This paper proposes an algorithm for estimating the location of wireless access points (APs) in indoor environments to realize smartphone positioning based on Wi-Fi without pre-constructing a database. The proposed method is designed to overcome the main problem of existing positioning methods requiring the advance construction of a database with coordinates or precise AP location measurements. The proposed algorithm constructs a local coordinate system with the first four APs that are activated in turn, and estimates the AP installation location using Wi-Fi round-trip time (RTT) lateration and the ranging results between the APs. The effectiveness of the proposed algorithm is confirmed by conducting experiments in a real indoor environment consisting of two rooms of different sizes to evaluate the positioning performance of the algorithm. The experimental results showed the proposed algorithm using Wi-Fi RTT lateration delivers high smartphone positioning performance without a pre-constructed database or precise AP location measurements.

This paper indicates a design methodology of Wi-Fi round-trip time (RTT) ranging for lateration through the performance evaluation experiments. The Wi-Fi RTT-based lateration needs to operate plural access points (APs) at the same time. However, the relationship between the number of APs in operation and ranging performance has not been clarified in the conventional researches. Then, we evaluate the ranging performance of Wi-Fi RTT for lateration focusing on the number of APs and channel-usage conditions. As the results, we confirm that the ranging result acquisition rates decreases caused by increasing the number of APs simultaneously operated and/or increasing the channel-usage rates. In addition, based on positioning performance comparison between the Wi-Fi RTT-based lateration and the Wi-Fi fingerprint method, we clarify the points of notice that positioning by Wi-Fi RTT-based lateration differs from the conventional radio-intensity-based positioning. Consequently, we show a design methodology of Wi-Fi RTT ranging for lateration as the following three points: the important indicators for evaluation, the severeness of the channel selection, and the number of APs for using. The design methodology will help to realize the high-quality location-based services.

This manuscript discusses a new indoor positioning method and proposes a multi-distance function trilateration over k-NN fingerprinting method using radio signals. Generally, the strength of radio signals, referred to received signal strength indicator or RSSI, decreases as they travel in space. Our method employs a list of fingerprints comprised of RSSIs to absorb interference between radio signals, which happens around the transmitters and it also employs multiple distance functions for conversion from distance between fingerprints to the physical distance in order to absorb the interference that happens around the receiver then it performs trilateration between the top three closest fingerprints to locate the receiver's current position. An experiment in positioning performance is conducted in our laboratory and the result shows that our method is viable for a position-level indoor positioning method and it could improve positioning performance by 12.7% of positioning error to 0.406 in meter in comparison with traditional methods.

3GPP (3rd Generation Partnership Project) has started to discuss D2D (Device-to-Device)-aided OTDOA (Observed Time Difference Of Arrival) as one of the mobile positioning enhancement techniques for LTE (Long Term Evolution) systems. It is a kind of multi-node based OTDOA which directly receives D2D signals from adjacent multiple UEs (User Equipment) to measure RSTD (Reference Signal's Time Difference). D2D signals provide valuable advantages in terms of OTDOA positioning because it can guarantee more reference nodes and high SNR (Signal-to-Noise Ratio) of PRS (Positioning Reference Signal). Two typical methods for multi-node based OTDOA can be applied to D2D-aided OTDOA. Multiple OTDOA positioning is one of the multi-node based methods that averages multiple results from OTDOA; however, it cannot always guarantee high accuracy due to the non-uniform geometry of UEs. OTDOA positioning based on TSE (Taylor Series Expansion) algorithm may be one of the solutions; however, it has the initial value problem and high computational complexity due to its iterative procedure. Therefore, in this paper, we propose a novel D2D-aided OTDOA positioning method which utilizes UEs not as reference node of OTDOA but as assisting node for RSTD error reduction. The proposed method can reduce RSTD error of eNB based hyperbola by using multiple hyperbola bands. The hyperbola band indicates the possible range in which a hyperbola can occur due to RSTD error. Then, by using principal axes of hyperbolas, we estimate a modified hyperbola from the overlap area of hyperbola bands, which has less RSTD error. We verify that the proposed method can effectively reduce RSTD error and improve positioning performance with lower computational complexity.

These days, cheap and intelligent sensors, networked through wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and controlling homes, cities and the environment. Networked sensors also offer a broad range of applications. Localization capability is essential in most wireless sensor networks applications; for instance in environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are meaningless without accurate knowledge of where they are obtained. Localization techniques are used to determine location information by estimating the location of each sensor node. Distance measurement errors are commonly known to affect the accuracy of the estimated location; resulting in errors that may be due to inherent or environmental factors. Trilateration [1] is a well-known method for localizing nodes by using the distances to three anchor nodes; yet it performs poorly when they are many distance measurement errors. Therefore, we propose the LRD (Localization with Ratio-Distance) algorithm, which performs strongly even in the presence of many measurement errors associated with the estimated distance to anchor nodes. Simulations using the OPNET Modeler show that LRD is more accurate than trilateration.

In wireless sensor networks (WSNs), localization using the received signal strength (RSS) method is famous for easy adaptation and low cost where measuring the distance between sensor nodes. However, in real localization systems, the RSS is strongly affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As a result, the proposed scheme shows that it can provide high precision.