改进的粒子滤波目标跟踪算法

针对环境迁移、目标被遮挡或姿态变化较大时传统粒子滤波算法的鲁棒性不强的问题,提出一种改进的粒子滤波目标跟踪算法。建立目标模型时,将目标的HSV颜色特征和Uniform LBP纹理特征进行加权融合;粒子重采样过程中,采用加权随机采样方法,将粒子权值作为重采样的影响因子而非决定因子,以提升粒子多样性,降低粒子衰退对目标跟踪的影响;目标被干扰时,采用卡尔曼滤波对目标位置进行偏移校正,以获取目标正确位置;最后采用模板更新策略对目标模板进行实时更新。实验结果表明：相较于传统粒子滤波算法和CMT算法,本文算法对复杂环境中目标被遮挡和姿态变化的情况下都具有较好的鲁棒性。     

一种改进的自适应重采样粒子滤波算法

针对粒子滤波跟踪算法中重采样所引起的粒子多样性缺失问题,提出了一种自适应粒子滤波重采样方法。首先将粒子权值进行分类,中等权值粒子保持不变,大、小权值粒子进行权值优化组合,其次对优化组合后的小部分粒子进行系统重采样。最后对粒子的权值及粒子的复制次数分别进行比较运算。试验结果表明,改进后的粒子滤波算法不仅提高了机动目标跟踪的运算效率,而且还有效提高了跟踪的稳定性,目标跟踪更加准确。     

Unstructured road detection based on adaptive edge matching

为了解决非结构化道路区域分割过程中复杂背景与目标的多峰分布问题,提出了一种适用于双阈值分割的改进Otsu方法.该方法借助于灰度直方图的峰值搜索解决目标与背景方差差异较大导致的Otsu误分割问题,并利用Otsu边缘与加权Canny边缘的配准提高道路区域分割的精度.基于配准后加权Canny边缘的结果,采用自适应蒙特卡罗方法提高道路边界识别的精度,通过自适应粒子规模选取和观测模型采样机制克服传统蒙特卡罗方法的粒子退化问题.不同场景下的非结构化道路识别实验表明,该方法能够有效克服道路缺损、光影、照度变化等不利因素的影响,同时能够满足智能车辆视觉导航的实时性要求.     

基于粒子滤波的虚实配准方法研究

提出了一种基于粒子滤波算法的虚实配准方法,该方法综合利用快速角点检测和内点统计方法计算粒子权值,在对权值作归一化运算之后,利用粒子加权和计算摄像机位置和姿态.有效解决了传统算法在摄像机抖动、标识遮挡和场景光照变化等情况下配准易失效问题.实验结果表明,该方法提高了基于标识虚实配准系统的健壮性.     

结合颜色和结构信息的粒子滤波跟踪算法

单纯依靠颜色信息往往使得目标跟踪不稳定.为了在复杂背景和光照变化等干扰情况下,能够准确地跟踪到感兴趣的目标,本文提出了将颜色和结构信息相结合的方法.该方法在基于颜色直方图的粒子滤波跟踪框架中,利用目标的灰度图像建立结构模型,通过结构相似性质量因子衡量目标在两帧图像之间的相似性.实验表明,该算法在使用相同粒子数目的情况下较传统的基于颜色直方图的粒子滤波跟踪算法鲁棒性更高,可以用于特定场合可疑目标的跟踪.     

一种舱音背景声的特征频率获取方法-CZT算法

针对传统舱音译码系统在辨听、分析舱音背景声上的不足,并利用线性调频Z变换(CZT)适用于高采样频率的信号、而且计算中可以任意调整频率的分辨率等频谱分析的灵活性,提出基于CZT获取飞机舱音背景声的特征频率的新方法.通过仿真和以飞机驾驶舱高速配平移动背景声为例进行计算分析.和实验测试结果对比得出:CZT方法是一种适合于确定舱音背景声特征频率的有效方法,对调查飞机事故原因具有较好的实用性.     

基于粒子滤波的交通标志牌自动跟踪方法

提出了一种基于粒子滤波的交通标志牌自动跟踪算法。首先对采集图像进行预处理来提高图像的对比度,接着利用颜色特征对交通标志牌进行检测并通过形状特征来对检测得到的交通标志牌候选区域进行筛选;然后在HSI颜色空间统计第一步得到的交通标志牌的颜色直方图特征信息,最后利用此特征采用粒子滤波算法实现对交通标志牌的跟踪。实验结果显示,对于分辨率为270*480的采集图像,粒子滤波算法每秒钟可以跟踪20帧,正确率达到了92%以上,表明该算法能实现对复杂背景下交通标志牌的自动跟踪。     

粒子滤波技术研究和分析

非线性非高斯状态空间模型的最优估计问题在信号处理、自动控制、金融、无线通讯等领域具有重要的应用,粒子滤波技术通过非参数化的蒙特卡罗模拟方法来实现递推贝叶斯滤波,适用于任何能用状态空间模型表示的非线性系统,滤波精度可以逼近最优估计,其有效性已经得到各领域研究人员的极大认可,基本粒子滤波算法存在的最大问题是粒子退化,针对这一问题,对权值退化、重要性函数选取、重采样等影响粒子滤波器性能的关键技术进行深入研究。     

复杂背景下基于HSV空间和模板匹配的车牌识别方法研究

车牌识别技术作为交通管理自动化的重要手段,在交通监视和控制中占有很重要的地位.车牌识别过程可分为车牌定位、车牌校正、字符分割和字符识别四个部分.在车牌定位中,若单纯采用纹理特征或颜色特征来进行定位,往往适用于背景较为简单的场景,对复杂背景的定位效果尚有待改进.在字符分割中,目前单行车牌的分割已比较成熟,但双行车牌的分割仍不理想.提出一种在HSV空间下两次颜色标定和纹理特征相结合的定位方法和一种单双行车牌的字符分割方法.该定位方法利用车牌固定颜色搭配特性,对图片两次标记并利用投影法定位车牌,对200张不同背景图片测试,定位准确率达到98％.在字符分割部分,利用改进的模板匹配方法对字符分割,可适用于单、双行车牌分割,准确率达到95％.     

基于线性优化粒子滤波的捷联惯导初始对准方法

针对标准粒子滤波存在的粒子多样性缺失的问题,提出了一种基于线性优化重采样的粒子滤波算法。该算法计算出了重采样中抛弃的粒子与复制的粒子按照一定的线性组合产生的新粒子,并用这些新的粒子来代替利用简单重采样复制出的粒子,从而避免仅对几个权值较大粒子的重复选取带来的粒子多样性缺失问题。将这种线性优化粒子滤波算法应用到捷联惯导系统的非线性初始对准中进行了仿真研究,仿真结果表明该算法的收敛速度和对准精度都明显优于标准粒子滤波算法,是解决大失准角情况下捷联惯导系统的非线性初始对准问题的一种有效方法。     

Estimation of Particle Size Distribution Based on Observed Weights of Groups of Particles

Particulate matter is collected on some sampling medium. The particles of interest are collected by some means, and weighed as a group. Weights are observed for a number of replicate samples. These observed weights may include background contribution from particles existing on the sampling medium prior to its use. Given these data, plus similar date to establish the background, the problem is to estimate the average number of particles per sample, and their weight distribution. The estimation is accomplished by equating sample moments to population moments. The first four moments of the population are found for an arbitrary weight distribution function possessing finite first four moments. Specific estimates are found in the event the weight distribution is exponential in form, and approximate sampling variances of these estimates are derived. A numerical example is included.     

Efficient cycle time-throughput curve generation using a fixed sample size procedure

The cycle time-throughput curve is one of the most important analytical tools used to assess operating policies in manufacturing systems. Unfortunately, the generation of this curve is complicated and time consuming when generation is based upon extensive simulation analysis. This paper presents a simulation-based, fixed sample size strategy for generating a cycle time-throughput curve with minimal mean square error that mitigates the typical problems associated with a simulation-based cycle time-throughput curve. The strategy comprises two components, the sampling method and sampling weights. A queuing network of five workstations in series is used for validation of the approach. Results indicate that the sampling method using antithetic variates is effective in reducing the variance as well as bias of a cycle time-throughput curve. Furthermore, this method is robust to the sample size. Given a sufficiently large sample, the combination of common random numbers and antithetic variates is preferred. A reduction in the sample size and complexity of the system increases the significance of the sampling weights.     

基于形函数方法快速识别结构动态荷载的试验验证

在动态荷载识别中常常由于矩阵的病态性影响识别的精度,利用有限元理论中的形函数逼近荷载曲线,将识别离散的荷载历程转化为计算有限的形函数权重,从而显著改善反卷积法识别荷载中存在的采样时间长或采样频率高时数值求解困难的问题;并能改善反问题的病态性,提高对噪音的鲁棒性。一个连续梁的数值算例比较验证了该方法在5%的高斯噪声影响下能精确地识别未知荷载。悬臂梁试验中,通过实测的结构动态响应,在移动时间窗内利用荷载形函数方法可以实现激励的在线识别。     

改进均衡分布适配的滚动轴承寿命阶段识别

针对不同工况下训练样本与测试样本分布差异导致滚动轴承寿命阶段无法被有效识别的问题,提出改进均衡分布适配的滚动轴承寿命阶段识别方法。采用无重复均匀随机抽样对源域类间样本进行多次均匀随机抽样,得到源域多样本训练集,以减小源域内部样本选择对目标域预测标签的影响;在再生核希尔伯特空间上利用平衡因子μ动态调节边缘分布和条件分布所占权值,并通过迭代的方式不断优化目标域伪标签以减小两域的最大均值差异;利用源域多样本数据集各自的映射矩阵构造多个分类器,经过一致性判别得到目标域样本最终识别结果。在两组滚动轴承寿命阶段数据集上进行实验验证,证明了所提方法的可行性和有效性。     

Monte Carlo simulation for model-based fault diagnosis in dynamic systems

Fault diagnosis requires the accurate estimation of the dynamic state of the system in real time. This can be pursued starting from a model of the system dynamics and on measurements related to the state of the system. In real applications, the nonlinearity of the model and non-Gaussianity of the noise typically affecting the measurement challenge the classical approximate approaches, e.g. the extended-Kalman, Gaussian-sum and grid-based filters, which often turn out to be inaccurate and/or too computationally expensive for real-time applications. On the contrary, Monte Carlo estimation methods, also called particle filters, can be very effective. Based on sequential importance sampling and on a Bayesian formulation of the estimation problem, these methods recursively approximate the relevant probability distributions of the system state by random measures composed of particles (sampled values of the unknown state variables) and associated weights.The present paper aims at demonstrating the power of particle filtering for fault diagnosis. This is done by applying an estimation procedure called sampling importance resampling (SIR) to a case study of literature.     

Application of time-of-flight aerosol mass spectrometry for the online measurement of gaseous molecular iodine

Here we present a new application of a time-of-flight aerosol mass spectrometer (TOF-AMS) for the measurement of atmospheric trace gases in real-time. Usually, TOF-AMS instruments are not sensitive to gas-phase species due to the aerodynamic particle focusing inlet system which reduces the gas phase species by a factor of about 10(7) relative to the particle phase. This efficient removal of the gas phase and the resulting high relative enrichment of particles is one reason for the very high sensitivity of TOF-AMS instruments for particle phase compounds (detection limits in the sub-μg/m(3)-range for online measurements with 1 min integration time), which allows application of the instruments even under clean atmospheric conditions. Here we use artificially generated particles as sampling probes to transfer selected atmospheric trace gases into the particle phase before entering the AMS (gaseous compound trapping in artificially generated particles-AMS, GTRAP-AMS). The sampling probe particles are mixed with the gaseous analytes upstream of the TOF-AMS in a 0.5 L flow tube. As an exemplary application of the method, the measurement of trace levels of gaseous molecular iodine is demonstrated. α-Cyclodextrin (α-CD/NH(4)Br) particles are used as selective sampling probes to transfer molecular iodine into the AMS. A detection limit in the subparts-per-billion (sub-ppb) range was achieved. The method was compared to a recently developed off-line method that combines denuder sampling of gaseous I(2) and gas chromatography/mass spectrometry (GC/MS) analysis. To demonstrate the usability of the method, temporally resolved I(2) emission profiles from a brown algae species (Laminaria saccharina) under exposure of ambient ozone levels were investigated. Total I(2) release rates of 36.5 pmol min(-1) grams fresh weight (gFW)(-1) at 100 pbb O(3) and 33.4 pmol min(-1) gFW(-1) at 50 ppb O(3) were obtained within the first hour of ozone exposure.     

Optical particle-sizing method that provides optical isolation of the sampling volume

We have constructed and tested a novel particle sizer that employs particle counting (the time-domain approach) rather than the ensemble-diffraction approach that currently dominates the market in atmospheric-dust particle sizers. The method does not depend on mechanical devices to restrict particles within the sampling volume but instead allows for optical isolation of the sampling volume. This technique is useful for atmospheric-dust measurements for which nonobtrusive measurement is often desirable.     

Local Particle Mean Velocity Measurement in Pneumatic Conveying Pipelines Using Electrostatic Sensor Arrays

In this paper, a measurement system for the local mean velocity of pneumatically conveyed particles is proposed and developed. It mainly consists of electrostatic sensor arrays, signal conditioning circuits, and a digital signal processor (DSP)-based data acquisition and processing unit. Electrostatic sensor arrays are used to detect the charge on particles in its sensing zone and further make the local particle mean velocity measurement in conjunction with cross-correlation method. The sampling frequency is determined from theoretical analysis of the bandwidth of electrostatic signal and accuracy of correlation velocity calculation. Experiments are carried out on a belt conveyor and a gravity-fed particle rig to determine the optimized sampling number of the electrostatic signal through analyzing the measurement error of the transit time. The results showed that the more sampling numbers, the higher stability of measurement results. The repeatability of the measurement system is less than ±2.2% and the linearity is better than ±4.9% over the velocity range of 5.50–21.98 m/s. Experiments are also performed on a high-pressure dense-phase pneumatic conveying system of pulverized coal, indicating that the measurement system is capable of achieving local mean velocity measurement of pneumatically conveyed particles with the relative standard deviation less than 5.5%.     

Evaluation of the emission characteristics of trace metals from coal and fuel oil fired power plants and their fate during combustion

Coal as well as fuel oil combustion generates emissions of potentially toxic trace pollutants including organic and inorganic chemical compounds besides major pollutants. A study on As, Cd, Co, Cr, Cu, Hg, Fe, Mn, Ni, Pb, Se, and Zn emissions from a 220 MW coal-fired power plant equipped with a electrostatic precipitators (ESPs) and 6 MW oil fired-power plant was carried out, using stack monitoring kit, Envirotech APM 620, which is similar to EPA Method 29. Simultaneous sampling of coal, fuel oil, oil waste, bottom ash, fly ash, flue gases, and particles associated with the gas phase has been performed. This sampling method was used for trace metal sampling. The content of all these metals in coal, oil, oil waste, bottom ash, fly ash have been determined by XRF, whereas their contents in the flue gases, and particles associated with the gas phase has been analyzed with ICP-AES. The mass balances obtained for trace elements were satisfactory in case of fuel oil based power plant, whereas in case of coal fired power plant, the mass balance for all the trace elements were below 50% except for the As, Se, and Hg. The enrichment factors for all trace metals was <1 in both cases. The above sampling method is moderately adequate method for trace element sampling in coal as well as oil fired power plants except for Hg. The results indicate that trace metals emissions were higher in coal-based power plant than the fuel oil-fired power plant.     

Improved 2D vector field estimation using probabilistic weights

We consider the application of tomography to the reconstruction of two-dimensional vector fields. The most practical sensor configuration in such problems is the regular positioning along the boundary of the reconstruction domain. However, such a configuration does not result in uniform distribution in the Radon parameter space, which is a necessary requirement to achieve accurate reconstruction results. On the other hand, sampling the projection space uniformly imposes serious constraints on space or time. In this paper, we propose to place the sensors regularly along the boundary of the reconstruction domain and employ probabilistic weights with the purpose of compensating for the lack of uniformity in the distribution of projection space parameters. Simulation results demonstrate that, when the proposed probabilistic weights are employed, an average 27% decrease in the reconstruction error may be achieved, over the case that projection measurements are not weighed (e.g., in one case the error reduces from 3.7% to 2.6%). When compared with the case where actual uniform sampling of the projection space is employed, the proposed method achieves a 90 times reduction in the number of the required sensors or 180 times reduction in the total scanning time, with only 7% increase in the error with which the vector field is estimated.