Reduced-complexity fingerprinting in WLAN-based indoor positioning

Zhang

et al. 2019

Sensors

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

Section: Review Of Related Literaturementioning

confidence: 99%

A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring

Zhang

et al. 2019

Sensors

“…Currently, a WLAN indoor location method has been widely studied and applied due to simpler device, lower cost and higher precision [18][19][20]. Propagation model [19,21] and scene analysis [20,22] are the two main methods in WLAN indoor positioning. The propagation model method can obtain the locations through a signal propagation model and trilateral positioning principle, which is based on the distances between positioning point and access points (APs).…”

Section: Introductionmentioning

confidence: 99%

Adaptive indoor positioning method based on direction discrimination and device conversion

Zhu

et al. 2020

IET wirel. sens. syst.

Received signal strength (RSS) greatly differs due to the different occlusion directions and receiving device heterogeneity. It greatly affects the positioning accuracy. In this study, an adaptive indoor positioning method based on the direction discrimination and device conversion is proposed to solve these problems. This method is mainly composed of three parts: direction discrimination, device conversion and positioning models. First, the direction discrimination model can reduce the impact of a user's body occlusion. Best access points can be selected by principal component analysis to adapt to different directions and areas. Secondly, a device conversion model is used to reduce high offline work due to device heterogeneity. RSS of other devices can be converted to the value of one fixed device by least squares piecewise polynomial algorithm, without increasing the offline data collection workload. Finally, the results can be obtained by the positioning model. The problems of high dimensionality and non-linearity can be solved by the least squares support vector regression algorithm. Experimental results show that the proposed method can solve the problems of occlusion direction and device heterogeneity. The engineering applicability of positioning system can also be greatly improved.

“…At present, the main research directions for fingerprint positioning are reducing the computational complexity and improving the positioning accuracy [4,5]. For the WiFi positioning, the positioning terminal is generally a battery-powered device with limited computing power [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance

Wang

et al. 2019

Sensors

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.