LOS compensation and trusted NLOS recognition assisted WiFi RTT indoor positioning algorithm

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications.

Section: Techniques For Drone Positioning and Attitudementioning

confidence: 99%

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Kucukayan,

Karacan

2024

“…As an example, in Figure 4, Room 515 is considered only in partial matching where f 515 = [null, 13,15,1,16,2]. Room 521, on the other hand, is also a candidate in full matching, where f 521 = [8,20,21,10,22,11]. For locations with incomplete beacons resulting in null values representing no signal detected, we replace null with zeroes for the model to better differentiate from distant beacons with higher negative values and to calculate the signal pattern feature.…”

Section: Matchingmentioning

confidence: 99%

“…Radiofrequency-based technologies [ 11 ], specifically Wi-Fi [ 12 , 13 , 14 , 15 , 16 ], Radio Frequency Identification (RFID) [ 17 , 18 ], and Bluetooth Low Energy (BLE) [ 19 , 20 , 21 ], are typically preferred considering flexibility to setup and integration with IoT. Recently, studies on Wi-FI have been optimizing line of sight (LOS) and non-line of sight (NLOS) [ 22 , 23 ] approaches, including combination with vision [ 24 ] for localization. Furthermore, sensors such as IMU [ 25 ] and Geomagnetism [ 26 ] are integrated in movement and positioning.…”

Section: Introductionmentioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

“…However, within indoor environments, satellite positioning signals are often blocked, making it challenging to obtain positioning information through satellites. Consequently, indoor positioning technologies [ 2 ] such as Wi-Fi [ 3 , 4 ], Bluetooth [ 5 ], UWB [ 6 ], and 5G positioning [ 7 ] have emerged. Nevertheless, the field of indoor positioning still faces several unresolved issues.…”

Section: Introductionmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.