A feature scaling based k-nearest neighbor algorithm for indoor positioning system

et al. 2016

Sensors

The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

Piao

et al. 2016

Sensors

“…One popular way to position the Erob in indoor environment is that to use reference fingerprints based on signals [14]. In general, there are two stages for a fingerprint-based positioning algorithm: (1) offline stage and (2) online stage.…”

Section: Positioning Principle By Using Wifi and Rfid Fingerprintmentioning

confidence: 99%

Positioning and guiding educational robots by using fingerprints of WiFi and RFID array

Luo

et al. 2018

J Wireless Com Network

Positioning educational robots in the indoor ambiance is an important and basic function for robots to provide intelligent educational services for users, and it is still an open challenge problem. This paper proposes to position indoor robots by using fingerprints of wireless fidelity (WiFi) and radio-frequency identification (RFID) array in the complementary way. The fingerprint of WiFi is first used to position educational robots (Erob) in a large area and to guide robots to a place close to the target location. Then, the fingerprint of RFID array is used to guide Erob to the target location with a small discrepancy. It proved that the designed layouts of WiFi devices and RFID array can have fingerprint matching to estimate the position of Erob. The proposed positioning method can guide Erob to the target location fast and accurately so that robots can provide multiple services based on it.

“…Presently, the KNN algorithm and the weighted -nearest neighbour (WKNN) algorithm [13] are the most commonly used matching algorithms. In [28], Li et al proposed a KNN algorithm based on feature scaling. Although their algorithm does consider the fact that the distance between RSSI vectors in the fingerprint database and the actual distance are not perfectly matched, their algorithm does not completely overcome the disadvantage that the RSSI can easily attenuate.…”

Section: Related Workmentioning

confidence: 99%

Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines

Song

Mathematical Problems in Engineering

Long

et al. 2017

Accurate target localization technology plays a very important role in ensuring mine safety production and higher production efficiency. The localization accuracy of a mine localization system is influenced by many factors. The most significant factor is the non-line of sight (NLOS) propagation error of the localization signal between the access point (AP) and the target node (Tag). In order to improve positioning accuracy, the NLOS error must be suppressed by an optimization algorithm. However, the traditional optimization algorithms are complex and exhibit poor optimization performance. To solve this problem, this paper proposes a new method for mine time of arrival (TOA) localization based on the idea of comprehensive optimization. The proposed method utilizes particle filtering to reduce the TOA data error, and the positioning results are further optimized with fingerprinting based on the Manhattan distance. This proposed method combines the advantages of particle filtering and fingerprinting localization. It reduces algorithm complexity and has better error suppression performance. The experimental results demonstrate that, as compared to the symmetric double-sided two-way ranging (SDS-TWR) method or received signal strength indication (RSSI) based fingerprinting method, the proposed method has a significantly improved localization performance, and the environment adaptability is enhanced.