Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches. The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

The purposes of this study were to characterise the golfer–ground interactions during the swing and to identify meaningful associations between the golfer–ground interaction force/moment parameters and the maximum clubhead speed in 63 highly skilled male golfers (handicap ≤ 3). Golfers performed shots in 3 club conditions (driver, 5-iron and pitching wedge) which were captured by an optical motion capture system and 2 force plates. In addition to the ground reaction forces (GRFs), 3 different golfer–ground interaction moments (GRF moments, pivoting moments and foot contact moments) were computed. The GRF moment about the forward/backward (F/B) axis and the pivoting moment about the vertical axis were identified as the primary moments. Significant (p < 0.05) correlations of peak force parameters (all components in the lead foot and F/B component in the trail foot) and peak moment parameters (lead-foot GRF moment and trail-foot pivoting moment) to clubhead speed were found. The lead-foot was responsible for generating the GRF moment, while the trail foot contributed to the pivoting moment more. The instant the lead arm becomes parallel to the ground was identified as the point of maximum angular effort, and the loading onto the lead-foot near this point was critical in generating both peak moments.

This letter presents a range-free localization algorithm with reliable anchor pair selection (RAPS) for anisotropic networks. To mitigate the effects of different network anisotropy, the proposed RAPS algorithm selects reliable anchor pairs by measuring the average hop progresses. The distances to the reliable anchors are estimated based on the geometric approximation of the node location. Compared to existing algorithms, the RAPS algorithm enables higher localization accuracy with fewer anchors while having less communication overhead in anisotropic networks. The superiority of the proposed algorithm is validated over the recently developed algorithms through extensive computer simulations.

This paper presents a multihop range-free localization algorithm that tolerates network anisotropy with a small number of anchors. A detoured path detection is proposed to detect if the shortest path between nodes is detoured from their direct path by measuring the deviation in the hop count between the direct and shortest paths. A novel distance estimation method is introduced to approximate the shortest path based on the path deviation and to estimate their distance by taking into account the extent of the detour of the approximate shortest path. Compared to other range-free algorithms, the proposed algorithm requires fewer anchors while achieving higher localization accuracy in anisotropic networks. We demonstrated its superiority over existing range-free localization algorithms through extensive computer simulations.

In this paper, we present a beam tracking algorithm based on the deep Q-network (DQN) for mobile millimeter-wave (mmWave) communications. The proposed algorithm determines the receive beam angle from the received signals without knowing the channel model and dynamics. It uses the received signals of the current and previous time slots to design the state and reward of the DQN. Our goal is to maximize the signal-to-noise ratio by the actions of the designed DQN. A significant computational complexity reduction is achieved since the receiver does not need to run complicated signal processing algorithms once the DQN is properly trained. Therefore a practical implementation of mmWave beam tracking with a very large number of antennas under harsh mobile environments becomes feasible. Through the extensive simulations, we verified the performance of the proposed algorithm and demonstrated robustness to the system uncertainty and low computational complexity in comparison with particle filter and the Q-learning.

Due to the received signal strength (RSS) variation, WiFi indoor positioning techniques using RSS have difficulties to provide good location estimates. To mitigate the effect of the RSS variation, this paper presents a Kalman filter-based positioning algorithm that is combined with pedestrian dead reckoning and RSS-based fingerprinting positioning. The RSS recovery and clustering methods are also introduced to enhance the accuracy of the fingerprinting positioning. Unlike other existing algorithms, the proposed algorithm estimates biases accumulated in RSS measurements based on the recursive least square estimation and removes them from the measurements. Reference points are effectively selected with clustering using the recovered RSS measurements. Hence, a more accurate location estimate can be obtained in the existence of the RSS variation. The proposed algorithm is implemented into an Android-based smartphone for test.