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.

随机误差是影响MEMS陀螺仪精度的重要因素.为了有效处理MEMS陀螺仪的随机误差,采用Allan方差对其进行辨识.通过对比选取Birge-Massart策略确定阈值,在分析硬阈值、软阈值和折中阈值小波去噪的基础上,提出了一种改进的小波阈值去噪算法.该算法构造了一种改进的阈值函数,它具有良好的连续性,一定程度减少了恒定偏差问题.利用MEMS陀螺仪输出信号,分别采用硬阈值、软阈值、折中阈值和改进阈值小波去噪算法进行对比实验.结果表明:改进的小波阈值去噪算法能更好地抑制MEMS陀螺仪的随机误差,处理信号的高频噪声,去噪效果较优.

针对室内环境中对定位精度和可用性不断提高的需求,基于视觉的同步定位与建图算法(SLAM)成为了室内定位领域的研究热点,但是视觉SLAM算法或因采用点作为特征导致其在低纹理环境鲁棒性较低,或因只采用光度计算而对环境过于敏感.由此,提出了基于光度和点线特征融合的半直接单目视觉定位算法,结合基于光度的直接法构建相对稠密的逆深度场景结构进行位姿优化,同时在基于点特征的算法上加入直线特征提高特征的丰富性和位姿估计的准确性.通过EuRoC数据集和实际场景实验表明:所提方法具有更高的位姿估计精度和鲁棒性.

The emergence of dual frequency global navigation satellite system (GNSS) chip actively promotes the progress of precise point positioning (PPP) technology in Android smartphones. However, some characteristics of GNSS signals on current smartphones still adversely affect the positioning accuracy of multi-GNSS PPP. In order to reduce the adverse effects on positioning, this paper takes Huawei Mate30 as the experimental object and presents the analysis of multi-GNSS observations from the aspects of carrier-to-noise ratio, cycle slip, gradual accumulation of phase error, and pseudorange residual. Accordingly, we establish a multi-GNSS PPP mathematical model that is more suitable for GNSS observations from a smartphone. The stochastic model is composed of GNSS step function variances depending on carrier-to-noise ratio, and the robust Kalman filter is applied to parameter estimation. The multi-GNSS experimental results show that the proposed PPP method can significantly reduce the effect of poor satellite signal quality on positioning accuracy. Compared with the conventional PPP model, the root mean square (RMS) of GPS/BeiDou (BDS)/GLONASS static PPP horizontal and vertical errors in the initial 10 min decreased by 23.71% and 62.06%, respectively, and the horizontal positioning accuracy reached 10 cm within 100 min. Meanwhile, the kinematic PPP maximum three-dimensional positioning error of GPS/BDS/GLONASS decreased from 16.543 to 10.317 m.

微机电(MEMS)陀螺仪的随机漂移误差较大,严重影响导航精度.针对上述问题,首先利用Allan方差分析了MEMS陀螺的随机漂移误差;然后基于小波阈值去噪算法处理陀螺信号的高频噪声,建立了硬阈值函数和软阈值函数,并通过两种函数对陀螺信号进行小波阈值去噪处理.实验结果表明:较之硬阈值函数,软阈值函数去噪效果更佳,去噪后信号标准差更低,量化噪声、角度随机游走和零偏不稳定性分别下降了97.34％、97.62％、57.07％.

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.

Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs.

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.

Location fingerprinting suffers in dynamic environments and needs recalibration from time to time to maintain system performance. This paper proposes an adaptive approach for location fingerprinting. Based on real-time received signal strength indicator (RSSI) samples measured by a group of reference devices, the approach applies a modified Universal Kriging (UK) interpolant to estimate adaptive temporal and environmental radio maps. The modified UK can take the spatial distribution characteristics of RSSI into account. In addition, the issue of device heterogeneity caused by multiple reference devices is further addressed. To compensate the measuring differences of heterogeneous reference devices, differential RSSI metric is employed. Extensive experiments were conducted in an indoor field and the results demonstrate that the proposed approach not only adapts to dynamic environments and the situation of changing APs’ positions, but it is also robust toward measuring differences of heterogeneous reference devices.

To support robust indoor localization, it is presented that a dynamic adaptive model (DAM) for WLAN (wireless local area network) location fingerprinting which can provide updated radio maps depending on the real time data from several base stations (BS). The model takes the spatial relationships between the BSs and the sample points of the radio map into account that the data of BSs and radio map is respectively used as the inputs and outputs of multilayer neural networks to update radio maps dynamically. In order to catch tempo-spatial environmental changes, the multivariate outlier detection technique is applied to examine the data of BSs. According to the detecting results, a retraining process and an interpolation method considering the floor plan are used to update the functional model and make the model adapt to tempo-spatial environmental changes. The model is evaluated in indoor dynamic environments. Compared to conventional ones, the average location error of the proposed model-based method decreases more than 10% in time-varying environments; and after spatial environmental changes (radio beacons are moved), its average location error increases 10% to 20% which is much lower than 165% increase of others. Moreover, the localization accuracy is around 3 m, holding the original performance. The results prove the adaptation of the proposed model to the tempo-spatial environmental changes. However, compared to conventional location fingerprinting, the model brings a little more computational overhead.

With the development of economic and people’s living standards, more and more people join the ranks of backpackers, which have brought a lot of pressures to the society. It’s the emergence of an enhanced hybrid location-based services (LBS) to solve this problem. LBS are classified into three categories, i.e. Reactive LBS, Proactive LBS, and Hybrid LBS (H-LBS). The prototype of H-LBS for supporting backpackers in Beijing was discussed. There are several services to support backpackers by H-LBS, for example travel insurance services, tourism planning services, in which kernel density estimation was used to calculate the hotspots, and travel process support services.

Radio frequency (RF) signal propagation suffers from time-varying fading effects, and thus radio map-based localization systems are hard to hold the expected accuracy. Base stations (BS)-based architectures show us the probable solutions to overcome the negative impacts by producing adaptive radio maps. In this chapter, the adaptive approach that is presented in our previous work is adopted. To further mitigate the impacts of dynamic environments, we propose a hybrid location estimation method that fuses WLAN signals and inertial data through the sequential importance resampling (SIR) Particle Filter (PF) algorithm. Experimental results suggest that the hybrid method can provide more accurate location tracking, compared to previous algorithms, such as K weighted nearest neighbors (KWNN), initial radio map-based PF, adaptive radio map-based PF, pedestrian dead reckoning (PDR). And it nearly costs equivalent computational time, compared to those radio map-based PF algorithms.

It is extremely important for pedestrian navigation and location-based services (LBS) that users of portable devices can quickly and accurately locate themselves wherever they might be. In this paper, we propose a novel hybrid approach for indoor and outdoor localization in urban area based on Enhanced Sequential Importance Resampling (Enhanced-SIR) Particle Filter (PF). The approach implements by gathering and processing signal strength information from Wi-Fi and other signals of opportunity, such as wireless sensor network (WSN). It combines WSN (2.4 GHz Zigbee) with Wi-Fi for hybrid location estimation, in which, Wi-Fi signal is used for the entire area, and WSN signal serves as the enhanced signal to assist the resampling of particles. The method of Enhanced SIR can not only solve the problem of degeneracy, but also improve the sampling accuracy of particles. Experimental results demonstrate that the proposed hybrid approach can provide more accurate location estimates in indoor and outdoor areas, and costs equivalent runtime, compared to conventional SIR PF.

无线通信网络的增值推动了适宜和有效的室内外定位的发展。提出WLAN环境下多基站辅助的自适应定位方法,此方法基于由多个基站信息及其与位置指纹样本点之间的映射关系实时生成的位置指纹库估算移动站的位置。其中,基站与位置指纹样本点对应信号强度之间的映射通过BP神经网络构建。此方法不但能够适应环境中小尺度扰动因素,还能够通过对基站信息的内插适应大尺度环境变化情况。另外,此方法采用的信号强度差分度量,能有效削弱多个设备之间的差异造成的定位误差。试验结果显示该方法能够在动态环境中精确定位目标。

Ubiquitous positioning shows great potential for wireless applications but confronts lots of fundamentally technical problems. Recent advanced positioning systems and emerging technologies are described,and a novel ubiquitous positioning technology by using the widely available and economically signals of opportunity in urban is proposed. It also include the principles,characteristics,and trends issues in ubiquitous positioning. An initial investigation test was conducted at a typical urban canyon area in Hong Kong.The result shows that the signals of opportunity assisted ubiquitous positioning demonstration prototypes facilitate a variety of wireless and geo-informatics applications.