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.

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods.

In this paper, we propose a new method to estimate the pitch angle, azimuth angle and range of 3D near-field sources. The signal reception model of the 3D near-field sources is established, the range parameter is eliminated by constructing a fourth-order non-Hermite cumulant matrix. We use the Lagrange multiplier method to reduce the two-dimensional angle search to a one-dimensional angle search problem, and use the Multiple Signal Classification (MUSIC) algorithm to finely estimate the azimuth and elevation angles.The fourth-order cumulant matrix enriched with the range parameter is constructed and searched for the distance. Compared with other algorithms based on higher-order cumulants, this algorithm effectively reduces the dimension of the fourth-order cumulant matrix and reduces the computational complexity. Simulation results show the effectiveness of the method.

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.

To meet the demand for high-precision positioning in commercial industrial internet scenarios, 3GPP introduced the Positioning Reference Signal (PRS) in the 5G standard. However, the PRS signal occupies specific time and frequency resources for transmission in 5G systems, limiting the efficiency of communication signal transmission to some extent. In this regard, we propose a 5G NR Co-Band PRS model that allows for the superimposition of PRS signals on communication signals in a low-power manner, without requiring additional communication resources or causing too much interference to the communication signal. Since orthogonal frequency division multiplexing (OFDM) signals are sensitive to synchronization errors, we have developed a three-stage Co-Band PRS-based reception scheme. First, an innovative weighted window coarse synchronization method is proposed to enhance the performance of capturing communication signals at a low signal-to-noise ratio (SNR). Next, the interference cancellation technique is utilized to remove the communication signals, and the synchronization error is corrected through multipath delay estimation. Finally, to further improve the ranging accuracy, we propose an iterative delay-locked loop (DLL) algorithm that can achieve a tracking accuracy of one percent sampling. Simulation and real environment tests confirm that the proposed Co-Band PRS reception scheme can achieve a ranging accuracy of 0.16 m@90%.

In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection mechanism is proposed to detect IMU rationality faults and consistency faults in 5G observation information. The main contributions are reflected in the following two aspects: first, by innovatively introducing Pearson VII-type distribution for noise modeling, dynamically adjusting the tail thickness characteristics of the probability density function through its shape parameter, and effectively capturing the distribution law of extreme values in the observation data. Afterward, this article combined the variational Bayesian strong tracking filtering algorithm to construct a robust state estimation framework, significantly improving the localization accuracy and continuity in non-Gaussian noise environments. Second, a hierarchical progressive fault detection mechanism is designed. A wavelet fault detection method based on a hierarchical voting mechanism is adopted for IMU data to extract the abrupt features of the observed data and quickly identify faults. In addition, a dual-channel consistency detection model with dynamic fault-tolerant management was constructed. Sudden and gradual faults were quickly detected through a dual-channel pre-check, and then, the fault source was identified through AIME. Based on the fault source detection results, corresponding compensation mechanisms were adopted to achieve robust continuous localization.