Hotspot Ranking Based Indoor Mapping and Mobility Analysis Using Crowdsourced Wi-Fi Signal

197

119

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

197

119

“…Li et al [21] designed a framework for two-dimensional laser scan matching for the detection of point and line feature correspondences. Zhou et al [22] developed a motion sensor-free approach and mobility analysis based on the sporadically collected crowd sourced Wi-Fi Received Signal Strength (RSS) data. The method focused on low time and labor cost in any unknown indoor environment.…”

Section: Introductionmentioning

confidence: 99%

“…The above studies mainly focused on a mapping system [12,14,18,19,21,22,23,24,25], sensor fusion [13,15,26], or solving a SLAM problem [16,17,20]. Furthermore, mobile robots were mostly considered in the above indoor mapping studies.…”

Section: Introductionmentioning

confidence: 99%

Indoor Mapping Guidance Algorithm of Rotary-Wing UAV Including Dead-End Situations

Park

Yoo

2019

A mapping guidance algorithm of a quadrotor for unknown indoor environments is proposed. A sensor with limited sensing range is assumed to be mounted on the quadrotor to obtain object data points. With obtained data, the quadrotor computes velocity vector and yaw commands to move around the object while maintaining a safe distance. The magnitude of the velocity vector is also controlled to prevent a collision. The distance transform method is applied to establish dead-end situation logic as well as exploration completion logic. When a dead-end situation occurs, the guidance algorithm of the quadrotor is switched to a particular maneuver. The proposed maneuver enables the quadrotor not only to escape from the dead-end situation, but also to find undiscovered area to continue mapping. Various numerical simulations are performed to verify the performance of the proposed mapping guidance algorithm.

“…Although not collected at specified locations, crowdsourced RSS samples still need to be annotated with some location information for fingerprint database construction. To this end, a common approach is to extract RSS samples from pedestrian movement trajectory [ 10 , 11 , 12 ]. As long as a trajectory can be correctly matched to one physical route, each step position can be obtained from the floor plan to annotate the corresponding step RSS sample.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Lin

Wang

Yang

et al. 2018

Self Cite

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.