基于Sh相关系数的非线性系统状态粒子估计算法

本文提出一种适用于非线性系统状态的粒子估计算法--基于Sh相关系数的粒子估计(PE)算法.该算法主要由预测、更新和平滑组成,利用被估计状态观测值路径和粒子观测值路径之间的Sh相关系数来修正粒子权值.仿真实验结果表明,该算法在状态估计精度上优于序列重要性重采样(SIR)算法、辅助粒子滤波(APF)算法、正则化粒子滤波(RPF)算法、高斯粒子滤波(GPF)算法和高斯混合粒子滤波(GSPF)算法.     

电动汽车锂电池SOC估测

SOC的估算对电动汽车锂电池的管理至关重要.该文提出了一种改进的安时积分法,在传统安时积分的基础上考虑温度对电池充放电的影响,及静置时间对SOC初值的影响,及时对SOC的初值进行修正,避免了传统安时积分法对误差的累积.以磷酸铁锂电池为例搭建实验平台,得到计算所需参数,并且验证了设计思路的可行性.     

动态系统参数与状态联邦双估计方法研究

本文提出了基于联邦滤波思想的参数和状态双估计模型及算法,给出了多传感器组合系统状态和参数信息分配原则,拓展了双估计及联邦卡尔曼滤波的使用范围.利用北斗/LORAN_C舰船航行实测数据,对双估计、联邦双估计等方法进行试验比较,实验表明,该方法提高了参数和状态估计的准确度.     

High-Gain Observers With Sliding Mode for State and Unknown Input Estimations

To handle the state estimation of a nonlinear system perturbed by a scalar disturbance distributed by a known nonlinear vector, a sliding-mode term is incorporated into the nonlinear high-gain observer (HGO) to realize a robust HGO. By imposing a structural assumption on the unknown input distribution vector, the observability of the disturbance with respect to the output is safeguarded, and the disturbance can be estimated from the sliding surface. Under a Lipschitz condition for the nonlinear part, the nonlinear observers are designed under the structural assumption that the system is observable with respect to any input. In the sliding mode, the disturbance under an equivalent control becomes an increment of Lipschitzian function, and the convergence of the estimation error dynamics can be proven similar to the analysis of HGOs. The proposed technique can be applied for fault detection and isolation. The simulation results for the bioreactor application demonstrate the effectiveness of the proposed method.      

Estimation of the Acoustic-to-Seismic Coupling Ratio Using a Moving Vehicle Source

We present a simple passive technique for estimating the acoustic-to-seismic signal coupling ratio (SAR) in the ground using noise produced by moving vehicles. The seismic signal received on a geophone contains some energy that has propagated as seismic waves and some energy that couples from acoustic waves to seismic waves in the vicinity of the geophone. We use the frequency-domain coherence between the microphone and geophone signals to determine when the seismic signal is predominantly due to acoustic-to-seismic wave coupling. In frequency bands where the microphone and geophone coherence is above 0.8, the ratio of the seismic ground particle velocity to sound pressure-SAR-can be determined with less than 2 dB of error. The method is applied to data from a summer experiment with grass ground cover and at two winter experiments with snow-covered ground. At 100 Hz, the summer analysis yields a SAR value of 1.0 times 10^-5 [(m/s)/Pa]. In addition, at 100 Hz, the two winter tests yield SAR between 0.1 times 10^-5 and 1.0 times 10^-5 [(m/s)/Pa]. In the later winter result, our vehicle-derived SAR estimate is shown to be in excellent agreement with SAR estimates obtained from blank pistol shots. Through the opportunistic exploitation background noise sources, our approach opens the possibility for automatic adaptation of unattended acoustic area, monitoring sensors to changing ground conditions.     

大范围视频监控下的人群密度估计和预测系统

Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In mo-nocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately esti-mated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd’s density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.     

基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计

精确实时在线的运动模型对于侧滑移动机器人的运动控制和轨迹规划至关重要,相比于离线模型估计,该文在基于速度瞬心(ICRs)的侧滑移动机器人运动学模型基础上,采用扩展卡尔曼滤波(EKF),在同一特定地形下在线准确得到ICRs的参数值;并针对不同的地形情况,采用k-近邻法对地形进行分类,实时判别机器人当前运行的路面,采用自适应的卡尔曼滤波器(AKF)调整滤波器参数。仿真和实验对比表明,该方法在同一地形和变化地形下均能快速估计出侧滑移动机器人的运动学模型,收敛时间均为3 s以内,可以满足实际使用的需要。     

Multiresolution and Wide-Scope Depth Estimation Using a Dual-PTZ-Camera System

Depth information is a critical cue for scene understanding which is very important for video surveillance. Traditional depth estimation approaches based on stereo vision have been well studied in past decades. However, little research is conducted with active cameras. In this correspondence, we discuss the depth estimation problem by employing dual-PTZ (pan/tilt/zoom)-camera system. The contributions of this correspondence includes the following three aspects: 1) we analyze the effect on depth's precision with different PTZ settings; 2) we propose a coarse-to-fine framework to deal with depth estimation problem for complex region; 3) we offer a method to generate mosaic of depth maps to combine depth information from different visual angles and resolutions for large scope. These contributions will widen the usage of dual-PTZ-camera systems to a greater extent. Real-data experimental results show that the proposed approach works well.      

Simultaneous State and Parameter Estimation in Induction Motors Using First- and Second-Order Sliding Modes

We address the problem of meeting the requirements of controllers for the control of speed of induction motors, but under the constraint of not using speed and flux sensors: the so-called “sensorless” control problem. We offer an observer-based solution and present the design of two observers which provide motor speed, flux, and rotor resistance estimates simultaneously. Both observers, based on the rotor flux model in the stationary reference frame, are designed with inputs that enforce first- (conventional) and second-order sliding modes, respectively, on appropriately chosen switching surfaces. We present experimental results of the estimation procedure to demonstrate that only current and input voltage measurements are needed for accurate speed and flux estimation even in the presence of unknown parameters.      

State Estimation With Initial State Uncertainty

The problem of state estimation with initial state uncertainty is approached from a statistical decision theory point of view. The initial state is regarded as deterministic and unknown. It is only known that the initial state vector belongs to a specified parameter set. The (frequentist) risk is considered as the performance measure and the minimax approach is adopted. Minimax estimators are derived for some important cases of unbounded parameter sets. If the parameter set is bounded, a method of finding estimators whose maximum risk is arbitrarily close to that of a minimax estimator is provided. This method is illustrated with an example in which an estimator whose maximum risk is at most 3% larger than that of a minimax estimator is derived.     

小阵列空间谱估计短波测向系统

本文介绍了小阵列空间谱估计短波测向系统的技术实现，给出了若干试验结果。结果表明，该系统不仅能对不相干信号测向，更重要的是能对多径到达的相干信号实现分离和测向。     

一种OFDM系统中叠加导频的信道估计方法

提出了一种新的基于叠加导频的信道估计算法。利用循环序列频域能量集中在某些频点上的特点,消除未知传输数据对导频的影响,将导频完全从接收数据中分离出来。在此基础之上,结合PN序列的自相关特性,在时域进行信道估计,进一步降低了噪声对导频的影响。仿真结果表明,与传统方法相比,该方法不但误码率和均方误差更低,而且还具有计算复杂度低、频带利用率高的特点。     

基于T-S模糊模型的非线性系统状态估计

现实世界中的动力系统大多是非线性的,而非线性系统的状态估计方法目前主要有扩展卡尔曼滤波,无味滤波,粒子滤波等,但它们对于非线性程度很高的系统滤波结果不是很理想。若用模糊规则模型去逼近非线性系统,基于规则理论去寻求状态的估计,则估计性能能够得到较大的改善。将模糊推理理论与Kalman滤波结合,用线性的方法逼近非线性模型,提出一种对非线性系统目标状态估计的新方法,给出了具体的处理过程,仿真实验支持理论结果     

Distributed State Estimation for Discrete-Time Nonlinear System with Unknown Inputs

This paper investigated the problem of distributed estimation for a class of discrete-time nonlinear systems with unknown inputs in a sensor network. A modification scheme to the derivative-free versions of nonlinear robust two-stage Kalman filter (DNRTSKF) is first introduced based on recently developed cubature Kalman filter (CKF) technique. Afterward, a novel information filter is proposed by expressing the recursion in terms of the information matrix based upon DNRTSKF. In the end, distributed DNRTSKF is developed by applying a new information consensus filter to diffuse local statistics over the entire sensor network. In the implementation procedure, each sensor node only fuses the local observation instead of the global information and updates its local information state and matrix from its neighbors’ estimates using Average-Consensus Algorithm. Simulation results illustrate that the proposed distributed filter reveals the performance comparable to centralized DNRTSKF and better than distributed CKF.     

网状被动传感器系统航迹初始状态估计

本文提出了一种估计被动传感器系统航迹初始状态的方法,该方法采用Levenbe唱一MarqlJan算法求解航迹初始状态,将cr锄er.Ra0下界近似作为状态估计误差的方差。文中还对如何获取全局最优解和如何排除在不可观测条件下得到的全局最优解进行了探讨。仿真实验结果验证了本文方法的有效性、合理性。     

非线性系统中状态和参数联合估计的双重粒子滤波方法

该文提出了一种双重粒子滤波方法,对存在未知参数的非线性系统进行状态和参数联合估计。该方法采用基于充分统计量的粒子滤波技术,避免了重采样过程中的粒子枯竭现象;采用贝塔分布拟合系统参数的后验分布,不仅充分利用了先验信息,而且避免了对高斯分布拖尾部分的采样,提高了粒子的采样效率。仿真实验结果表明,该方法提高了非线性系统中状态和参数的估计精度,降低了滤波器对初始误差的敏感性。     

无线传感器网络中基于多比特量化数据的滚动时域状态估计

该文基于多比特的量化策略,提出了无线传感器网络中多比特分布式滚动时域状态估计算法。每个传感器节点预先设定一个包含多个阈值的阈值簿,利用这个阈值簿将观测值量化成多比特,融合中心接收这些比特信息运用滚动时域的思想得到系统的状态估计值,与预期相同。仿真结果表明阈值簿中阈值个数越多则估计的结果会越精确。与单比特滚动时域状态估计方法相比,该方法避免了每一时刻传感器节点接收融合中心的反馈状态估计值用来设计阈值,并且在多比特信息下状态估计值的精度更高。     

车辆状态信息显示处理系统的研究与设计

介绍了基于CAN总线的车辆状态信息显示处理系统,分析了CAN.总线的特点及在汽车中使用CAN总线的优点,并给出了其在国内外汽车中的应用现状.首重介绍了系统的总体设计方案,系统的主节点、固定节点及活动节点的硬件组成和工作原理,以及大屏幕液晶显示器的特点及其在系统中的应用.