Abstract
The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.
Highlights
With the increasing improvement of the Internet of Things (IoT), location-based services and localization-based computing have attracted much attention because of their widespread applications [1]
This paper reviews the comprehensive description of radio wave signals for indoor positioning, based on common technologies and effective positioning methods
The behaviour of non-radio wave signals was mentioned in the introduction section
Summary
With the increasing improvement of the Internet of Things (IoT), location-based services and localization-based computing have attracted much attention because of their widespread applications [1]. Distance estimation is usually mentioned as ranging, and is based on different traditional parameters such as the received signal strength (RSS), angle of arrival (AOA), TOA, and TDOA of beacon signal changes between the target node and the beacon nodes. The RSSI-based algorithm only requires the received signal strength and does not require an auxiliary hardware apparatus and time synchronization, able to achieve higher accuracy than other methods. The achievement the TDOA is subject tocomparing synchronization between nodes and atarget position the variances inthe theanchor arrival timesin [62] This method performs by determining the change in the time a couple of anchor arrival timesof[62]. The radio signals reaching the receiving antenna by different paths cause multipath transmission The frequency diversity method was introduced for the accurate position estimation using weighted averages of evaluations with uncorrelated accurate position estimation using weighted averages of networks evaluations errors acquired in various [66].with uncorrelated errors acquired in various networks [66]
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