的真实最优解 .仿真结果表明了算法的有效性和实用性.