Blind receiver for OFDM systems via sequential Monte Carlo in factor graphs

Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.

Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

利用序列Monte Carlo的OFDM系统符号定时校正

针对非高斯、非线性的正交频分复用(OFDM)系统下的符号同步定时校正,常规的卡尔曼滤波算法不能直接解决非线性、非高斯的问题,因而提出一种序列蒙特卡罗(SMC)算法.首先将符号定时偏差以及系统的观测方程建模为动态的系统传递模型;然后用加权的离散随机样本点近似所关注的后验概率密度函数,在频率选择性多径信道估计和跟踪OFDM系统符号定时变化.从仿真结果可以看出,SMC算法在相同信噪比(SNR)下的误比特率(BER)及定时均方误差(MSE)均好于扩展卡尔曼滤波(EKF)算法.

LSSVM-Monte Carlo定价高维美式期权

随着美式期权维数的增加,存在所谓的“维数灾难”问题,为了克服这一难题,最小二乘支持向量机(LSSVM)被应用于定价高维美式期权.首先用M-C方法仿真美式期权标的物的多条价格路径,接着采用最小二乘支持向量机作为求条件期望的回归算子,并提出了一种基于改进序列优化(ISMO)的LSSVM的训练算法.针对4种不同的美式期权支付函数,给出了该方法应用于标的资产的个数分别为5和30的算例.研究结果表明,所提出的方法能很好地解决高维美式期权定价问题.

PET/PBT反应性共混酯交换动力学及其Monte Carlo模拟

在前文研究的基础上 ,对 PET/ PBT反应性共混过程中的酯交换动力学作了定量表征 ,发现该过程很好地符合二级反应动力学 ,活化能为 12 0 k J/ m ol,较文献报道的低。Monte Carlo模拟能较好地反映酯交换过程中各序列结构参数与共混温度及时间的变化关系 ,共混温度越高 ,酯交换速度越快 ,序列长度变短 。

Semi-analytical Monte Carlo simulation of laser-induced fluorescence propagation in an optically participating spray

A semi-analytical Monte Carlo (SMC) simulation was developed to simulate the propagation of laser-induced fluo- rescence (LIF) in an optically participating spray, which simultaneously exhibits spectrally dependent emission, anisotropic scattering, absorption, and re-emission. The SMC simulation is described and then applied to an experimental configuration of a cloud of polydisperse droplets composed of water and sulforhodamine B dye. In the SMC simulation, the collected LIF flux on the remote receptor element is calculated as the global contribution from the emissive source, single, twice, … and nth collision events in any sequence. The effects on the fluorescence photons propagation of spray parameters like the dye concentration, droplets concentration, and droplets size are examined. Three spectral bands representing different optical properties are chosen to analyze the interference of absorption, scattering and re-emission on the detected LIF flux. The obtained spectral LIF flux distribution on the receptor demonstrates a “red shift” phenomenon.

序贯Monte Carlo算法仿真结果分析

序贯Monte Carlo方法能够解决很多实际问题。它的系统模型与Kalman滤波算法相比具有更广泛的适用性,所以研究Monte Carlo方法是很有实际意义的。文中对序贯Monte Carlo算法进行性能分析,对这一方法的跟踪能力进行了仿真实验。采用的仿真系统模型是非线性系统模型。仿真实验比较了EKF、SIS、SIR算法的性能。通过对不同算法的仿真结果之间的分析和比较,得出了有意义的结论。这对一些工程问题的解决是有重要意义的。

SSO Risk Evaluation for a Grid-connected PMSG-based Wind Farm Based on Sequential Monte Carlo Simulation

In a grid-connected wind farm based on permanent magnet synchronous generators(PMSGs),the wind speed and the number of operating PMSGs are the two most important influencing factors along with the stochastic nature of sub-synchronous oscillation(SSO)from the point view of the farm.This paper proposes a method of unstable SSO risk evaluation for grid-connected PMSG-based wind farms based on the sequential Monte Carlo simulation(SMCS).The determination of critical wind speed(CWS)of SSO and the sequential simulation strategy of wind speed states and PMSG states in a wind farm at the same wind speed(S-WF),as well as in a wind farm at different wind speeds(D-WF),are studied.Five indices evaluating the expectation,duration,frequency and energy loss of SsO risk are proposed.Moreover,a strategy to reduce SsO risk by adjusting the cut-in wind speed is discussed.The effectiveness of the discussed issues in this paper are proved by the case studies of a 750-PMSG wind farm based on the actual wind speed data collected.

计及随机性的多分布式电源接入中压配电网承载能力评估

电力系统的源侧和荷侧均具有随机性,影响配电网接入分布式电源的容量分析。为此,提出了一种考虑源荷随机性的中压配电网多分布式电源承载能力评估方法。首先,将分布式电源和负荷出力分解为可预测变量和随机变量,结合序贯蒙特卡洛模拟法,分别模拟时序周期内源荷随机生产模型。其次,在划分供电分区和简化源荷接入位置基础上,以各供电分区可接入分布式电源容量最大为目标,考虑传输容量、电压偏差、短路电流等约束,对配电网承载能力模型进行随机潮流求解。然后,以指标变化量和限值距离为衡量依据,确定多分布式电源有序接入策略,防止局部运行参数不优而导致配电网对分布式电源承载能力受限。最后,将该模型应用在中部某城市的区域配电网中,验证该方法的可行性和有效性。

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

考虑时变因素的高速公路新能源微网可靠性评估

在“碳中和”和“碳达峰”背景下,高速公路新能源微网逐渐步入大众视野,对该系统可靠性进行评估是开展其可靠性设计的必要前提,对于保障微网安全稳定运行也具有重要意义。首先,对系统中元件进行可靠性建模,基于热损得到换流器的实时故障率模型从而对分布式电源元件的故障概率进行修正,再对系统引入气象因子来量化气象条件的影响,从而得到整体的时变故障概率;其次,根据系统中可再生能源高分散度的特点采用馈线分区的方法对系统可靠性建模进行简化,也便于进行孤岛划分;然后,在时变故障概率的基础上采用时序蒙特卡洛法构建系统可靠性评估模型,并根据时变负荷用能特性引入负荷削减策略,通过仿真分析得到系统的三种可靠性指标,并为识别高速公路新能源微网的薄弱环节提供依据,从而有效提升运维效率与水平。

基于SMC-PHDF的部分可分辨的群目标跟踪算法

提出一种基于粒子概率假设密度滤波器(Sequential Monte Carlo probability hypothesis density filter,SMC-PHDF)的部分可分辨的群目标跟踪算法.该算法可直接获得群而非个体的个数和状态估计.这里群的状态包括群的质心状态和形状.为了估计群的个数和状态,该算法利用高斯混合模型(Gaussian mixture models,GMM)拟合SMC-PHDF中经重采样后的粒子分布,这里混合模型的元素个数和参数分别对应于群的个数和状态.期望最大化(Expectation maximum,EM)算法和马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)算法分别被用于估计混合模型的参数.混合模型的元素个数可通过删除、合并及分裂算法得到.100次蒙特卡洛(Monte Carlo,MC)仿真实验表明该算法可有效跟踪部分可分辨的群目标.相比EM算法,MCMC算法能够更好地提取群的个数和状态,但它的计算量要大于EM算法.

基于SMCS-NSGAⅡ的独立型微网优化配置

针对独立型微网优化配置问题,采用风速、光照强度及负荷的概率模型和序贯蒙特卡罗模拟法处理风光出力和负荷的不确定性。建立了以总净现费用为经济性优化目标和以年二氧化碳排放量为环保性优化目标的多目标优化配置模型。通过设置概率约束的方法处理相关不确定性下供电可靠性约束问题,最后采用改进的非劣排序遗传算法对某算例进行仿真计算。讨论了供电可靠性置信水平和柴油价格增长对优化配置结果的影响,结果表明所提优化配置模型和方法的有效性。

基于SMC-CPHD的多目标跟踪算法研究

针对CPHD滤波算法在多目标跟踪中计算难处理和对于局部目标估计存在漏检的问题,提出了基于序贯蒙特卡罗方法的基数概率假设密度(SMC-CPHD)滤波算法。这种方法是将SMC和CPHD两种滤波算法的优点相结合,用一些离散的粒子去接近PHD函数,不仅解决了在滤波修正步没有闭式解的问题,而且避免了当某个目标发生漏检时,PHD权值的转移问题,在递推PHD函数的同时也递推基数分布。将此方法应用到有杂波存在复杂的多目标跟踪环境中,通过仿真实验,对CPHD滤波和SMC-CPHD滤波得出的结果进行比较,验证了本文所提出方法对多目标跟踪的可行性和精确性。

基于星凸RHM的扩展目标SMC-PHD滤波

扩展目标跟踪与传统目标跟踪不同,不仅需要对目标的运动状态进行跟踪,同时对于目标的外形特征也不能忽略。针对扩展目标跟踪过程中存在的外形拟合和非线性的问题,提出一种基于星凸随机超曲面模型(RHM)的扩展目标序贯蒙特卡洛概率假设密度滤波(SMC-PHD)算法。该算法运用星凸RHM对扩展目标量测源建模,在SMC-PHD的框架下,推导出非线性滤波算法的量测似然表达式和更新方程,实现扩展目标跟踪。仿真结果证明,所提算法的跟踪性能较其他滤波对于目标扩散程度和质心估计均有提高。

MSMC跟踪算法在目标跟踪中的应用

针对序贯蒙特卡罗(Sequential Monte Carlo,MC)算法存在的计算量大的缺点,提出了一种新的Mean Shift MonteCarlo(MSMC)目标跟踪算法。在传统的MC算法中采取Mean Shift这种梯度最优下降法来寻找局部最大样本值,以目标的颜色特征建立目标状态空间模型,并用Bhattacharyya系数作为评价函数给出样本的权值。算法以少于300个样本(实验用200个样本)来保持对目标运动状态预测的多样性,有效地克服了MC算法收敛速度较慢的弱点,将算法的计算时间从76 ms/frame降低到了35 ms/frame(跟踪窗口为28 pixel×26 pixel)。实验结果表明,提出的算法能够在发生遮挡的情况下实现较稳定的目标跟踪,使算法应用于实际工程成为可能。

SMC框架中引入残差信息的分层重采样策略

近年来序列蒙特卡罗理论及其应用在自动导航、非线性估计与金融等诸多领域受到了越来越广泛的关注。提出了一种引入残差信息的分层重采样策略,通过引入当前粒子集权值的残差来构建累积分布函数,同时针对随机区间逐级分层以产生有序的随机数集合,从而提高重采样的合理性与采样效率。首先从仿真实验的角度证明了它的有效性,对比残差重采样、多项式重采样与遗传重采样,提出的重采样策略在后验均值误差、均方差与运行时间方面均为最小;将提出的重采样策略嵌入到运动目标跟踪算法中,基于标准测试视频的跟踪结果同样佐证了该重采样策略的收敛性及良好的抗噪性能。

机动目标跟踪的交互多模型泊松多伯努利混合滤波

满足共轭先验性质的泊松多伯努利混合(Poisson multi-Bernoulli mixture,PMBM)滤波器将目标状态分为泊松和多伯努利混合两部分,分别对这两部分进行预测和更新,具有较高的跟踪精度和较快的运行速度。在多目标机动场景下,使用单一模型不足以描述目标的运动,将导致跟踪性能的下降。针对这一问题,提出了一种交互多模型(interacting multiple model,IMM)PMBM滤波器,充分利用模型之间的交互信息,可以有效实现多机动目标的跟踪。同时,该算法采用序贯蒙特卡罗(sequential Monte Carlo,SMC)方法实现PMBM滤波,可应用于非线性场景。仿真结果表明,所提的IMM-SMC-PMBM算法可以有效地在非线性环境下跟踪数目变化的多机动目标,与IMM-SMC概率假设密度(probability hypothesis density,PHD)滤波器相比具有更好的跟踪精度和稳定性。

基于JMS-SMC-PHD滤波的检测前跟踪算法

针对低信噪比条件下机动目标的检测与跟踪问题,提出跳跃马尔可夫系统下的序贯蒙特卡罗概率假设密度(JMS-SMC-PHD)滤波的检测前跟踪算法。该算法在机动目标数目和模型未知情况下,直接利用红外传感器量测数据,通过在目标状态矢量中增加模型变量并利用马尔可夫模型概率转移矩阵结合序贯蒙特卡罗概率假设密度(SMC-PHD)滤波,实现机动弱小目标的检测前跟踪。仿真结果表明所提方法可以有效地实现目标的检测与跟踪。

一种用于多目标跟踪的增强型SMC-PHD滤波算法

该文对标准型SMC-PHD滤波器作了两点改进。第一,提出基于观测值的目标个数和目标状态估计方法,该方法首先计算以观测值为行、存活粒子为列的权值矩阵,将按行计算的权值和与判决门限比较,把大于门限的观测值判决为真实观测值,并据此估算目标个数和目标状态。第二,为每个粒子分配表示存活年龄的辅助变量,以抑制强杂波环境下的目标数高估问题。仿真实验表明,在强杂波环境下,增强型SMC-PHD算法在多目标跟踪稳定性方面优于标准型SMC-PHD算法。