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     

基于粒子滤波的一种改进的资料同化方法

针对在粒子数较少时传统的集合卡尔曼滤波和粒子滤波方法不能有效表征后验概率密度函数(PDF)的问题, 提出了一种改进的粒子滤波方法. 主要思想是在预测步之后引入更新步, 并且将观测时刻与非观测时刻的同化分析进行区别处理. 对典型的低维和高维混沌系统的仿真结果表明:改进粒子滤波方法是一种非常有效的估计非线性非高斯随机系统状态的方法.     

基于状态估计的混沌直扩信号联合均衡与解调

针对混沌直接序列扩频信号(以下称混沌直扩信号)通过非理想信道,利用信道和混沌直扩信号的状态方程及其均衡和解调的关联性,提出一种基于状态估计的联合均衡与解调算法.算法采用多扩展卡尔曼滤波(extended kalman filter,EKF)结构,一边对信道均衡,一边估计二进制信息码,二者互为因果,同时进行,不仅可以有效克服非理想信道所带来的多径信道畸变、加性噪声等对信号的影响,还可将掩藏在混沌直扩信号中的原始二进制信息码解调出来,比均衡和解调分离的做法更有效地利用信息,有更好的实时性.仿真结果表明,所提出的算法收敛速度快,能在信道特性未知情况下较好地抵御多径效应和噪声影响,实现了混沌直扩信号在非理想信道条件下的有效可靠传输. 关键词： 混沌直扩通信 非理想信道 联合均衡与解调算法 扩展卡尔曼滤波     

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.      

混合退火粒子滤波器

针对非线性、非高斯系统状态的在线估计问题，提出一种新的基于序贯重要性抽样的粒子滤波算法. 在滤波算法中，用状态参数分解和退火系数来产生重要性概率密度函数，此概率密度函数综合考虑了转移先验、似然、噪声的统计特性以及最新的观察数据，因此更接近于系统状态的后验概率. 理论分析与仿真实验表明该粒子滤波器的性能明显优于标准的粒子滤波器和扩展卡尔曼滤波器. 关键词： 非线性 非高斯 粒子滤波 序贯重要性抽样     

基于油液光谱分析和粒子滤波的发动机剩余寿命预测研究

油液光谱分析是机械磨损状态监测、故障诊断与故障预测的重要技术,基于光谱数据的机械状态剩余寿命预测有利于实现机械系统的最优维修决策。由于机械设备越来越复杂,其健康状态的退化过程很难用线性模型来表示,而粒子滤波(particle filter, PF)对非线性非高斯系统的处理能力,与经典Kalman滤波相比具有明显的优势,文章将PF预测方法运用于光谱分析,提出了基于PF和油液光谱分析技术的设备剩余寿命预测方法。在预测模型中实现了根据设备后验分布的估计值预测其先验分布概率,建立了基于PF的多步向前长期预测模型。最后,对某发动机实际的光谱分析数据进行了预测和分析,并与传统Kalman滤波方法的预测结果进行了比较,结果充分表明了本方法的有效性和优越性。     

改进的自适应衰减卡尔曼滤波算法

SINS/GPS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和随机噪声信息必须准确已知。实际情况下,大部分系统的模型和随机噪声信息不完全可知,这可能会导致滤波器估计精度下降。针对这一问题,根据求解遗传因子的方法不同对传统的自适应衰减卡尔曼滤波进行改进,提出一种改进的自适应衰减卡尔曼滤波。改进后的算法分别适用于系统噪声统计模型不准确可知和量测噪声统计模型不准确可知两种情况,分别对应于两种滤波算法,并且二者具有统一的滤波框架。仿真结果表明,改进的自适应衰减卡尔曼滤波比卡尔曼滤波精度较高,有效解决了因为噪声模型不准确导致的精度下降问题。     

一种改进的DVB-T信道估计算法

针对传统DVB-T系统信道估计算法估计精度低或复杂度高的问题,为了提高信道估计算法整体性能,提出了一种改进的DVB-T信道估计算法。该算法首先采用最小二乘法估计离散导频处频率响应估计值;进而,利用连续导频对信噪比进行估计;最后,将得到的信噪比估计值与预先设定的信噪比阈值进行比较,若估计值小于阈值,则先经过卡尔曼滤波处理,再在时频二维方向上用高斯插值得到全部子载波的频率响应值,否则直接在时域和频域上进行插值。此方法在信道条件较好时关闭卡尔曼滤波过程,从而有效减少滤波次数,提高算法整体运行速度。仿真结果表明,该方法能有效提高系统整体性能,减少在噪声影响较大时的干扰,同时也兼顾了算法整体运行速度。     

基于不敏粒子滤波的水中非合作磁性目标实时磁定位方法

针对水中非合作磁性目标的实时定位问题, 提出了一种基于不敏粒子滤波(unscented particle filter, UPF)的实时磁定位方法. 从非合作磁性目标的运动特征出发, 建立了状态空间模型, 利用UPF算法对目标状态进行实时估计. 为了提高系统的可观测性, 在算法迭代过程中对粒子状态进行约束及利用最小二乘法反演磁矩. 仿真与铁磁物体定位实验结果表明, 该方法的定位精度较高, 实时定位效果较好, 可用于近场实时磁定位问题中. 关键词： 磁定位 椭球体模型 状态空间模型 不敏粒子滤波     

区间衍生粒子滤波器

针对非线性、非高斯环境下具有不确定动态模型参数的系统状态估计问题,提出了一种新颖的区间衍生粒子滤波算法.该算法利用区间滤波生成的重要性密度函数,在系统状态转移概率密度的基础上,融入最新的系统观测数据,从而提高了对系统状态后验概率的逼近程度.为了进一步提高算法的实时性,提出一种类似光子衍射的粒子衍生过程,进而缓解了滤波精度与运算量之间的矛盾.通过陀螺/星敏感器组合定姿问题验证了该算法的有效性和鲁棒性.     

Robust tracking of moving sound source using scaled unscented particle filter

Tracking an active sound source involves the modeling of non-linear non-Gaussian systems. To address this problem, this paper proposed scaled unscented particle filter (SUPF) algorithm for tracking moving sound source. The particle filter part of the SUPF provides the general probabilistic framework to handle non-linear non-Gaussian systems, and the scaled unscented Kalman filter (SUKF) part of the SUPF generates better proposal distributions by taking into account the most recent observation. Meanwhile, models used in SUPF algorithm were also explored for the sound source motion, observation and the likelihood of the sound source location in the light of the Langevin process. Compared with the conventional PF approach, the simulated results demonstrated that the SUPF algorithm had superior tracking performance.     

混沌信号在无线传感器网络中的盲分离

混沌信号在本质上属于非线性非高斯信号,它在无线传感器网络下的应用还涉及到信号量化问题,这使得混沌信号在此应用环境下的信号盲分离更为棘手.针对此问题,本文在容积卡尔曼粒子滤波的框架下提出一种解决方法.文中首先推导出观测信号的概率密度函数,在量化比特有限的情况下,采用最优量化器,获得最优的量化结果.在此基础上,使用容积卡尔曼滤波器产生粒子滤波中的重要性概率密度函数,融入最新的观测值,提高粒子对系统状态后验概率的逼近,提高信号盲分离的精度.仿真结果表明算法能够有效地分离混合混沌信号,参数估计的精度及其运算量均优于已有的无先导卡尔曼粒子滤波算法,其运行时间为无先导卡尔曼粒子滤波算法的88.77%.     

基于时频差的正交容积卡尔曼滤波跟踪算法

针对基于时频差测量的无源跟踪中面临的非线性估计问题, 提出一种正交容积卡尔曼滤波跟踪算法. 该算法在容积卡尔曼滤波算法的基础上, 通过引入特定正交矩阵改进容积采样方法, 在高维状态估计下减小因采样产生的误差, 在没有增加计算量的前提下, 有效提高收敛速度及跟踪精度. 仿真结果表明, 在基于到达时差和到达频差的联合无源跟踪问题中, 与扩展卡尔曼滤波及容积卡尔曼滤波算法相比, 本文所提算法在跟踪性能上有明显提升.     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

Dim moving target tracking algorithm based on particle discriminative sparse representation

The small dim moving target usually submerged in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio (SNR). A target tracking algorithm based on particle filter and discriminative sparse representation is proposed in this paper to cope with the uncertainty of dim moving target tracking. The weight of every particle is the crucial factor to ensuring the accuracy of dim target tracking for particle filter (PF) that can achieve excellent performance even under the situation of non-linear and non-Gaussian motion. In discriminative over-complete dictionary constructed according to image sequence, the target dictionary describes target signal and the background dictionary embeds background clutter. The difference between target particle and background particle is enhanced to a great extent, and the weight of every particle is then measured by means of the residual after reconstruction using the prescribed number of target atoms and their corresponding coefficients. The movement state of dim moving target is then estimated and finally tracked by these weighted particles. Meanwhile, the subspace of over-complete dictionary is updated online by the stochastic estimation algorithm. Some experiments are induced and the experimental results show the proposed algorithm could improve the performance of moving target tracking by enhancing the consistency between the posteriori probability distribution and the moving target state.     

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.     

光度曲线反演空间目标形状的发展现状

光度曲线反演空间目标形状和姿态等参数为空间目标监视与识别提供了新的技术途径。首先分析了基于光度曲线反演空间目标信息的必要性,之后通过关注和跟踪国内外最新发展情况,全面总结了目前进行形状反演的3种主流方法的研究现状及优缺点,对3种反演方法进行了比较分析,指出基于非线性滤波技术的形状反演方法是未来的主要发展方向,并分析了3种非线性滤波技术的优缺点。最后针对反演存在的问题,指出了基于改进的实时性更好的粒子滤波算法,融合多种传感器信息反演几何形状和表面材料更为复杂的空间目标是下一步研究发展的主要方向。     

Adaptive nonlinear neuro-controller with an integrated evaluation algorithm for nonlinear active noise systems

An adaptive nonlinear neuro-controller with an integrated evaluation algorithm for nonlinear active noise control systems is proposed to attenuate the nonlinear and non-Gaussian noises. Inspired by the structure of the Hammerstein or Wiener model, the proposed controller is realized by the static nonlinear memory function mapping on the basis of a single neuron. A generalized filtered-X gradient descent algorithm based on an integrated evaluation criterion is developed to adaptively adjust the weights of the controller, where the weighted sum of Renyi's quadratic error entropy and the mean square error is applied as the integrated performance index, which improves the performance of the adaptive algorithm by introducing the information entropy. In addition, the convergence of the proposed approach is analyzed, and the computational complexity among different methods is investigated. The proposed scheme can effectively attenuate the nonlinear and non-Gaussian noises and has a relative simple structure and less learning parameters. The simulation results demonstrate the validity of the proposed method for attenuating the nonlinear and non-Gaussian noises.