The semantic information has been increasingly incorporated as pose optimization constraints into visual simultaneous localization and mapping (vSLAM) for autonomous vehicles against complex environments. However, existing semantically constrained SLAM systems are still vulnerable to incomplete object observations and mobile objects, both of which are prevalent in dynamic vehicle environments. To mitigate these challenges, we propose a semantically constrained SLAM method by extracting and utilizing reliable object landmarks. This method first detects the motion status of objects using both geometric and semantic information, specifically tailored to address degenerate motions in driving scenarios. Subsequently, object-level landmarks are constructed from the identified static features and objects. These landmarks are then used to establish semantic constraints, which are adaptively weighted based on the reliability of each landmark, for camera pose optimization. Experimental results using a self-collected vehicle-borne dataset characterized by multiple categories of dynamic objects and severe partial observations demonstrate that the proposed system improves the 3-D positioning accuracy by approximately 68% and 29% compared with two state-of-the-art (SOTA) semantic vSLAM systems. Experimental evaluations on diverse sequences from the public KITTI and TUM RGB-D datasets are also performed to further validate the superior positioning accuracy and robustness provided by the proposed system across both dynamic outdoor and indoor environments.

As one of the main components of the Earth orientation parameters, short-term prediction of the geodetic polar motion series is crucial in the field of deep-space exploration, high-precision positioning, and timing services, which require high real-time performance. Additionally, its middle- and long-term prediction is equally important in climate forecasting and geodynamics research. In this study, we propose the combined BiLSTM+ARIMA model, which is based on bidirectional long- and short-term memory (BiLSTM) and autoregression integrated moving average (ARIMA). First, ensemble empirical mode decomposition (EEMD) is performed as a filter to decompose the polar motion time series to obtain low- and high-frequency signals. The EOP14 C04 time series provided by International Earth Rotation and Reference Systems Service and decomposed by EEMD includes low-frequency signals like the long-term trend, decadal oscillation, Chandler wobble, and prograde annual wobble, along with shorter-period high-frequency signals. Second, low- and high-frequency signals are predicted using BiLSTM and ARIMA models, respectively. Finally, the low- and high-frequency signal forecast components are reconstructed to obtain geodetic polar motion predictions. In middle- and long-term polar motion prediction, the results show that the proposed model can improve the prediction accuracy by up to 42

Exploring land use evolution and its impact on carbon storage is crucial for mitigating climate change in resource-based cities and promoting green, low-carbon development. This study constructs a GMOP-PLUS-InVEST coupled model and utilizes remote sensing data from five phases of land use from 2000 to 2020. Four scenarios are established to simulate the future patterns of land use and carbon storage changes in Jining City. The results indicate that: (1) from 2000 to 2020, farmland, forest land, and grassland in Jining City show a declining trend; while construction and waters increase, resulting in a reduction of carbon storage from 167.35 × 10⁶ t in 2000 to 159.85 × 10⁶ t in 2020; (2) coal mining significantly influences nearby land utilization types and carbon storage, leading to a decline in nearby carbon reserves; (3) compared to the other three scenarios, the coordinated development scenario exhibits higher land use efficiency and carbon storage, with lower levels of human disturbance; balancing the local economy and environment, and serving as a sustainable pattern of land use for the area. The outcomes of this paper quantitatively reflect the relationship between land use, coal mining, and carbon storage in high-water-level resource-based cities; providing guidance for the local economy, urban development, and ecological environment protection.

Identification of the optimal noise model for space geodetic time series is crucial for various studies in geodynamics and geodesy. However, the position time series of Very Long Baseline Interferometry (VLBI) are typically irregularly sampled. There is no clear evidence from previous studies to support that the Maximum Likelihood Estimate (MLE) method can allow for stable and reliable noise estimates for irregularly sampled data. To address this issue, we first conducted simulation studies. Results indicate that when the number of data points in the VLBI position time series reaches 300, reliable noise estimations result can be obtained using the MLE. We also found that the higher proportion of white noise a station has, the less influences the data gaps would be on noise estimations. Second, we apply the MLE to investigate the noise properties in the 32 VLBI position time series. About 12 (37.5%) stations exhibit pure white noise, and 15 (46.9%) stations display a combination of white noise and flicker noise. For these stations, the median amplitudes of white noise in the East, North, and Up components are 3.9, 4.6, and 11.9 mm, respectively. Moreover, white and flicker noise amplitudes show a latitude-dependent pattern, with stations located in the southern hemisphere and low latitudes (0-30 degrees N) in the northern hemisphere exhibiting higher noise amplitudes. The remaining approximately 5 stations (15.6%) have the optimal noise model of power law noise with the mean spectral index of -0.32, -0.36, and -0.53 for the East, North, and Up components, respectively. Simply assuming pure white noise can significantly affect velocity uncertainty estimates when compared to using a noise model that includes colored noise. The velocity uncertainty will be underestimated by a factor of 2.9 on average. The effects of a small proportion of colored noise in VLBI data on velocity uncertainty estimation can be ignored only when the time series length exceeds 32.6 years. Additionally, the choice of different noise model can lead to a maximum difference of 0.84 mm for the annual signal of the Up component. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

火星极移属于火星定向参数,通过对火星极移时间序列周期项进行研究,可以更加深入地了解火星内部结构及大气和潮汐运动的特征与规律.利用NASA提供的NP.ang和NP.ds火星极移序列进行时变分析与预报,时变分析使用快速傅里叶变换(fast Fourier transform,FFT)进行处理.结果表明,NP.ang与NP.ds两组数据的周期项长度基本一致,且与火星年具有很强的相关性.短期预报结果表明,使用差分整合移动平均自回归(autoregressive integrated moving average,ARIMA)模型对NP.ds进行20 d内的短期预报是一个可行的思路;中长期预报结果表明,使用最小二乘外推模型进行NP.ds数据的中长期预报是可行的,且使用准确度更高的周期项进行最小二乘外推模型的拟合,可以显著提高对NP.ds的中长期预报精度.本文研究结果可为火星极移的预报研究提供参考.

The Earth Rotation Parameters (ERPs) are estimated with the BeiDou Navigation Satellite System (BDS), GPS, and Galileo obser-vations from approximately 100 selected International Global Navigation Satellite System (GNSS) Service (IGS) tracking stations around the world. The results are compared with the IGS final products (IGSF) and the International Earth Rotation and Reference Systems Service (IERS) series 14C04 at 12 UTC. The accuracy of the ERPs derived from the GPS-only reaches the IGS accuracy level. The standard deviations of the differences in the X-pole, Y-pole, and Length of Day (LOD), compared with the IGSF, are 23 mu as, 20 mu as, and 7 mu s/day, respectively. Meanwhile, the standard deviations of the differences in the X-pole, Y-pole, and LOD, in contrast with IERS 14C04, are 28 mu as, 25 mu as, and 9 mu s/day, respectively. Similarly, the ERPs are estimated using BDS-only and Galileo-only observations from approximately 100 selected IGS stations. For ERPs derived from BDS Medium Earth Orbit (MEO-only) satellites, the standard deviations of the differences in the X-pole, Y-pole, and LOD, compared with the IGSF, are 76 mu as, 53 mu as, and 19 mu s/day, respectively, and 75 mu as, 55 mu as, and 19 mu s/day, respectively, compared with IERS 14C04. Likewise, for the ERPs derived from Galileo-only obser-vations, the standard deviations of the differences in the X-pole, Y-pole, and LOD, compared with the IGSF, are 48 mu as, 31 mu as, and 12 mu s/day, respectively, and 48 mu as, 30 mu as, and 14 mu s/day, respectively, in contrast with IERS 14C04. Despite the three orbital planes, tracking station distribution, and unoptimized models for the BDS MEO satellites, these results are close to the IGS accuracy level. The impact of spurious system-specific signals on these solutions is investigated using spectral analysis, a notable spurious signal appears for the 3-plane BDS MEO and Galileo constellations. The pronounced peaks at the frequencies of 2.97 cycles per year (cpy) for Polar Motion and Polar Motion rate, 1.98 cpy for LOD are significantly larger than those from GPS.(c) 2023 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

利用 IERS(International Earth Rotation and Reference Systems Service)发布的 EOP 14 C04 产品中1984～2022 年 LOD实测值进行频谱分析及周期项提取,并采用最小二乘外推模型联合多项式曲线拟合模型方法将提取到的周期项应用于对 LOD序列的拟合.实验结果表明,相比于单一的 LS外推模型拟合,新方法拟合序列的RMSE从 0.0003 s下降至 0.0001 s,且新方法将确定系数从 0.80 提高到 0.97 左右.该研究结果可为 LOD序列的预报研究提供参考.

在嫦娥三号探月工程中,月球天平动参数是影响其着陆器定位精度的重要参数,研究月球天平动参数估计方法对于中国探月工程的发展具有重要意义.目前,月球激光测距(lunar laser ranging,LLR)是解算月球天平动参数的常用方法,该方法通过数值拟合将拟合系数放在历表文件中供用户读取.首先,基于差分甚长基线干涉测量技术(very long baseline interferometry,VLBI)解算月球物理天平动参数,其仿真结果表明,与DE421星历的插值结果对比,其欧拉角Ω、i和μ的改进值分别补偿至-0.6924"、0.009 6"和-0.009 7";然后,基于补偿后的月球物理天平动参数求解着陆器坐标和速度,结果显示,相比于补偿前,着陆器在X、Y、Z方向的定位精度分别提高了 24.204m、0.405 m、1.996 m,速度误差的估计精度也分别提高了 0.010 6 m/s、0.013 5 m/s、0.007 2 m/s.上述研究结果可为未来月球天平动参数解算的相关研究提供参考.

NeQuick2 is a three-dimensional ionospheric electron density empirical model that uses numerical integration to calculate the total electron content along any line-of-sight (LOS). As one of the most commonly used three-dimensional ionospheric models, it is necessary to objectively evaluate the accuracy and stability of NeQuick2 over a long period, especially over the mid-latitudes of the northern hemisphere where most of the ground-based GNSS stations are distributed. Therefore, different methods are used in this study to evaluate the accuracy of the NeQuick2 model from 2008 to 2021, including comparison with the International Global Navigation Satellite System Global Ionosphere Maps (IGSG), Jason2 Vertical Electron content (VTEC), and self-consistent evaluation. The comparison with IGSG shows that the standard deviation (STD) value is about 2.59 TECU. The accuracy of the IGSG and NeQuick2 model over ocean regions shows that the bias of IGSG is more significant than that of the NeQuick2 model. The mean STD value is 2.09 TECU for IGSG, and the corresponding value is 3.18 TECU for the NeQuick2 model, which is about 50% worse than IGSG. The dSTEC assessment results indicate that the variation in bias for IGSG is more stable than that of the NeQuick2 model. The mean STD value is 0.86 and 1.52 TECU for IGSG and NeQuick2 model, respectively. The conclusion could be made that NeQuick2 model represents the average ionosphere electron content and its accuracy fluctuates with solar conditions. Compared with the IGSG, the NeQuick2 model always underestimates TEC value, especially in low solar activity periods and compared with Jason2, the TEC values obtained by NeQuick2 model are overestimated, but the degree of overestimation is smaller than that of IGSG.

闰秒是协调世界时(coordinated universal time,UTC)为了调和天文时(universal time 1,UT1)和国际原子时(international atomic time,TAI)这两套不同的时标方法之间愈来愈大的累积差距而采取的"强制性的"时标修改机制.因为闰秒会带来诸多弊端,所以在本世纪初科学界就开始研究如何取消闰秒.但是,由于地球自转的影响因素过于复杂,人类目前的研究进度远没法掌握其确切规律,所以完全消除闰秒是一件非常困难的事情.因此,尽可能地延长闰秒的周期,减少跳秒频率,是可以通过缩小UT1和TAI之间的累积差距来完成的.通过对IERS(International Earth Rota-tion Service)近11年来的地球定向参数(earth orientation pa-rameter,EOP)数据进行筛选、处理和数学分析,最终确定了一套可以有效减少闰秒的天文时与原子时的对应关系,提出了可以减少世界时闰秒调整频率的原子时秒长调整方法.

利用中国探月甚长基线干涉测量(very long baseline interferometry,VLBI)观测数据改进月球物理天平动参数的预测精度,对于着陆器和巡视器的精密定位具有重要意义.利用VLBI单点定位模型解算得到"嫦娥三号"(Chang'E-3,CE-3)着陆器的坐标和物理天平动,分别采用循环神经网络(recursive neural net-work,RNN)和长短期记忆(long-short term memory,LSTM)网络进行物理天平动的预测.选取月球着陆器的坐标和VLBI观测量作为输入量,将3个欧拉角Ω,i,μ作为输出量,将11 323个样本用于训练,2 315个样本用于测试,2 315个样本用于验证,1000个样本用作与预测结果进行对比.结果显示,验证集的数据经过1000次训练和9次迭代训练后的梯度约为6.2×10-5(")/s,证明了 LSTM网络与RNN的可靠性.LSTM网络和RNN的3个欧拉角的预测精度分别达到了 97.8％、99.7％、97.2％和95.2％、98.5％、95.8％,LSTM网络的预测精度更高.与DE421星历对欧拉角的预测结果进行比较,结果证明了 LSTM网络预测精度更高.

地球自转速度变化是导致全球气候变暖的重要原因之一,而探讨地球自转速度变化与气温变化的影响的相关性,并尝试建立一定的相关性模型是其中一种研究方式.首先分别利用地球自转速率数据与全球气温数据分析与建模,并判断模型温度预测的可行性,发现仅可进行短期温度预测;研究全球温度数据与地球自转速度变化数据相关性,进行相关性分析以及显著性检验,可知二者有强正相关性;最后研究气温预测的可行性,对气温和自转速率数据回归拟合,利用拟合公式,进行实际与预测的地球自转速率温度预测实验,结果证明推测方法可行.实验以IERS官网获取的日详细地球自转速度变化数据,以及伯克利地球组织获取的全球气温数据为例.

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

In recent years, smart city has attracted more and more attention because of its high intelligence and informatization. In the future, smart community, smart parking lot and smart exhibition hall will enter people's life. In these smart services, indoor navigation and positioning technology is the basic technology. High precision indoor and outdoor seamless positioning technology plays a vital role in people's future life, is the science and technology source power to promote public innovation and entrepreneurship, and is an important part of supporting the national strategic needs. At present, indoor navigation is still facing challenges such as high cost, poor adaptability and poor endurance. In this paper, combined with threedimensional( 3D) visualization technology, indoor navigation is designed and applied. The results show that the indoor navigation system combines indoor positioning and artificial intelligence to achieve multichannel positioning and reduce power consumption, which can be widely used in the construction of smart city and bring convenience to people's life.

首先推导了基于星光角距观测值的月球探测器天文导航方法的观测方程;然后根据月球车的运动模型建立了系统状态方程,并利用扩展卡尔曼滤波方法建立了天文导航模型;最后对月球探测器的地月转移轨道段飞行进行了模拟,分析了仿真结果.

Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the fingerprint image required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters location. Additionally, the observers coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.

针对整数最小二乘模糊度降相关平差(LAMBDA)算法解算高维整周模糊度效率比较低的问题,该文从模糊度解算一般规则是一个非线性整数规划问题的角度出发,提出了一种改进模拟植物生长算法.该算法是一种智能优化算法.通过多组高维模拟数据和实测数据,将该文算法与LAMBDA算法及MLAMBDA算法进行了对比分析.结果 显示,当模糊度维数等于45维和50维时,该文算法在运算效率上略优于LAMBDA算法.当维数达到55维及以上时,相比于LAMB-DA和MLAMBDA算法运算速度分别提高了至少52.8％和19.2％.因此改进模拟植物生长算法对于快速固定高维整周模糊度具有一定的应用参考价值.

精密单点定位(precise point positioning,PPP)已经广泛应用于许多领域,如测绘、交通、导航、地震监测等.近些年来,随着卫星数量的增多,多系统组合呈现越来越明显的趋势.利用全球MGEX (Multi-GNSSExperiment)网数据研究了BDS(BeiDou navigation satellite system)/GPS(global positioning system)组合精密单点定位技术,并与BDS单系统和GPS单系统进行了对比.结果表明,在静态定位中,BDS PPP在E、N、U方向的均方根误差分别为4.35 cm、3.01 cm、6.40 cm;GPS PPP在E、N、U方向的均方根误差分别为1.21 cm、0.48 cm、1.79 cm;BDS/GPS组合PPP在E、N、U方向的均方根误差分别为1.21 cm、0.50 cm、1.87 cm.在动态定位中,BDS PPP外符合精度水平方向优于io cm,高程方向优于15 em;GPS PPP和BDS/GPS组合PPP的外符合精度水平方向均优于5 cm,高程方向均优于8 cm.另外,无论是在静态还是动态的PPP中,组合系统相对于单系统,能大大缩短收敛时间,减少定位结果抖动,尤其是相对于BDS PPP来说,优势更为明显.

Celestial navigation is a method of autonomous navigation without relying on the remote control of ground stations. In this article, first, the observation equation of celestial navigation of the lunar probe based on observations of the starlight angle is established. Next, a systematic state equation is established according to motion model of the lunar probe, then the model of celestial navigation by extending the method achieved by Kalman filtering. At last, a transfer orbit of the lunar probe from the Earth to the Moon is simulated and the simulation result is analyzed.

在利用GPS解算地球自转参数的过程中,测站分布对解算的效率和最终的解算精度会产生比较大的影响.本文设计三种不同的测站分布的方案来研究测站分布对GPS解算地球自转参数的影响.实验结果表明,测站分布对极移xp、yp影响较大,对UT1-UTC的影响较小.测站均匀分布在全球有助于提高计算结果的精度和稳定性.