Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.

Integrated sensing and communication (ISAC) is emerging as a pivotal development direction for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless networks, especially with the challenge of achieving the communication and sensing capabilities necessary for the future Internet of Things (IoT) paradigm under resource constraints. In this article, we propose a joint explicit communication and implicit sensing (JECIS) system designed to generate sensing and communication signals across two distinct domains, thereby enabling the multiplexing of the same time and frequency resources. The proposed system generates a block-shaped sensing reference signal in the delay-Doppler domain to facilitate the estimation of relative range and velocity between the transmitter and the receiver, while communication signals are produced in the time-frequency domain. Furthermore, we introduce a two-step algorithm for range and velocity estimation that achieves high precision while ensuring exceptional noise resilience and low complexity. The simulation results indicate that the proposed method effectively achieves high sensing performance while the sensing signal operates at a significantly lower power level in comparison to communication signals, rendering the impact on the bit error rate (BER) of communications negligible.

In complex environments, the application of traditional Kalman Filtering in GNSS/INS integrated navigation systems often encounters challenges such as filter divergence and accuracy degradation. This paper introduces the technique of Simplified Spherical Unscented Kalman Filtering (SSUKF) and, based on this, proposes an Adaptive Simplified Spherical Unscented Kalman Filtering (ASSUKF) integrated navigation method. This approach, built upon SSUKF, incorporates an adaptive filter that effectively utilizes residuals and innovation sequences to mitigate the divergence phenomenon during the filtering process. Furthermore, the system is capable of online estimation and dynamic adjustment of the statistical characteristics of measurement noise, leading to more accurate state estimation and significantly enhancing the adaptive capability of SSUKF. ASSUKF improves position accuracy in the latitude direction by 18.10% and in the longitude direction by 20.6%. For attitude error, ASSUKF performs exceptionally well. Specifically, the pitch angle error improves by 27.6% compared to UKF and by 27.1% compared to SSUKF. The roll angle error improves by 29.9% compared to UKF and by 20.1% compared to SSUKF. The heading angle error improves by 24.3% compared to SSUKF, validating the method’s substantial advantages in improving system accuracy and robustness, demonstrating its effectiveness and potential in complex environments.

Foot-mounted inertial navigation is an important option for pedestrian positioning when external localization infrastructure is limited, unavailable, or unreliable. Its performance, however, depends heavily on zero-velocity updates (ZUPTs), which can become overconfident during heel-strike, toe-off, and other non-ideal contact phases. This paper formulates ZUPT as probabilistic pseudo-observation modeling and introduces posterior-contact soft-ZUPT, in which update strength is controlled by a detector-derived contact prior and an innovation-conditioned posterior within a single error-state Kalman filter. Experiments on the 56-trial foot-mounted VICON benchmark under a two-fold development/evaluation protocol compare the method against hard-ZUPT, robust soft-ZUPT, contact soft-ZUPT, and a foot-instability-based adaptive covariance (FIBA)-like baseline. Posterior-contact soft-ZUPT improves difficult-trial robustness and upper-tail error suppression while remaining competitive in overall accuracy, achieving the best selected p95 and tail-risk values among the compared methods. These results indicate that probabilistic reliability modeling can soften ZUPT effectively while preserving a lightweight single-filter implementation.

Positioning and navigation systems have become indispensable infrastructure for modern society. However, standalone wireless positioning and inertial navigation systems each exhibit inherent limitations. Therefore, there is a strong need for integrated positioning techniques that can exploit the complementary strengths of multiple sensing modalities. Loosely coupled fusion approaches do not fully utilise raw observation information, whereas tightly coupled Kalman‐filter‐based methods remain limited in strongly nonlinear scenarios. To address these issues, this paper proposes a particle‐filter‐based fusion positioning method. In addition, an effective neighbourhood‐based particle set optimisation strategy is developed to mitigate particle degeneracy. Experimental results show that the proposed method significantly improves positioning accuracy over standalone positioning methods and tightly coupled Kalman‐filter‐based fusion methods. Compared with the cubature Kalman filter‐based fusion method, which provides the best performance among the conventional baselines, the proposed method improves positioning accuracy by 33.5%.