A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obtain smoothing localization results. The proposed method analyzes the joint error distribution of the GNSS/visual SLAM system, uses the visual frame to invert the relative error offset of the GNSS from the dimensions of time and localization, performs error analysis and mutual verification based on the verification threshold. According to the mutual verification results, the GNSS spoofing is identified, and the corresponding back-end optimization strategy is selected to obtain a smoothing localization result. Through simulation, the time verification threshold and localization verification threshold of the proposed method are obtained under the condition that the sensors frequency and accuracy are set. The KITTI datasets in rural and urban scenes are used for verification. The simulation results show that our method can identify GNSS spoofing and provide credible and smoothing localization results in the case of GNSS spoofing occurs.

High-precision indoor positioning problems have attracted considerable attention recently. The indoor ranging and positioning method based on WIFI round-trip-time (RTT) has become popular. However, resulting from clock drift (CD), there are two problems that are still not completely resolved and need to be addressed. The first problem is ranging error drift, namely the reset of phase distortion errors (PDEs), while the second problem is the communication state change, namely ranging information loss and restoration. To solve these two problems, this study noticed that the reset of PDEs will only change along with the changes in communication states. Thus, a monitoring-based positioning system (MPS) framework is presented based on the theoretical model of clock drift as a method to account for ranging error drift and communication state change when estimating position with IEEE 802.11mc protocol. Then, a state monitoring algorithm (SMA) suitable for the MPS framework is proposed to accurately capture the moment of communication state change. Further, a PDE constraint (PDEC) model is proposed based on the SMA to estimate PDEs in different communication states to reduce the impact of CD on positioning results. The experimental results finally show that considerable improvement in positioning accuracy can be obtained using the SMA and PDEC model compared with that obtained using the semi-parametric solution algorithm

In indoor positioning through the Internet of Things (IoT), the accuracy of anchor locations profoundly impacts receiver localization. At present, the determination of anchor positions primarily relies on traditional manual surveying techniques. The considerable costs of manual surveying have led the academic community to advocate for anchor self-localization methods as viable alternatives. However, existing methodologies prove inadequate in complex indoor environments lacking supplementary information sources, particularly in addressing non-line-of-sight (NLOS) errors that impact anchor positioning accuracy. To address these challenges, this study utilizes ultra-wideband (UWB) signal sources as a case study. We mainly propose a novel anchor self-localization method that relies solely on ranging measurements. The method integrates the proposed NLOS bias model based on temporal and spatial correlation to overcome NLOS effects. The method initially employs an initialization algorithm to obtain coarse estimates of anchor positions through multiepoch least squares (LS) joint estimation, followed by a two-step iterative algorithm that utilizes the expectation maximization principle to optimize these estimates further. The effectiveness of the proposed method is validated through anchor self-localization tests in indoor environments with decimeter-level positioning accuracy results. The root mean square error (RMSE) of the subsequent receiver positioning results was 0.1936 m, validating that the attained anchor positions satisfy pragmatic application standards.

Indoor and outdoor seamless higher-precision positioning has recently become a research hotspot. Heterogeneous sensor fusion technology has emerged as a popular approach to achieving seamless positioning technology. However, the problem of ping-pong effects in cross-domain areas between indoors and outdoors still poses a challenge to high-precision localization. This paper proposes a smartphone-based smooth positioning method for indoor-outdoor crossover areas that utilizes Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and Ultra-WideBand (UWB) localization techniques. Essentially, seamless positioning is implemented by optimizing the sub-filter factor allocation structure in the two-stage filter structure of the Error-State Kalman Filter (ESKF) and the federal filter. Additionally, a monitoring and optimization algorithm based on empirical mode decomposition (EMD) and grey prediction model is designed to enhance positioning accuracy and system robustness. The experiment results verify that in indoor-outdoor cross-region environments, this method has higher positioning accuracy and robustness than the environmental discrimination based seamless positioning method. Moreover, the algorithm ensures the smooth transition of positioning effect when the environment changes. This method distinguishes from other solutions by weighting different sensor data based on the data itself rather than environmental discrimination, which can suppress the impact of the ping-pong effect on the results and obtain smoother, more reliable, and higher-precision positioning performance.

Ultra-wideband (UWB) indoor positioning relies on precise base station (BS) locations. However, regular receiver positioning may become unfeasible in non-cooperative environments or when BS locations are inaccurate. Existing BS establishment methods require manual calibration using techniques such as laser ranging or automated surveying with auxiliary information sources like IMU. Efficient estimation and calibration of base station positions solely based on relative ranging measurements remains unattainable. To address the issue, this study proposes a BS position self-calibration method that solely relies on relative ranging measurements, solving the problem of the automatic estimation of non-cooperative BS positions in complex indoor environments. Firstly, a sliding-window least squares (LS) algorithm is proposed to achieve the preliminary estimation of the BS positions and the receiver positions simultaneously while constraining the indoor propagation path errors. Following this, a clustering weighting optimization algorithm is proposed to mitigate multipath effects and converge the preliminary results to final BS locations. Experimental results show that under the 3D indoor positioning condition, the maximum error of the base station position estimation is less than 0.5m, fully meeting the decimeter-level indoor positioning requirements for Internet of Things (IoTs) applications.