面向3维空间的移动机器人快速自适应SLAM算法

针对3维空间中移动机器人同时定位与地图构建(SLAM)问题,提出了一种基于改进强跟踪滤波(STF)的快速自适应SLAM算法.该算法首先对于强跟踪滤波器的噪声协方差阵进行在线自适应估计,用于抑制噪声对系统状态估计的影响,使系统状态估计迅速收敛到真实值附近;随后将状态协方差矩阵进行奇异值分解(SVD),提高算法的数值稳定性.该算法可提高对系统时变的自适应能力以及系统状态估计精度.与基于强跟踪滤波器的SLAM算法的仿真对比结果说明了该算法的有效性及其在抑噪性能和估计精度方面的优越性.     

改进的强跟踪SVD-UKF算法在组合导航中的应用

针对无迹卡尔曼滤波（Unscented Kalman Filter,UKF）在系统强非线性或状态模型不精确的情况下,存在滤波精度降低甚至发散的问题,提出一种改进的强跟踪SVD-UKF算法。该算法采用奇异值分解（Singular Value Decomposition,SVD）的方法改进UKF中状态协方差矩阵的迭代,保证协方差矩阵的非负定性及迭代的稳定性;算法基于强跟踪滤波（Strong Tracking filter,STF）理论框架,对改进的SVD-UKF引入多重渐消因子自适应调整状态协方差矩阵,在系统状态发生突变的情况下,实现系统真实状态的强跟踪。将该算法在BDS/INS组合导航中仿真验证,结果表明了该算法的有效性。     

自适应粒子滤波在卫星紫外导航中的应用

基于紫外敏感器的自主导航系统是典型的非线性和噪声非高斯分布的系统,针对扩展卡尔曼滤波(EKF)和Unscented 卡尔曼滤波(UKF)不适于噪声非高斯分布的系统,和一般粒子滤波缺乏在线自适应调整能力等问题,提出了将基于正交性原理的自适应强跟踪滤波器(STF)和UKF相融合作为重要密度函数,应用于基于紫外敏感器自主导航粒子滤波器新方法,通过UKF构造粒子群,对粒子群中的每一个粒子的每一个sigma点用STF进行更新,使得算法的鲁棒性增强,有极强的对突变状态的跟踪能力,具有强的自适应能力.为了说明算法的有效性,结合模拟的轨道数据和测量数据进行了仿真,仿真结果说明了所提算法的有效性.     

基于噪声缩放的自适应UKF-SLAM算法

针对扩展卡尔曼滤波(EKF)算法在移动机器人同时定位和环境建模(SLAM)中的缺点,即非线性系统简单线性化所导致的系统状态方程的不准确性、雅克比矩阵的计算所导致的计算复杂化以及噪声模型不确定性所导致的滤波稳定性降低等问题,提出一种对噪声自适应的UKF-SLAM算法。该算法通过对噪声缩放进而改变噪声模型,利用观测残差序列准确估计观测噪声模型协方差,运用预测的新息协方差和IAE开窗法求其系统状态噪声缩放因子,从而准确估计系统状态噪声模型协方差,实现对不确定的噪声模型能够自适应UKF-SLAM算法。UKF的Sigma点采样策略是比例对称采样。实验结果证明,该方法相对EKF算法和UKF算法具有较高的定位精度和自适应能力。     

自适应UKF算法在目标跟踪中的应用

针对目标跟踪中系统噪声统计特性未知导致滤波发散或者滤波精度不高的问题, 提出了一种自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF)算法.该算法在滤波过程中,利用改进的Sage-Husa估 计器在线估计未知系统噪声的统计特性,并对滤波发散的情况进行判断和抑制, 有效提高了滤波的数值稳定性,减小了状态估计误差. 仿真实验结果表明,与标准UKF算法相比,自适应UKF算法明显改善了目标跟踪的精度和稳定性.     

FAST-SR-UKF算法及其在组合导航系统中的应用

为解决无迹卡尔曼滤波（UKF）算法在组合导航应用中遇到的系统模型不确定、系统噪声统计特性未知以及计算误差较大等问题,提出了模糊自适应强跟踪平方根无迹卡尔曼滤波（FAST-SR-UKF）算法,该算法不仅具有传统UKF的优势,而且包含如下特点：通过模糊自适应强跟踪模块,增强了系统对模型不确定性以及噪声统计参数未知的适应能力;利用平方根滤波的思想,提高了模糊自适应强跟踪无迹卡尔曼滤波算法的数值稳定性,改善了由于计算误差导致的滤波发散问题。仿真结果表明：相对于传统的UKF算法,该算法精度更高、鲁棒性更强。     

强跟踪延迟滤波算法及其在感应电机无速度传感器控制中的应用

在强跟踪滤波(Strong track filter, STF)算法和延迟扩展Kalman滤波(Schmidt extended Kalman filter, SEKF)算法的基础上, 提出了强跟踪延迟滤波(Strong track Schmidt filter, STSF)算法, 结合感应电机降阶模型建立了电机状态估计算法, 将其应用于感应电机无速度传感器控制系统中, 并与扩展Kalman滤波(Extended Kalman filter, EKF)、SEKF和STF三种算法的状态估计性能作比较. 仿真和实验结果表明, STSF算法在估计精度、跟踪速度、抑止噪声等方面均优于EKF算法, 并且计算复杂度显著降低, 能有效在线估计电机转速和磁链.     

基于极大似然准则和最大期望算法的自适应UKF 算法

针对噪声先验统计特性未知情况下的非线性系统状态估计问题,提出了基于极大似然准则和 最大期望算法的自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF) 算法.利用极大似然准则构造含有噪声统计特性的对数似然函数,通 过最大期望算法将噪声估计问题转化为对数似然函数数学期望极大化问题,最终得到带次优递 推噪声统计估计器的自适应UKF算法.仿真分析表明,与传统UKF算法相比,提出的自适应UKF算法 有效克服了传统UKF算法在系统噪声统计特性未知情况下滤波精度下降的问题,并实现了系统噪 声统计特性的在线估计.     

基于强跟踪滤波器的交互式多模型算法

针对传统的EKF-IMM算法鲁棒性较差等问题,提出了一种基于强跟踪滤波器（STF）的交互式多模型算法。该算法通过引入强跟踪滤波器（STF）的渐消因子,实现了对滤波器增益的实时调节,从而提高了系统对机动目标的自适应跟踪能力和跟踪精度。仿真结果表明,在目标不发生机动时,该算法和EKF-IMM算法的跟踪效果相近,在目标发生强机动时,该算法在径向速度和方位角的跟踪精度要优于EKF-IMM算法;提出的算法具有更优的机动目标跟踪性能。     

基于改进UKF算法的移动机器人定位方法研究

针对移动机器人在多传感器融合定位过程中因噪声统计特性未知或不准确引起的定位精度不高的问题,提出了一种基于Sage-Husa滤波改进的无损卡尔曼滤波(UKF)移动机器人定位算法。首先建立了移动机器人定位相关模型;然后根据噪声统计特性时变特点利用Sage-Husa中的噪声估计器,对状态噪声和量测噪声进行自适应地估计,减小扰动噪声给定位解算带来的误差;接着在状态更新时引入收敛因子,加快算法收敛速度;最后将UKF算法和改进的UKF算法应用到实验室移动机器人中进行仿真实验。实验结果表明,所提出的算法对状态扰动具有较强的抵制能力,对机器人定位的准确性与稳定性的提升具有显著效果。     

Robust unscented Kalman filter with adaptation of process and measurement noise covariances

Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear stochastic systems, which suffers from performance degradation and even divergence when the noise distribution used in the UKF and the truth in a real system are mismatched. For state estimation of nonlinear stochastic systems with non-Gaussian measurement noise, the Masreliez–Martin extended Kalman filter (EKF) gives better state estimates in relation to the standard EKF. However, the process noise and the measurement noise covariance matrices should be known, which is impractical in applications. This paper presents a robust Masreliez–Martin UKF which can provide reliable state estimates in the presence of both unknown process noise and measurement noise covariance matrices. Two numerical examples involving relative navigation of spacecrafts demonstrate that the proposed filter can provide improved state estimation performance over existing robust filtering approaches. Vision-aided robot arm tracking experiments are also provided to show the effectiveness of the proposed approach.     

Marginalized particle filter for spacecraft attitude estimation from vector measurements

An algorithm based on the marginalized particle filters (MPF) is given in details in this paper to solve the spacecraft attitude estimation problem: attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle in order to reduce the size of the state space and computational burden. The distribution of attitude vector is approximated by a set of particles and estimated using particle filter, while the estimation of gyro bias is obtained for each one of the attitude particles by applying the Kalman filter. The efficiency of this modified MPF estimator is verified through numerical simulation of a fully actuated rigid body. For comparison, unscented Kalman filter (UKF) is also used to gauge the performance of MPF. The results presented in this paper clearly demonstrate that the MPF is superior to UKF in coping with the nonlinear model.     

Marginalized particle filter for spacecraft attitude estimation from vector measurements

An algorithm based on the marginalized particle filters （MPF） is given in details in this paper to solve the spacecraft attitude estimation problem： attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle in order to reduce the size of the state space and computational burden. The distribution of attitude vector is approximated by a set of particles and estimated using particle filter, while the estimation of gyro bias is obtained for each one of the attitude particles by applying the Kalman filter. The efficiency of this modified MPF estimator is verified through numerical simulation of a fully actuated rigid body. For comparison, unscented Kalman filter （UKF） is also used to gauge the performance of MPE The results presented in this paper clearly derfionstrate that the MPF is superior to UKF in coping with the nonlinear model.     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

自适应无迹卡尔曼滤波算法在地磁导航中的应用

考虑到地磁导航过程中,无迹卡尔曼滤波(UKF)受初始值误差、系统噪声不确定性和环境磁异常扰动这些因素的影响,将自适应估计原理引入到UKF算法,提高UKF算法的收敛性和地磁导航系统的稳定性.计算结果表明,在地磁导航系统数据处理中,UKF算法略优于扩展卡尔曼滤波(EKF),自适应UKF算法优于自适应EKF算法,自适应UKF算法能够很好地抑制初始化误差、系统噪声不确定性和环境磁异常扰动对导航解的影响,进一步提高地磁导航系统的定位精度和可靠性.     

基于DSP的电动汽车锂电池荷电状态估算的研究与实现

锂电池荷电状态(SOC)的准确估算是电动汽车能源管理的关键技术。为了提高锂电池SOC的估算精度,将无迹卡尔曼滤波(UKF)应用于锂电池SOC估算,以减小拓展卡尔曼滤波(EKF)简单线性化带来的误差。搭建电池检测系统的硬件平台,以TMS320F28335型数字信号处理器(DSP)为主控芯片(MCU),实现电压、电流、温度的检测及UKF算法,并设计了相关的电池测试实验。实验结果表明,UKF可以实时估算锂电池SOC,估算误差在4%以内,高于传统的拓展卡尔曼滤波(EKF)。     

Maximum correntropy unscented filter

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for non-linear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearisation regression based on the maximum correntropy criterion is then used to obtain the posterior estimates of the state and covariance matrix. The satisfying performance of the new algorithm is confirmed by two illustrative examples.     

基于UKF 和神经网络的一类非线性系统状态估计

在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.