Multipath propagation greatly affects the accuracy of time of arrival (ToA)-based indoor positioning when line-of-sight (LOS) signals are only used. In this paper, we present a novel real-time and low computation complexity multipath-assisted ToA positioning method, namely MSC-MAP. The delays of reflected signals are taken as additional spatial observations to compensate for an insufficient number of physical transmitters to locate a moving user equipment (UE). Virtual anchors are used to model the propagation path of reflected signals, whose locations are obtained via a multi-state constraint estimator, along with the trajectory of UE. In addition, we demonstrate the mismatch problem in data association and its impact on positioning performance. To achieve real-time processing, we propose two robust multipath-assisted positioning methods with mismatch alleviation by randomly selecting subset and constraint relaxation respectively, to meet various computational complexity requirements. Simulation results show that, for the MSC-MAP method, the mean square error of the position is generally less than 0.2 m in challenging indoor environments. Among mismatch alleviation algorithms, positioning error is reduced by 69% even when the percentage of mismatched measurement data is as high as 42%. The proposed algorithms can also efficiently handle signals with non-Gaussian impairments, a common characteristic in real-world data. Moreover, these algorithms can substantially improve positioning performance while adding minimal computation time in the presence of measurement mismatches, outperforming state-of-the-art methods utilizing different data association techniques.

The complex topography of urban canyons with many reflectors and scatterers makes it challenging to detect low-altitude, smaller, and slow-speed targets. In this paper, we present a novel multipath-assisted passive detection scheme based on the global navigation satellite system signals in urban canyons. We first propose an information-level target detection scheme, where a binary hypothesis test is conducted according to variation in the received signal given the presence or absence of targets in the environment. To take usage of multipath components (MPCs) in the proposed scheme, we introduce virtual anchors to model reflected signals’ propagation paths. We also introduce the reconfigurable intelligent surface to artificially improve the reflective environment and enhance the quality of received MPCs. The detection performance indicators are analyzed theoretically. Simulation results show that the proposed schemes respectively reach 90% and 94% detection probability at a signal-to-noise ratio of 5 dB. The RIS-based method outperforms the multipath-assisted method when the RIS error is less than 0.41 m.

Wi-Fi fingerprint-based positioning is widely used caused by its low cost and ease of implementation when Global Positioning System technology struggles to obtain signal and accuracy in indoor environments. A prerequisite for Wi-Fi fingerprint-based positioning is the availability of an accurate fingerprint database. However, in practice, access to a large proportion of the area is restricted, leading to a rapid degradation of the positioning accuracy of existing interpolation techniques in the absence of reference data in the vicinity of the area. In this paper, we propose an enhanced propagation model constrained fingerprint patching algorithm for incomplete RSS fingerprint databases, aiming to extend the coverage of fingerprints and reduce the overall workload of survey. The algorithm integrates a map-assisted wireless signal propagation models with incomplete spatially sampled fingerprint data to construct a propagation model for each Wi-Fi access point (AP). Recalculate the received signal strength at each reference point for each AP in the fingerprint database according to the specific model. The fingerprint information for the unrecorded regions is then refined by interpolation and the fingerprints initially collected are optimized. We experimentally validate the effectiveness of the proposed interpolation algorithm and compare it with the conventional Support Vector Regression (SVR), Gaussian Process Regression (GPR), and rational quadratic kernel (RQK) methods. The results show that the positioning performance of proposed method is improved by 18.1%, 23.1% and 39.7% in the blank area relative to RQK, GPR and SVR, respectively. In addition, our method outperforms other algorithms with stable performance in different scenarios, especially in scenarios containing obstacle walls, and the performance benefit appears more notable in the complicated indoor scenario with cramped rooms.

Multipath-assisted positioning is a promising way to realize robust and accurate indoor positioning, taking advantage of the environmental information carried by multipath signals. In this paper, we propose a novel multipath-assisted time-of-arrival (TOA) positioning method for complex indoor scenarios without a-priori knowledge of the floor plan. We use virtual anchors (VAs) to model the propagation path of reflected signals. The trajectory of the user equipment (UE) and the locations of VAs are iteratively estimated using two unscented Kalman filters (UKFs), considering the changeable visibility of VAs due to the birth and death of multipath components (MPCs). To use TOA measurements of MPCs as input for the update phase of the UKF, we present a data association method based on multipath peak flow tracking by baseband signal processing to establish the correspondence between measurements and VAs. We derive the analytical solution using the received power of MPCs for multipath tracking, which can realize low computational complexity data association. Simulation results show that, most of the MPCs can be correctly tracked using the peak flow method, even if some MPCs are densely distributed. For the proposed iterative UKF, the mean square error of the UE's position, with associated measurements obtained by the peak flow method as input, is generally less than 0.42 m in the complex indoor scenario.