Reduced-order Kalman filtering for state constrained linear systems

This paper aims at solving the state filtering problem for linear systems with state constraints. Three classes of typical state constraints, i.e., linear equality, quadratic equality and inequality, are discussed. By using the linear relationships among different state variables, a reduced-order Kalman filter is derived for the system with linear equality constraints. Afterwards, such a solution is applied to the cases of the quadratic equality constraint and inequality constraints and the two constrained state filtering problems are transformed into two relative constrained optimization problems. Then they are solved by the Lagrangian multiplier and linear matrix inequality techniques, respectively. Finally, two simple tracking examples are provided to illustrate the effectiveness of the reduced-order filters.

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.

A genetic resampling particle filter for freeway traffic-state estimation

On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.

An all-solid-state high power quasi-continuous-wave tunable dual-wavelength Ti：sapphire laser system using birefringence filter

This paper describes a tunable dual-wavelength Ti：sapphire laser system with quasi-continuous-wave and high-power outputs. In the design of the laser, it adopts a frequency-doubled Nd：YAG laser as the pumping source, and the birefringence filter as the tuning element. Tunable dual-wavelength outputs with one wavelength range from 700 nm to 756.5 nm, another from 830 nm to 900mn have been demonstrated. With a pump power of 23 W at 532 nm, a repetition rate of 7 kHz and a pulse width of 47.6 ns, an output power of 5.1 W at 744.8 nm and 860.9 nm with a pulse width of 13.2 ns and a line width of 3 nm has been obtained, it indicates an optical-to-optical conversion efficiency of 22.2%.

Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

Dual Extended Kalman Filter for Combined Estimation of Vehicle State and Road Friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.

ESTIMATE ACCURACY OF NONLINEAR COEFFICIENTS OF SQUEEZEFILM DAMPER USING STATE VARIABLE FILTER METHOD

The estimate model for a nonlinear system of squeeze film damper (SFD) is described.The method of state variable filter (SVF) is used to estimate the coefficients of SFD.The factors which are critical to the estimate accuracy are discussed

State Estimation for Non-linear Sampled-Data Descriptor Systems:A Robust Extended Kalman Filtering Approach

This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.

Robust Sliding-mode Filtering for a Class of Uncertain Nonlinear Discrete-time State-delayed Systems

This paper is concerned with the problem of robust sliding-mode filtering for a class of uncertain nonlinear discrete-time systems with time-delays. The nonlinearities are assumed to satisfy global Lipschitz conditions and parameter uncertainties are supposed to reside in a polytope. The resulting filter is of the Luenberger type with the discontinuous form. A sufficient condition with delay-dependency is proposed for existence of such a filter. And the desired filter can be found by solving a set of matrix inequalities. The resulting filter adapts for the systems whose noise input is real functional bounded and not be required to be energy bounded. A numerical example is given to illustrate the effectiveness of the proposed design method.

MLP training in a self-organizing state space model using unscented Kalman particle filter

Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.

STOCHASTIC NOISE TOLERANCE:ENHANCED FULL STATE OBSERVER VS. KALMAN FILTER FROM VIDEO TRACKING PERSPECTIVE

A control-based full state observer scheme is explored for video target tracking application, and is enhanced with a lowpass filter for improving the tracking precision, thus forming an Enhanced Full State Observer (EFSO). The whole design is based on the given lab-generated video sequence with motion of an articulate target. To evaluate the EFSO’s stochastic noise tolerance, a Kalman Filter (KF) is intentionally employed in tracking the same target with the given Gaussian white noises. The comparison results indicate that, for system noises of certain statistics, the proposed EFSO has its own noise resistance capacity that is superior to that of KF and is more advantageous for implementation.

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

基于稀疏直接法的水下单目视觉惯性里程计

针对水下视觉导航在弱纹理环境下定位精度低及稳健性较差的问题,本文提出了一种基于稀疏直接法的水下单目视觉惯性里程计。该方法基于像素灰度不变的假设,通过优化光度误差估计相机位姿,避免了特征点提取和匹配的复杂过程,从而提高了导航的实时性和稳健性;同时,结合惯性测量单元(IMU)的数据,利用误差状态卡尔曼滤波(ESKF)进行数据融合进一步减小误差,以提高自主水下机器人(AUV)在水下复杂环境导航的稳定性和精度。试验结果表明,误差达厘米级且与单纯的视觉算法相比,有所减小,证明了该系统能够有效融合视觉和惯性信息,在水下导航领域具有较高的精度和稳健性。

基于Bloom filter的多模式匹配引擎

基于Bloom filter,结合位拆分状态机设计了一种适合硬件实现的多模式匹配引擎,由bloom filter过滤出可疑字符,位拆分状态机进行精确匹配.提出了过滤引擎和精确匹配引擎的流水线连接结构,通过增加分配器、缓存等硬件单元解决两引擎处理速度不匹配的问题,利用引擎的并行处理达到较高的吞吐性能.还通过设定规则长度等简化设计使引擎在保持高吞吐量的同时减小资源占用量,提高了可扩展性.

ODBF:基于操作型衰落Bloom Filter的P2P网络弱状态路由算法

在P2P网络中,基于衰落Bloom Filter的弱状态路由算法试图将每条查询消息沿着成员资格信息量最强的方向传递,并最终以较低的传输代价和传输时延确保较高的查准率.研究发现衰落Bloom Filter在传递过程中存在严重的多径叠加和噪音问题,这直接导致查询消息以很高的概率沿着错误的方向传播,甚至会退化为泛洪路由算法.为解决这一挑战性难题,文中提出了基于操作型衰落Bloom Filter的弱状态路由算法ODBF(Operative Deca-ying Bloom Filter).ODBF通过分别保存对象的衰落Bloom Filter及源节点等信息,使得ODBF能够有效解决基于衰落Bloom Filter的路由信息在P2P网络中的多径叠加和信息回流问题,有效抑制噪音的影响,进而使得基于弱状态的路由能够以很高的概率沿着正确方向进行.

基于BP-DCKF-LSTM的锂离子电池SOC估计

电池荷电状态(SOC)的准确估计是电池管理系统(BMS)的核心功能之一。为了提高锂电池SOC估算精度,提出了一种将反向传播神经网络(BP)、双容积卡尔曼滤波(DCKF)和长短期记忆神经网络(LSTM)相结合的SOC估计方法。针对多温度条件下传统多项式拟合法在拟合开路电压(OCV)与SOC时效果较差的问题,提出了一种基于BP神经网络的拟合方法,通过验证表明该方法能有效提高拟合精度。针对单独使用模型法或数据驱动法估计SOC各自存在的优缺点,提出了一种将DCKF与LSTM相结合的估计方法,在提高估计精度的同时,可以减少参数调节时间和训练成本。实验验证表明,BP-DCKF-LSTM算法的均方根误差(RMSE)和平均绝对误差(MAE)分别小于0.5%和0.4%,具有较高的SOC估算精度和鲁棒性。

基于衰落Bloom Filter的P2P网络弱状态路由算法

在P2P网络中,基于衰落Bloom Filter的弱状态路由算法试图将每条查询消息沿着成员资格信息量最强的方向传递,并最终以较低的传输代价和传输时延确保较高的查准率.衰落Bloom Filter在传递过程中存在严重的多径叠加和噪音问题,这直接导致查询消息会以很高的概率沿着错误的方向传播,甚至会退化为泛洪路由算法.为了解决这一挑战性难题,提出了DWalker这种基于衰落Bloom Filter的高效弱状态路由算法.DWalker基于有向随机网络,采用指数衰落Bloom Filter来发布和传播每个节点共享资源的信息,且其最大传播距离小于网络中任意两点之间距离的期望值,从而有效抑制了衰落Bloom Filter在传播过程中的多径叠加问题.DWalker采用多个Bloom Filter而不是单个Bloom Filter来表达一项路由条目,在单个Bloom Filter的错误发生概率达到设计上限时,可按需动态增加新的Bloom Filter,以将更多资源对象信息纳入到当前路由条目中.DWalker仅根据当前节点的各项路由条目中值为1的比特位所占的最大比例,以及查询消息在正确转发方向对应的路由条目中对应比特位中值为1的个数的临界值,就能使进入目标对象传播范围内的查询消息以较高的概率辨认出正确的路由方向.理论分析和实验结果表明,DWalker能够以较低的查询消息代价、较小的路由条目存储开销以及较短的查询时延,使绝大多数查询消息沿正确方向转发,从而获得较高的查准率.

基于改进粒子滤波的锂电池健康状态估计

为了准确估计锂离子电池健康状态(SOH),文章提出一种基于改进粒子滤波算法的SOH评估方法。针对传统粒子滤波算法中粒子权重趋于零、导致粒子多样性丧失的问题,引入残差重采样算法,通过分离粒子权重的整数和小数部分,以替代传统的重采样方法,从而减轻粒子退化现象,保持粒子集的多样性。同时,结合无迹卡尔曼滤波(UKF)算法生成基于状态均值和协方差的Sigma点,以更精确地捕捉系统状态的不确定性,避免局部线性化近似的截断误差。采用NASA实验室公布的试验数据进行验证,结果表明,与传统粒子滤波算法相比,该方法将平均误差降低至2%以内,显著提升了SOH估计的精度和鲁棒性。