Search Results (263)

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the Received Signal Strength Indicator (RSSI) signals from WiFi Anchor Points (APs).Indoor movement is detected through a successive estimation of a target’s multiple positions. Using the K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) algorithms, these RSSI measurements are trained for estimating the position of an indoor target. Additionally, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) has been integrated into the PSO method for removing RSSI-estimated position outliers of the mobile device to further improve indoor position detection and monitoring accuracy. We also employed Time Reversal Resonating Strength (TRRS) as a correlation technique as the third method of localization. Our extensive and rigorous experimentation covers the influence of various weather conditions in indoor detection. Our proposed localization methods have maximum accuracies of 92%, 80%, and 75% for TRRS, KNN, and PSO + DBSCAN, respectively. Each method also has an approximate one-meter deviation, which is a short distance from our targets. Full article

Smartphone GNSS technology has advanced significantly, but its performance varies considerably among Android devices due to differences in hardware and software. This study compares the GNSS capabilities of the Google Pixel 5 and Pixel 9 Pro XL (Google LLC, Mountain View, CA, USA) using five-hour static measurements under three environmental conditions: open area, canopy, and indoor. Complete raw GNSS data and the tools used for positioning are freely available. The analysis focuses on signal quality and positioning accuracy, derived using raw GNSS measurements. Results show that the Pixel 9 Pro XL provides better signal completeness, a higher carrier-to-noise density (C/N0), and improved L5 frequency reception. However, this enhanced signal quality does not always translate to superior positioning accuracy. In single-point positioning (SPP), the Pixel 5 outperformed the Pixel 9 Pro XL in open conditions when considering mean positional errors, while the Pixel 9 Pro XL performed better under canopy conditions. The precise point positioning results are modest compared to the current state of the art, only achieving accuracies of a few meters. The static method achieved sub-decimeter accuracy for both devices in optimal conditions, with Pixel 9 Pro XL demonstrating a higher fix rate. Findings highlight ongoing challenges in smartphone GNSS, particularly related to the limited quality of signals received by smartphone GNSS receivers. While newer devices show improved signal reception, precise positioning remains limited. Future research should explore software enhancements and the use of various external correction sources to optimize GNSS accuracy for mobile users. Generally, a shift from research to user-ready applications is needed. Full article

We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article

Real-time estimation of fish growth offers multiple benefits in indoor aquaculture, including reduced labor, lower operational costs, improved feeding efficiency, and optimized harvesting schedules. This study presents a low-cost, vision-based method for estimating the body length and weight of olive flounder (Paralichthys olivaceus) in tank environments. A 5 × 5 cm reference grid is placed on the tank bottom, and images are captured using two fixed-position RGB smartphone cameras. Pixel measurements from the images are converted into millimeters using a calibrated pixel-to-length relationship. The system calculates fish length by detecting contour extremities and applying Lagrange interpolation. Based on the estimated length, body weight is derived using a power regression model. Accuracy was validated using both manual length measurements and Bland–Altman analysis, which indicated a mean bias of −0.007 cm and 95% limits of agreement from −0.475 to +0.462 cm, confirming consistent agreement between methods. The mean absolute error (MAE) and mean squared error (MSE) were 0.11 cm and 0.025 cm², respectively. While optimized for benthic species such as olive flounder, this system is not suitable for free-swimming species. Overall, it provides a practical and scalable approach for non-invasive monitoring of fish growth in commercial indoor aquaculture. Full article

A core problem relating to indoor positioning is a lack of prior knowledge of the environment. To date, most WiFi–RTT research assumes knowledge of the access points in an indoor environment. This paper provides a solution to this problem by using a simultaneous localisation and mapping (SLAM) algorithm, using WiFi–RTT and pedestrian dead reckoning, which uses the inertial sensors in a smartphone. A WiFi–RTT SLAM algorithm has only been researched in one instance at the time of writing; this paper aims to expand the exploration of this problem, particularly in relation to the use of outlier detection and motion models. For the trials, which were 35 steps long, the final mobile device horizontal positioning error was 1.01 m and 1.7 m for the forward and reverse trials, respectively. The results of this paper show that unmapped indoor positioning using WiFi–RTT is feasible for metre-level indoor positioning, given correct access point calibration. Full article

Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field. Full article

Customer context analysis (CCA) in brick-and-mortar shopping malls can support decision makers’ marketing decisions by providing them with information about customer interest and purchases from merchants. It makes offline CCA an important topic in marketing. In order to analyze customer context, it is necessary to analyze customer behavior, as well as to obtain the customer’s location, and we propose an analysis system for customer context based on these two aspects. For customer behavior, we use a modeling approach based on the time-frequency domain, while separately identifying movement-related behaviors (MB) and semantic-related behaviors (SB), where MB are used to assist in localization and the positioning result are used to assist semantic-related behavior recognition, further realizing CCA generation. For customer locations, we use a deep-learning-based pedestrian dead reckoning (DPDR) method combined with a node map to achieve store-level pedestrian autonomous positioning, where the DPDR is assisted by simple behaviors. Full article

High-precision indoor positioning is essential for various applications, such as the Internet of Things, robotics, and smart manufacturing, requiring accuracy better than 1 m. Conventional indoor positioning methods, like Wi-Fi or Bluetooth fingerprinting, typically provide low accuracy within a range of several meters, while techniques such as laser or visual odometry often require fusion with absolute positioning methods. Ultra-wideband (UWB) and Wi-Fi Round-Trip Time (RTT) are emerging radio positioning technologies supported by industry leaders like Apple and Google, respectively, both capable of achieving high-precision indoor positioning. This paper offers a comprehensive survey of UWB and Wi-Fi positioning, beginning with an overview of UWB and Wi-Fi RTT ranging, followed by an explanation of the fundamental principles of UWB and Wi-Fi RTT-based geometric positioning. Additionally, it compares the strengths and limitations of UWB and Wi-Fi RTT technologies and reviews advanced studies that address practical challenges in UWB and Wi-Fi RTT positioning, such as accuracy, reliability, continuity, and base station coordinate calibration issues. These challenges are primarily addressed through a multi-sensor fusion approach that integrates relative and absolute positioning. Finally, this paper highlights future directions for the development of UWB- and Wi-Fi RTT-based indoor positioning technologies. Full article

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation. Full article

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. Full article

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm. Full article

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods. Full article

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system. Full article