Indoor Localization Algorithm based on Fingerprint Using a Single Fifth Generation Wi-Fi Access Point

Abstract

Over recent years, wireless indoor positioning systems (WIPS) have attracted considerable research interest. However, high-performance WIPS proposed in the literature requires that the building have at least three access points (APs). This paper proposes an WIPS using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses beam fingerprints and classification models based on KNN (K-nearest neighbor) and Bayes rule. The beam fingerprint is composed of RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment for each beam codebook in the off-line phase. In the online phase, RSS samples of the best beams are collected by user equipment (UE) during the beamtracking process, which are then classified based on beam fingerprints into predefined coordinates. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.

Improving the performance of a radio-frequency localization system in adverse outdoor applications

“…The approach described in [26] uses multipath characteristics of the scenario to build a database of NLOS rays, applying a clustering procedure to match the real-world measurement with the simulated one to locate the emitter. In [27], the authors presented different localization fingerprints using algorithms received signal strength (RSS) and the K-nearest neighbour (KNN). There are different types of "Fingerprints, " as discussed in [28] and [29], where a performance improvement was observed when the position estimation was evaluated using the channel state information (CSI) for long term evolution (LTE).…”

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

“…In general, radio locating is usually classified into ranging and nonranging; the former needs to measure the actual Euclidean distance or angle, such as received signal strength indication (RSSI) [17,18], time of arrival (TOA) [19,20], time difference of arrival (TDOA) [21][22][23][24], and their fusion algorithms [25,26], while the latter is based on topology [27], connectivity [28], multihop [29], and fingerprint information [30] of the network itself. Where the accessorial vehicle positioning based on RSSI needs to know the spatial attenuation characteristics of signals, which is difficult to be accurately obtained due to the increasing complexity of wireless channels, the TDOA and TOA algorithms are both very sensitive to the measurement of time, which makes them difficult to achieve high precision; hence, direction of arrival (DOA) estimation has become a good choice.…”

Localização em ambientes internos baseada em regressão kNN utilizando beamforming

“…Assim, muitos autores [11], [13], [15] têm procurado utilizar a técnica de beamforming nos sistemas de localização, visando reduzir as interferências nos sinais transmitidos e o número de APs necessários.…”

“…Os autores de [15] propõem um sistema de localização interna usando um único AP Wi-Fi de quinta geração (5G), ou padrão IEEE 802.11ad. O método utiliza as impressões digitais das potências de cada feixe formado pelo transmissor.…”

“…Assim, neste trabalho, busca-se aprimorar os conceitos propostos em [15] utilizando métodos estatísticos para reduzir a quantidade de características (feixes para o beamforming) aplicadas como preditores para localização. Este artigo está organizado nas seguintes seções: Na Seção II apresenta-se a fundamentação teórica.…”