一种顾及有色噪声的四星座GNSS动态导航滤波算法

经典Kalman滤波要求噪声是高斯白噪声，而动态GNSS定位的观测误差和状态预测误差往往是有色噪声。本文提出一种简便的有色噪声函数模型拟合滤波算法，采用前面历元的观测残差和状态残差建立有色噪声模型，削弱有色噪声对动态导航解算的影响。采用四星座GNSS接收机观测数据进行动态导航实验，结果表明，顾及有色噪声的动态导航滤波算法比未顾及有色噪声的经典Kalman滤波算法定位精度更高，三维位置精度提升9%以上。     

顾及时变非高斯噪声的高斯和滤波及其导航应用

高斯和滤波可利用高斯混合模型精化非高斯噪声随机模型来提高估计精度,但导航测量环境的动态性和复杂性使非高斯噪声具有时变性特征,若GMM不随之调整会导致滤波解算失真。针对该问题,本文提出一种基于位移参数自适应估计的高斯和滤波算法。首先分析GMM位移参数对非高斯噪声拟合精度的影响,然后利用位移参数自适应技术修正GMM,进而改善高斯和滤波性能。实验结果表明,当GNSS/SINS量测模型存在时变非高斯噪声时,本文算法的滤波结果较传统高斯和滤波算法的波动小,抗干扰能力强,在实际应用中可进一步改善估计精度和稳定性。     

基于航向角变化的趋势集合轨迹特征划分算法

在GPS或网络信号受到干扰或遮蔽时,导航精度和自主性会大大降低,组合导航技术能弥补这一缺陷。针对组合导航匹配实时、快速和准确的应用需求,本文提出一种具有实时性、高准确性的基于航向角变化的趋势集合特征划分算法。该算法通过计算轨迹点的航向角变化量,对趋势确定集合和趋势待定集合的趋势状态进行赋值,能有效地划分出轨迹上各段的特征点集合。算法还对由外界因素（如超车、紧急避让和驾驶员不同驾驶习惯等）引起的无效特征情况,进行趋势集合状态的分析和判断,以保证特征划分的准确性。本文以北京西五环地区的车辆轨迹数据为例,进行了实地跑车实验表明,基于航向角的趋势集合特征划分算法,在实时性和提取效果方面,具有明显的优势,算法简单可行、高效、可操作性强。     

一种改进的UKF滤波算法在BDS/SINS组合导航系统中的应用研究

将自适应估计原理引入UKF（unscented Kalman filter）算法中,该改进算法可以自适应地调节系统模型信息在导航解中的比重,从而减少滤波器初值偏差和系统模型异常扰动对导航解的影响。将改进的UKF算法应用于BDS/SINS（北斗/捷联惯导）组合导航系统中,仿真结果验证了其有效性,且该算法可提高组合导航系统的精度。     

非高斯有色噪声影响下的高斯混合滤波算法

针对GNSS/SINS紧组合定姿定位系统在非高斯有色噪声环境下直接使用高斯混合扩展卡尔曼滤波（Gaussian mixture extend Kalman filter, GMEKF）出现的随机模型失配现象,提出一种顾及非高斯有色噪声影响的GMEKF优化算法。该算法在高斯混合模型对非高斯噪声近似的基础上,通过状态扩增和量测组差对非高斯噪声序列中的有色噪声成分进行白化处理,从精化随机模型的角度改善了GMEKF算法的性能。实验结果表明,在非高斯有色噪声环境下,相比于GMEKF,该算法对随机模型建模更准确,将其用于受非高斯有色噪声影响的定姿定位场合中可进一步提高导航解算精度。     

基于零速修正辅助MIMU的行人导航算法研究

针对微惯性测量单元(MIMU)误差积累问题,根据行人行走过程中每一次迈步的落地阶段短时间内速度可保持为0的特性,在零速时刻利用系统输出对系统误差进行估计,从而提高导航精度。对比分析基于加速度、角速度、加速度方差和加速度与角速度组合的4种零速检测算法,结果表明,在步态平缓、行走距离较短的情况下,4种算法都能达到较高的精度,其中组合检测法精度最高。     

Multi-Objective Weather Routing Algorithm for Ships Based on Hybrid Particle Swarm Optimization

Maritime transportation has become an important part of the international trade system.To promote its sustainable de-velopment,it is necessary to reduce the fuel consumption of ships,decrease navigation risks,and shorten the navigation time.Ac-cordingly,planning a multi-objective route for ships is an effective way to achieve these goals.In this paper,we propose a multi-ob-jective optimal ship weather routing system framework.Based on this framework,a ship route model,ship fuel consumption model,and navigation risk model are established,and a non-dominated sorting and multi-objective ship weather routing algorithm based on particle swarm optimization is proposed.To fasten the convergence of the algorithm and improve the diversity of route solutions,a mutation operation and an elite selection operation are introduced in the algorithm.Based on the Pareto optimal front and Pareto optimal solution set obtained by the algorithm,a recommended route selection criterion is designed.Finally,two sets of simulated navigation simulation experiments on a container ship are conducted.The experimental results show that the proposed multi-objective optimal weather routing system can be used to plan a ship route with low navigation risk,short navigation time,and low fuel consumption,fulfilling the safety,efficiency,and economic goals.     

Uncertainty Assessment of Soil Erosion Model Using Particle Filtering

Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.     

Kalman滤波在GPS/BDS组合伪距差分定位中的应用

随着各国卫星导航系统的蓬勃发展,单一的GPS系统时代正逐步转变为多系统并存且兼容的全球性卫星导航系统(GNSS)时代。相比于单一卫星导航系统,多系统组合将显著增加可视卫星数目、改善卫星空间几何结构,多系统组合导航定位将是必然的发展趋势。滤波算法是减小GNSS定位随机误差的重要方法,利用非线性滤波方法可消除多种随机误差,从而提高导航定位精度。该文实现了基于Kalman滤波的GPS/BDS组合的伪距差分定位,并将其与最小二乘方法进行比较。实验结果表明:基于Kalman滤波的GPS/BDS伪距差分的定位精度能达到分米级,在差分定位解算过程中,多卫星系统伪距差分精度明显优于单卫星系统伪距差分精度,Kalman滤波解算的精度明显优于最小二乘解算的精度。     

基于软件接收机的BDS/MEMS IMU深组合导航性能分析

针对BDS/MEMS IMU深组合导航全物理实验难度较大的问题,提出一种基于软件接收机的BDS/MEMS深组合导航系统仿真分析方法。给出BDS软件接收机结构,介绍深组合导航系统结构设计及滤波算法,最后基于仿真数据进行验证。结果表明,在高动态条件下,就BDS/MEMS IMU导航系统而言,其深组合的导航精度较紧组合有较大提高,位置误差小于1 m,速度误差小于0.01 m/s。