A Survey of Recent Indoor Localization Scenarios and Methodologies

Abstract

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

Unmanned aerial vehicles and machine learning for detecting objects in real time

“…As part of the testing requirements, the YOLO model is first tested to check its latency from different distances. The drone has been changed with different angles and views to get as many readings of latency as possible [25]. The results show that when the distance between the base station and drone increased, the latency slightly increases as well, as presented in Table 2.…”

Sensor fusion-based localization methods for mobile robots

“…Vision sensors are used in both indoor and outdoor environments, and they include cameras and LiDAR sensors [5][6]8]. The GPS is a widely used technology for determining absolute position in outdoor environment, but it does not provide reliable measurements in indoor environment [9]. Signal strength measurements are widely used for absolute position estimation in indoor environments [9].…”

“…The GPS is a widely used technology for determining absolute position in outdoor environment, but it does not provide reliable measurements in indoor environment [9]. Signal strength measurements are widely used for absolute position estimation in indoor environments [9]. These methods utilize the received signal strength indicator (RSSI), which can be read from the wireless transceiver modules and can be used to estimate distances.…”

Multiple Fingerprinting Localization by an Artificial Neural Network

“…There are different methods for indoor localization based on RF signals. In the Time of Arrival (TOA), Time Difference of Arrival (TDOA), and Angle of Arrival (AoA) methods [ 1 ], a target is estimated by calculating the time differences, distances, and angles, respectively, between a target and transmitters using the received signals from deployed beacons. On the other hand, the WiFi fingerprinting approach estimates a target location based on pattern recognition through machine learning methods.…”