Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

Kalman inverse filtering is used to develop a methodology for real-time estimation of forces acting at the interface between tyre and road on large off-highway mining trucks. The system model formulated is capable of estimating the three components of tyre-force at each wheel of the truck using a practical set of measurements and inputs. Good tracking is obtained by the estimated tyre-forces when compared with those simulated by an ADAMS virtual-truck model. A sensitivity analysis determines the susceptibility of the tyre-force estimates to uncertainties in the truck’s parameters.     

Onboard tuning of vessel seakeeping model parameters and sea state characteristics

It is essential for a safe and cost-efficient marine operation to improve the knowledge about the real-time onboard vessel conditions. This paper proposes a novel algorithm for simultaneous tuning of important vessel seakeeping model parameters and sea state characteristics based on onboard vessel motion measurements and available wave data. The proposed algorithm is fundamentally based on the unscented transformation and inspired by the scaled unscented Kalman filter, which is very computationally efficient for large dimensional and nonlinear problems. The algorithm is demonstrated by case studies based on numerical simulations, considering realistic sensor noises and wave data uncertainties. Both long-crested and short-crested wave conditions are considered in the case studies. The system state of the proposed tuning framework consists of a vessel state vector and a sea state vector. The tuning results reasonably approach the true values of the considered uncertain vessel parameters and sea state characteristics, with reduced uncertainties. The quantification of the system state uncertainties helps to close a critical gap towards achieving reliability-based marine operations.     

Real-time bus route state forecasting using particle filter and mesoscopic modeling

In the absence of system control strategies, it is common to observe bus bunching in transit operations. A transit operator would benefit from an accurate forecast of bus operations in order to control the system before it becomes too disrupted to be restored to a stable condition. To accomplish this, we present a general bus prediction framework. This framework relies on a stochastic and event-based bus operation model that provides sets of possible bus trajectories based on the observation of current bus positions, available via global positioning system (GPS) data. The median of the set of possible trajectories, called a particle, is used as the prediction. In particular, this enables the anticipation of irregularities between buses. Several bus models are proposed depending on the dwell and inter-stop running time representations. These models are calibrated and applied to a real case study thanks to the high quality data provided by TriMet (the Portland, Oregon, USA transit district). Predictions are finally evaluated by an a posteriori comparison with the real trajectories. The results highlight that only bus models accounting for the bus load can provide valid forecasts of a bus route over a large prediction horizon, especially for headway variations. Accounting for traffic signal timings and actual traffic flows does not significantly improves the prediction. Such a framework paves the way for further development of refined dynamic control strategies for bus operations.     

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.     

A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

并行扩展卡尔曼滤波的船舶模型参数辨识研究

随着人类对海洋的开发,对船舶数学模型的要求越来越高。为确定船舶运动模型的未知参数,首先针对船舶特点建立相应的数学模型,并提出一种并行扩展卡尔曼滤波算法,该方法通过一种并行计算使辨识效率更高,之后设计辨识实验,将得到的辨识结果进行仿真,并且与传统扩展卡尔曼辨识的结果进行对比,仿真结果表明改进的算法优于扩展卡尔曼滤波。     

一种舰船辐射噪声线谱简易生成方法研究

为得到逼真的舰船辐射噪声线谱仿真数据,需要在低信噪比情况下对实测噪声数据作谱估计,从而准确提取舰船辐射噪声线谱成分,再采用相应数字信号处理方法重构舰船辐射噪声线谱信号。本文提出一种简易重构方法,即根据特征线谱生成线谱所在频段频谱信号,将其它频段置零,进而在时域重构线谱信号。仿真结果表明,该方法能够很容易地重构舰船辐射噪声线谱信号,与特定幅频响应的连续谱信号一起重构的舰船辐射噪声信号,其结果可作为半实物仿真试验信号源。     

单通道混合信号粒子滤波盲分离算法的快速实现

粒子滤波可以处理非线性非高斯问题,可以应用于混合信号的盲分离中,是一种有效的盲分离算法。但是粒子滤波算法存在一个很大的缺陷,其复杂度太大,为了减少粒子滤波运算时间、提高运算效率,通过对粒子滤波实现过程中的符号向量进行前置扩维,从而改进了粒子滤波实现方法。仿真结果表明：改进后的算法在运算速度上相比之前有了比较明显的提升。     

弹载无线电高度表测高能力拓展方法研究与应用*

高度表的测高能力决定了导弹纵向弹道的控制品质,影响导弹飞行高度的准确性和飞行安全。论文详细分析了决定高度表测高能力的主要因素,提出了拓展高度表测高能力的基本方法和实现途径,结合工程实践完成了量程拓展型高度表控制环节的改装设计以及接收机通频带、鉴频特性等关键参数的调整与优化。实验结果表明,该方法使该型高度表的量程增加了一倍以上,提升了装备的使用价值,满足了导弹弹道形态拓展的需求。     

一种新的INS/LOG组合导航算法

针对电磁计程仪引入的海流速度可能会导致INS/LOG组合导航系统的卡尔曼滤波器输出发散问题,设计一种新的INS/LOG组合方式。以计程仪速度2次采样的差分值(即速度增量)作为系统观测量以抑制慢变海流的影响,采用基于延迟状态卡尔曼滤波算法,推导这种组合方式的观测方程,实现INS误差的最优估计。为抑制计程仪速度差分对其高频噪声的放大效应,采用巴特沃斯低通滤波器对其输出进行平滑处理。通过对3种INS/LOG组合方式的仿真比较,验证该算法的有效性。