The WiFi fine time measurement (FTM)-based ranging positioning often encounters challenges instability and low accuracy in real-life scenarios due to inaccurate round-trip time (RTT)-based ranging. To address these issues, we construct a WiFi RTT/RSS/PDR/Map fusion system with an improved mapaided particle filter (PF). Our approach focuses on FTM-based localization from a fingerprinting perspective rather than WiFi ranging positioning. The WiFi FTM fingerprinting utilizes an artificial database, reducing the need for offline labor-intensive measurement work. The semi-parametric error model is employed to compensate for the WiFi RTT and RSS data and improve fingerprinting accuracy. A collaborative optimization strategy incorporating map and fingerprinting information is designed to enhance the PF’s performance in terms of particle impoverishment, propagation, and weight update. Extensive experiments demonstrate that the integrated WiFi RTT/RSS/PDR/Map system is error-tolerant under various additional error variance conditions (e.g., heading δ _Ψ ² ≤4/ deg ² , or step length δ _L ² ≤0.5/ m ² , or RTT ranging δ _d ² ≤2/ m ² and RSS δ _R ² ≤2/ dBm ² ), with an accuracy fluctuation within 0.1 m. The system obtains meter-level accuracy within 0.19 s when utilizing 1000 particles for every localization request. Compared to the state-of-the-art fusion approaches, the WiFi RTT/RSS/PDR/Map fusion system can deliver better accuracy and stability.

A perfluoroglutaric acid stapled ligand for synthesis of highly emissive CsPbBr₃ perovskite nanocrystals in polar organic solvent.

Robust and accurate step counting plays an essential role in some fields, such as indoor positing, behavior recognition, and health management. Currently, there are numerous applications or methods in step counting. However, most solutions did not emphasize the elimination of false steps in the non-walking state, which still encounters the over-counting or under-counting problem. In this study, a robust and accurate step counting solution based on movement mode recognition is proposed. First, according to the characteristics of pedestrians walking, the SVM and FSM-DT classifiers were constructed to recognize the user’s motion state and phone usage mode, respectively. The purpose of classification is to solve the step-counting error of non-walking state and initialize a suitable parameter for different cases. Then, we utilized a crest-valley detection algorithm with multi-feature constrained to detect the step, and the initial threshold values for each mode can be adjusted adaptively during walking. The results indicate that the accuracy of the proposed method can reach 99.11% and 97.43% in normal and free walking. Compared with standard peak detection and an excellent method, the proposed method can improve by 44.14% and 18.71% for false walking, respectively, which is a significant improvement in the robustness and accuracy. Furthermore, we also achieve an average accuracy higher than 98% in some typically carrying modes and ways, and higher accuracy compared with four popular step-counting mobile applications in different walking states.

Currently, various indoor positioning technologies are widely studied, and visible light positioning (VLP) is a promising technology due to its high accuracy, low cost, and high output rate. However, the most common method based on the received signal strength (RSS) requires calibrating the model in advance, which has a weak generalization ability. This article focuses on the VLP method based on the time difference of arrival (TDOA), which does not require heavy preparatory work. Firstly, we analyze the influence of different errors on TDOA-based VLP, such as the time synchronization error, receiver noise, etc. Secondly, a convolution neural network (CNN) based network is designed for phase difference estimation, which significantly improves the accuracy of phase difference estimation compared to the traditional in-phase&quadrature signal-based method. Lastly, a particle filter based on the motion state is proposed to improve positioning accuracy and robustness. Simulated experiments evaluate the proposed methods, and the final results show a significant improvement in accuracy when compared with traditional methods. The improvements in ranging and localization accuracy can both reach over 50%.

In this paper, we have constructed a high-precision indoor positioning system by leveraging WiFi round-trip time (RTT), magnetic field and pedestrian dead reckoning (PDR). For the WiFi RTT module, a collaborative WiFi RTT positioning approach is proposed by combining RTT ranging and fingerprinting through a Cramer-Rao lower bound (CRLB)-based outlier detection method. Based on this, an enhanced version of the geomagnetic-aided WiFi RTT localization method is implemented. For better smartphone-based deployment, we improve PDR in terms of step detection, step length estimation and heading optimization. Moreover, an optimization strategy incorporating map-aided resampling and particle swarm reconstruction is designed to enhance the performance of particle filter. Finally, the WiFi RTT, magnetic positioning and PDR are integrated based on the improved particle filter (IPF). Our system employs two mechanisms to achieve fusion localization, which means that the system executes WiFi/magnetic/PDR fusion when the WiFi module is available and switches to magnetic/PDR fusion when the WiFi signal is absent. Extensive experiments conducted in a corridor environment demonstrate that the IPF-based WiFi RTT/Magnetic/PDR model achieves localization accuracy of 0.64 m and 0.91 m when the WiFi module is available and unavailable, respectively, showing meter-level continuous positioning capability even in the presence of additional WiFi ranging errors and in areas without WiFi signal coverage.

A new single‐epoch single‐frequency algorithm based on partial least‐squares ambiguity decorrelation adjustment (Par Lambda) that employs baseline constraints is developed for BeiDou Navigation Satellite System (BDS) called Improved Par Lambda, based on the characteristics of building deformation monitoring. The floating solution of double‐difference ambiguities is obtained by means of double‐difference observations and least‐squares method. The double‐difference observation equations with baseline length constraint are composed of the equations of BDS single‐frequency pseudo‐range observations and carrier phase observations and the baseline length constraint equation. The least‐squares principle is used to compute floating solution of double‐difference ambiguities and the corresponding cofactor matrix. Based on the precision of double‐difference ambiguities, the ambiguities will be divided into different levels (2, 1, 1,…, 1) and then be fixed based on the Lambda method progressively. The 1, 5 and 10 s short baseline data of BDS single‐epoch single‐frequency based on B 1 band carrier phase were computed through this method. The results showed that, for BDS single‐epoch single‐frequency, the success rate and the search efficiency of fixing ambiguities of Improved Par Lambda were all better than the traditional methods and the success rate was more than 98%.