基于应力松弛试验用有限元预估车辙

为了使车辙预估变得更为简便,提出一种基于应力松弛试验的改进的有限元车辙预估方法。研究结果表明：不仅从粘弹性力学的角度分析,应力松弛试验在理论上与Maxwell模型具有一致性,而且实例计算结果也验证了此方法的可行性。该方法便于不熟悉本构理论的工程人员进行车辙预估。     

基于出租车GPS数据的路段平均速度估计模型

为了解决现有基于GPS数据的路段平均速度估计模型应用条件苛刻、难以满足低成本和高精度信息需求问题,考虑不同类型GPS车辆运行特征,设计了两个基于出租车GPS数据估计路段平均速度的改进模型.基于两个改进模型,设计了路段平均速度的融合估计方法.用某城市局部路网的出租车GPS数据验证两个改进模型,并与传统模型进行了对比分析....     

Adaptive robust control for an active heave compensation system

Active heave compensation systems are usually employed in offshore and deep-sea operations to reduce the adverse impact of unexpected vessel’s vertical motion on the response of underwater instruments.This paper presents a control strategy for an active heave compensation system consisting of an electro-hydraulic system driven by a double rod actuator,which is subjected to parametric uncertainties and unmeasured environmental disturbances.Adaptive observer and discontinuous projection type updating law with bounded adaption rate are presented firstly to estimate the uncertain system parameters.Then a similar estimation algorithm is designed by using a multiple delayed version of the system to enhance the performance of parameter observation.A reduced order observer is also introduced to estimate unknown wave disturbances.Using the obtained uncertainty information,the resulting control development and stability analysis are implemented based on the Lyapunov’s direct method and back-stepping technique.The proposed controller guarantees the heave compensation error convergent to a bounded neighborhood around the origin.Simulations illustrate the effectiveness of the proposed control system.     

诱发电位潜伏期变化的分析处理方法

研究了诱发电位的主要检测参数潜伏期变化的估计测定方法：广义相关时间延迟方法、处适应时间延迟估计方法和自适应相位谱时间延迟估计方法,并分析了动态跟踪潜伏期时变规律的有效性。     

DRM同步与均衡技术研究

DRM（Digital Radio Mondiale）是世界上唯一非专利的数字广播系统,它为30MHz以下频段的长波、中波、短波广播提供了数字化的标准。同步算法和信道估计作为其关键技术对系统性能有着十分重要的影响,文章提出了适用于DRM系统的符号同步算法、整数倍载波频偏估计算法、小数倍载波频偏估计算法以及时频域联合信道估计和均衡算法,并对这些算法进行了仿真分析。     

Iterative Sequence Detection Combined with Channel Shortening and Sphere Decoding in Underwater Acoustic Communications

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLS-DFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.007 6 to 0.003 7 in the 8-12 kHz band and from 0.151 6 to 0.114 5 in the 13-17 kHz band at a distance of 2 000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.     

自动驾驶目标检测不确定性估计方法综述

随着自动驾驶技术的发展,目标检测的准确性和可靠性变得至关重要。深度学习作为自动驾驶系统中的核心组成部分,其预测结果的不确定性估计对于系统的安全性和稳定性具有显著影响。总结了深度学习不确定性估计理论在自动驾驶目标检测中的应用,并探讨了有效的不确定性评价体系的重要性。介绍了深度学习不确定性估计的基本理论,包括贝叶斯神经网络、蒙特卡洛方法以及集成学习方法等。这些方法通过不同的途径量化模型预测的不确定性,为自动驾驶系统提供了更丰富的信息。深入探讨了自动驾驶目标检测中不确定性估计的应用。通过案例分析,展示了如何利用不确定性信息来提高目标检测的准确性,特别是在面对复杂环境和极端条件时,不确定性估计可以作为决策支持,帮助系统避免潜在的风险。总结了自动驾驶目标检测不确定性估计评价指标,同时,考虑了模型的预测性能、不确定性估计的准确性。     

基于信号质量监测的高效鲁棒的多结构GNSS接收机

为保证复杂环境下全球卫星导航系统(GNSS)导航性能的同时兼顾接收机的运行效率,充分利用传统标量跟踪环路(STL)、矢量跟踪环路(VTL)以及直接位置估计(DPE)接收机的优势,设计了一种基于信号质量监测的多结构GNSS接收机,通过实时监测卫星信号质量自适应切换接收机的工作模式;除基本的STL、VTL和DPE这3种工作模式外,进一步在STL和VTL模式中增加排除多径通道模式和窄相关技术抗多径模式,在DPE模式中增加排除多径通道模式。研究结果表明：对于仿真信号来说,卫星信号质量良好的环境下,接收机进入STL模式,水平和垂直定位误差分别为2.20和4.65 m,多径出现时切换至STL抗多径模式,对应的水平和垂直定位误差分别为3.23和18.18 m,较切换前的STL模式(误差分别为28.07和112.24 m)显著减小,当存在卫星遮挡时,接收机切换至VTL工作模式,此时水平和垂直定位误差分别为7.24和38.44 m,优于切换前的STL抗多径模式(误差分别为16.59和110.10 m),当进入弱信号环境时,接收机切换至DPE模式,对应的水平和垂直定位误差分别为3.24和17.30 m,较切换前模式(误差分别为4.47和24.89 m)进一步改善,仿真试验结果验证了多结构接收机能根据实时信号监测结果切换至最佳工作模式;对于实测数据来说,信号质量良好的情况下接收机在STL模式工作,水平和垂直定位误差分别为7.74和13.19 m,当部分卫星被遮挡时,接收机切换至VTL模式,水平和垂直定位误差分别为16.07和9.31 m,进入弱信号环境下,切换至DPE模式,对应的水平和垂直定位误差分别为6.72和48.99 m,均优于切换前的VTL模式。