Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation.

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm’s effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3–1.7 m.

In global navigation satellite system (GNSS)-based positioning and applications, multipath is by far the most obstinate impact. To overcome paradoxical issues faced by current processing approaches for multipath, this paper employs an intrinsic method to identify and mitigate multipath based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT). Frequency spectrum and power spectrum are comprehensively employed to identify and extract multipath from complex data series composed by combined GNSS observations. To systematically inspect the multipath from both code range and carrier phase, typical kinds of combinations of the GNSS observations including the code minus phase (CMP), differential correction (DC), and double differential (DD) carrier phase are selected for the suggested intrinsic approach to recognize and mitigate multipath under typical positioning modes. Compared with other current processing algorithms, the proposed methodology can deal with multipath under normal positioning modes without recourse to the conditions that satellite orbits are accurately repeated and surrounding environments of observing sites remain intact. The method can adaptively extract and eliminate multipath from solely the GNSS observations using intrinsic decomposition mechanism. From theoretical discussions and validating tests, it is found that both code and carrier phase multipath can be identified and distinguished from ionospheric delay and other impacts using the EMD based techniques. The resultant positioning accuracy is therefore improved to an obvious extent after the removal of the multipath. Overall, the proposed method can form an extensive and sound technical frame to enhance localization accuracy under typical GNSS positioning modes and harsh multipath environments.

The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

室内定位需求急剧增加,普及的智能手机带来了解决问题的一种方法.本文提出了一种基于智能手机的粒子滤波室内融合定位方法.利用三轴加速计和三轴罗盘等微机电系统(micro-electromechinical system,MEMS)传感器数据估计目标的运动状态信息,利用WiFi数据更新运动状态,实现融合定位.室内动态环境下实验结果表明,融合定位方法平均定位误差小于2m,其有效利用智能手机平台获取多种传感器数据,很好地结合了行人航迹推算方法和K加权最近邻方法的优势,在定位精度和稳健性方面均有良好表现.

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

The expansion of Location-based services and applications leads to extensive interests on smart phone-based indoor and outdoor localization. Rich sensors embedded in smart phone support varies of localization techniques, provide infrastructural elements for indoor and outdoor seamless localization solutions. The pedestrian dead reckoning (PDR) system based on smart phone-embedded MEMS sensors plays an important role in a seamless localization system, since it can link up different absolute positioning systems (such as BeiDou Navigation Satellite System (BDS), WiFi localization systems, etc.) flexibly. However, as a relative localization system, it is limited to location error accumulation, and therefore it cannot run for long. The problem can also affect the performance of a seamless localization system. As a result, in order to improve the tracking performance of the PDR system in complex environments indoors and outdoors, a method based on Robust Adaptive Extended Kalman Filtering (RAEKF) is proposed. The method includes heading and speed estimation, for heading estimation, outputs from gyroscope, accelerometer, and magnetometer sensors are used, and for speed estimation, only outputs from accelerometer are used. RAEKF is employed both in heading and location estimation. Although speed and location estimation refer to different state and measuring models, the proposed filtering can be applied flexibly. The M-estimator is used to handle measurement outliers. To weaken the impacts of dynamic disturbance errors for heading and location estimation, an adaptive factor is introduced to adjust their models respectively. Extensive experiments on static and dynamic localization were conducted in indoor complex environments. And the experimental results demonstrate the proposed method provides more accurate and robust performances, compared with methods based on conventional EKF.

针对室内及卫星信号遮蔽区域的定位问题,提出了基于粒子滤波的混合定位方法.此方法通过采集和处理WiFi和其他泛在信号,比如WSN(wireless sensor network,2.4GHz Zigbee)的混合信号强度信息实现位置估算,其中,WiFi数据作为滤波过程的主要观测量,而WSN数据作为增强信息辅助粒子重采样来提高关键区域的定位精度.在卫星信号遮蔽区域开展了实验,结果表明,与单一定位方法相比,混合定位方法取得了更高的定位精度.

Inertial navigation based on Micro-Electro-Mechanical System (MEMS) sensors has recently received much attention. Attitude heading estimation of an indoor moving object, as one of the important parts of inertial navigation is still worth studying. Attitude heading estimation using the acceleration and magnetic sensors fixed on the indoor moving object can easily be disturbed by surrounding environments or other factors. The gyroscope can also be used for computing the attitude heading. However, due to its own characteristics that the estimated errors gradually accumulates, the accuracy is not reliable for a long time. In this paper, quaternion-based Extended Kalman Filter algorithm is proposed to estimate the attitude heading of indoor moving object combining the acceleration sensor, magnetic sensor and gyroscope. For the method, the quaternion derived from gyroscope's readings is selected as the state vector of the filter, and the quaternion calculated from the measurements of accelerometer and magnetometer is used for the observations of the filter. The real-time estimated results are achieved by the state update of the designed filter. The experiment result shows that the proposed method can effectively improve the accuracy of the attitude heading estimation, compared with the complementary filtering.

针对传统的CAD地形图数据向ArcGIS数据转换的需求越来越高的现状,该文以某市的计算机辅助设计地形图为实例,讨论了ArcGIS工具模块、FME软件、C/S转换平台、编程这4种数据转换方法的优缺点和适用范围.利用这几种方法对计算机辅助设计地形图数据进行转换,并将转换结果进行对比分析.实验结果表明,基于FME软件的转换方法最为简便、可行,在一定的条件下转换的结果精度更高、更可靠.

An improved non-equidistant GM(1,1)model is proposed for the problem that the traditional non-equidistant GM(1,1)model is not enough high accuracy in the settlement monitoring of buildings.The model is based on the improved initial conditions and the improvement on the representation of grey derivatives which is expressed by the discrete form in the whitening equation of the grey differential equation,combined with the instance of a high-size building with the integration of business and residence in a square of Guilin.The results of this model which are compared with the results of other non-equidistant GM(1,1)methods in the literature predict the analysis and test of settlement.The feasibility and superiority of this model is verified by the instance of the building subsidence deformation analysis and prediction,it has played a positive role in the application of the non-equidistant GM(1,1)model in the prediction of settlement deformation.

According to the problem that the SHP database layer note cannot directly be converted into the text annotation of the CAD,the paper puts forward a new method that realizes the process that the ArcGIS layer with the notes converted into the CAD topographic map layer.It can realize the process that the ArcGIS layer with the notes converted into the CAD topographic map layer by the method used the ArcMap software and Database,which is better to solve the matter that the SHP format note cannot directly be transformed into the text annotation of the CAD.With an ArcGIS layer as the data source,the experimental results show that this method can be better handle the ArcGIS annotation layer,and provides a feasible scheme for the ArcGIS layer transformed into CAD topographic map,which is of great significance for the realization of data conversion.

针对CAD数据与GIS数据之间转换存在差异性,处理过程繁琐等问题,提出一种新的转换方法实现CAD数据向GIS数据转换的过程,通过开发C/S系统,实现将.dwg格式数据转换成.shp数据,很好地解决现有转换方法存在的问题和缺陷,为CAD地形图顺利入库提供一种很好的方案.最后以某区域的CAD地形图为数据源,通过实验分析,验证现所提出的方法的可行性和有效性,不仅简化数据转换的处理流程,而且提高了数据处理的效率,为实现CAD数据高效处理和数据的同步入库提供一种有效的方案.

针对直接利用空间数据操作引擎将CAD注记数据转换成ArcGIS数据与原CAD图文字注记数据存在一定差异的问题,提出了一种新的转换注记方法。利用Arc Map提取CAD原图注记层数据,较好的减少了FME转换的注记的差异,为地形图的转换提供了可行的方案。以某市的地形图为数据源进行实例论证,实验结果显示所提的方法不仅能够更好地保持数据的精度和完整性,还有利于数据的入库。研究结果对从事地理信息系统开发的技术人员具有一定的参考价值。

针对GIS数据与计算机辅助设计(CAD)数据之间的差异性,以及现有转换方法存在的问题,该文提出了一种计算机辅助设计数据处理方法——采用空间数据操作引擎(FME)和Arc Map联合使用的方法.该方法能够较好地解决计算机辅助设计数据向GIS数据转换的难题,实现了对计算机辅助设计数据的批量转换和处理.以某市的地形图为数据源进行实例论证,实验结果显示运用该文所提方法能够更好地处理计算机辅助设计数据,为计算机辅助设计地形图的转换提供了可行的方案;特别是对转换出来的数据进行了拓扑重建、附属性值以及多边形数据分类处理,能够更好地保持数据的精度和完整性,更有利于数据的入库.

This article, firstly, reveals the difference between the text annotation of original CAD Map and the results converted to ArcGIS using Feature Manipulation Engine (FME). Then we propose a strategy, extracting the annotation layer information directly from original map on ArcMap platform and figuring out the conversion errors of annotation information, to correct the errors. The strategy not only guarantees high conversion accuracy, but also improves work efficiency dramatically. Taking topographic map of a city for example, an experiment was carried out. The results show that this strategy could better deal with CAD data, and it provides a feasible scheme for topographic map conversion.