基于遗传算法的潜艇自航模水声定位系统定位站布置研究

根据自航模试验中水声定位系统遇到的具体问题,抽象出了三种优化模型,并且提出了衡量特定阵型定位精度的目标函数,编制了适用于该优化模型的遗传算法程序,通过优化模型Ⅰ验证了该程序的正确性。此后,利用该程序分别计算出了模型Ⅱ、Ⅲ定位站最佳布置位置,得出水下布防2个水听器形成十字阵为最佳布阵型式这一结论,总结出新的布阵方法,即在岸边先选取距离尽量远的两个点布防水听器,之后在两点连线的中垂线附近布防两个水下水听器,具体位置可以通过遗传算法算出。     

自导深弹脱靶距离测量方法研究

脱靶距离是衡量自导深弹制导控制能力的终级指标,指自导深弹跟踪目标时与目标之间的最小距离。试验时需要对其进行精确测量。提出了同步测距进行脱靶距离测量,简要阐述了系统的组成及工作原理,对系统的软硬件设计作了简要说明。系统测距精度高,使用方便,能满足工程需要。     

多途信道短慕线时间反转聚焦研究(英文)

The existence of a multi-path channel under the water greatly decreases the accuracy of the short baseline positioning system.In this paper,the application of a time reversal mirror to the short baseline positioning system was investigated.The time reversal mirror technique allowed the acoustic signal to better focus in an unknown environment,which effectively reduced the expansion of multi-path acoustic signals as well as improved the signal focusing.The signal-to-noise ratio(SNR) of the time reversal operator greatly increased and could be obtained by ensonifying the water.The technique was less affected by the environment and therefore more applicable to a complex shallow water environment.Numerical simulations and pool experiments were used to demonstrate the efficiency of this technique.     

鱼雷攻潜末弹道测量中的抗多途专家系统

依据鱼雷攻潜末弹道测量定位模型,对所接收鱼雷上加装的声信标脉冲序列结构进行仿真,并就如何从带有干扰的接收数据中挑选出直达脉冲进行研究。依此总结出的抗多途专家系统,有效解决了直达声脉冲识别难题,提高了测量精度。实验室测试表明,应用该专家系统的处理方法,测距直达脉冲和测深直达脉冲的正确选择概率超过95%。     

基于模拟统计法的水声对抗试验航路优化

在建立水声对抗试验航路优化评估标准的基础上,利用Monte_Carlo方法,研究了悬浮式和自航式声诱饵对抗声自导鱼雷的优化试验航路问题,并对建立的模型进行了计算机仿真。仿真结果表明,该方法对于研究水声对抗试验航路优化是一种行之有效的方法。     

Optimal locations of electric public charging stations using real world vehicle travel patterns

We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quickly regardless of charging speed.     

Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour

Road designers assume that drivers will follow the road alignment with trajectories centred in the lane, and move at the design speed parallel to the road centreline (i.e., the horizontal alignment). Therefore, they assume that if the horizontal alignment indicates the “designed trajectory”, the driving path indicates the “operating trajectory”. However, at present, they do not have the necessary tools to measure the relationship between the designed alignment and possible vehicle trajectories.The paper has two objectives: (a) to develop an understanding of the root causes of differences between road alignment and vehicle trajectories; and (b) to define and calibrate a model that estimates the local curvature of trajectories on the basis of the designed horizontal alignment.The two objectives were pursued by carrying out a naturalistic survey using vehicles equipped with high precision GPS in real-time kinematics (RTK) mode driven by test drivers on road sections of known geometric characteristics. The results provide an insight into the effects of road geometrics on driver behaviour, thus anticipating possible driving errors or unexpected/undesired behaviours, information which can then be used to correct possible inconsistencies when making decisions at the design stage.     

Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity.     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.     

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO₂ emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.