基于扩展卡尔曼滤波器的AUV导航定位数据融合

导航定位是AUV(自主式水下潜器)自动化受限的关键技术.GPS等单独的定位方式又存在缺陷.故采用低成本罗经-DVL-GPS组合导航定位.因为导航定位观测的非线性关系,设计了基于扩展卡尔曼滤波器的导航定位数据融合算法.针对扩展卡尔曼滤波器存在的滤波发散现象,引入机动目标模型抑制滤波发散,研究并仿真了AUV的两种运动方式,实现了覆盖四个象限运动的导航定位仿真,结果表明算法能有效进行数据融合,提高状态估计精确度.     

输入饱和及带宽限制下高超飞行器的闭环稳定边界研究

针对于吸气式高超声速飞行器开环不稳定的动力学特性,研究了控制信号存在饱和约束及带宽限制条件下的闭环稳定边界.首先,简要介绍了吸气式高超声速飞行器的建模方法与动力学特性的主要问题.考虑到飞行器控制信号的幅值限制及带宽约束,综合高超声速飞行器的开环不稳定特性,定量地分析了系统的闭环稳定边界:与系统不稳定极点的位置,其对应的左特征向量及控制信号的幅值约束有关;执行器的带宽限制在此基础上进一步缩小了反馈控制系统的稳定边界.根据高超声速飞行器短周期不稳定特性,解析地给出了闭环稳定边界的计算公式.采用蒙特卡洛分析方法对闭环系统的稳定边界及滑模变结构控制器作用下的稳定区域进行验证.仿真结果与理论分析具有一致性,验证了系统开环特性对于闭环稳定性的限制及控制信号带宽约束对稳定性的影响.     

固定翼无人机的自抗扰反步控制

针对固定翼无人机姿态和速度控制中系统存在模型不确定性和外界扰动的情况,本文设计了基于扩张状态观测器的反步控制器抑制系统扰动以提高无人机的控制性能.首先建立无人机速度误差模型和姿态误差模型,其中姿态误差模型采用四元数作为变量以避免欧拉角在描述姿态时存在的奇点问题和复杂三角运算;进而设计扩张状态观测器对系统中存在的扰动进行估计,并将扰动估计值与控制器设计相结合,分别设计出姿态控制器和速度控制器来抑制扰动的影响且使无人机姿态和速度收敛到期望值.最后基于李雅普诺夫理论证明系统的稳定性.仿真结果表明,本文所设计方法能够抑制系统中存在的扰动.     

Integrated control design for driver assistance systems based on LPV methods

The paper proposes a control design method for a driver assistance system. In the operation of the system, a predefined trajectory required by the driver with a steering command is followed. During manoeuvres the control system generates differential brake moment and the auxiliary front-wheel steering angle and changes the camber angles of the wheels in order to improve the tracking of the road trajectory. The performance specifications are guaranteed by the local controllers, i.e. the brake, the steering, and the suspension systems, while the coordination of these components is provided by the supervisor. The advantage of this architecture is that local controllers are designed independently, which is ensured by the fact that the monitoring signals are taken into consideration in the formalisation of their performance specifications. The fault-tolerant control can be achieved by incorporating the detected fault signals in their performance specifications. The control system also uses a driver model, with which the reference signal can be generated. In the control design, the parameter-dependent linear parameter-varyingmethod, which meets the performance specifications, is used. The operation of the control system is illustrated through different normal and emergency vehicle manoeuvres with a high-accuracy simulation software.     

Moving-in-pulse duration model-based target integration method for HSV-borne high-resolution radar

Hypersonic vehicles (HSVs) offer great advantages over airplanes and satellites, owing to their hypersonic speeds and flexible trajectories. However, HSV-borne radar may suffer from performance degradation if traditional target integration methods with stop-and-go (SAG) approximation are applied; this is especially true for high resolution radar and applications that utilize long-time coherent integration. In this paper, we consider the effects of moving-in-pulse duration (MPD) to derive an HSV-borne radar signal model without SAG approximation by specifically characterizing platform motion during the transmission and reception of a pulse. An explicit formula for wavenumber domain echo is derived using the 2-D joint stationary phase method. To mitigate the pulse-dependent echo distortion induced by the MPD effect, a target integration method based on the omega-K algorithm is proposed; this method employs revised filters for bulk focusing and revised Stolt interpolation for differential focusing to improve the overall focusing quality. The paper discusses the ambiguity functions of in-range MPD echo models, and describes the performance metrics for the integration results in both dimensions, including impulse response width (IRW), peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR). Simulation results have verified the effectiveness of the proposed method in MPD circumstances.     

固定翼巡线无人机实时蚁群算法路径规划

本文针对传统蚁群算法局部早熟等问题对算法进行了改进,并对无人机路径规划及重规划多条件约束问题进行了研究,提高蚁群算法航迹规划计算速度及全局性来满足实时蚁群算法航迹规划要求。文章针对传统蚁群算法的早熟问题采用全局与局部信息素互补衰减法,来高效完成蚁群算法寻优问题,并当无人机偏离航线时能及时根据无人机所在位置重新规划。通过实验结果得出蚁群算法具有快速的计算能力,能在短时间内对更改的路径进行响应,完成无人机自校正航迹规划。     

基于数据优先级和车辆密度的安全数据的转发算法

在VANET中,为了保证安全数据的准确性和实时性,和安全相关的数据通常需要较高的优先级并且优先传输,现有的VANET数据转发算法不能满足这一点。为了满足VANET中安全数据的优先传输需求,提出了一种数据优先级模型,并基于数据优先级和车辆密度提出了一种安全数据转发算法。数据优先级模型可以给不同状态的数据分配相应的优先级;基于数据优先级和车辆密度的数据转发算法PDT动态分配计数器阈值和碰撞窗口,动态分配碰撞窗口可以确保数据的传输次序,而动态设定计数器阈值既能够保证数据的可靠传输又能有效地抑制广播风暴。仿真结果表明,与传统的counter-based数据转发算法和slotted-p数据转发算法相比,在数据吞吐量、传输时延和抑制广播风暴方面,PDT算法都有显著的性能提升。     

计算机视觉技术的车型识别应用研究

针对目前立体停车库主要采用传统的传感器入库检测系统来识别车辆信息,存在施工周期长、器件损坏率较高,维护成本较高的问题,提出了一种应用计算机视觉技术的车型识别解决方案.应用卷积神经网络框架Caffe,基于Caf-feNet模型,通过fine-tuning模型优化以及参数优化,最终得到了一个性能较优异的识别模型.实验结果表明,该模型可克服输入图片背景复杂多变,目标被遮挡的情况,对轿车车型识别这一问题鲁棒性好,具有一定的可行性及应用价值.     

基于位置的服务在车联网系统中的应用

车联网作为物联网在汽车行业的应用领域之一,是汽车智能化的重要发展方向,介绍基于位置的服务,并详细描述该服务的一个应用实例：一套完整的基于位置服务的车辆调度管理系统;介绍该系统的部分功能模块的详细功能以及该系统在实践中的应用,说明该系统应用前景和社会效益。