Indoor localization based on Wi-Fi received signal strength indication (RSSI) has the advantage of low cost and easy implementation compared with a range of other localization approaches. However, Wi-Fi RSSI suffers from multipath interference in indoor dynamic environments, resulting in significant errors in RSSI observations. To handle this issue, a number of different methods have been proposed in the literature, including the mean method, Kalman filter algorithm, and the particle filter algorithm. It is observed that these existing methods may not perform sufficiently well in ever-changing dynamic indoor environments. This paper presents an algorithm to improve RSSI observations by using the average of a number of selected maximum RSSI observations. Smoothness index is employed to evaluate the quality of RSSI so as to select an appropriate number of RSSI observations. Experiments were conducted in four rooms and a corridor within an office building and the results demonstrate that the proposed method considerably outperforms the existing algorithms in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.

The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points' coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.

This paper first proposes a new clustering algorithm for selection of reference points (RPs) based on virtual positions of APs for indoor localization in area without linear constraints, which can not only cluster automatically, but also guarantee the consistency of methods between the off-line phase clustering and the online phase positioning. A new weighted algorithm based on physical distance is then presented for position determination. With angle velocity measurement provided such as by gyroscope, the weighted algorithm is particularly suited for scenarios where the mobile moves along a trajectory. The number of clusters in traditional RP clustering algorithms needs to be predefined, which means an unsuitable number of clusters would lead to poor estimation accuracy. Traditional WKNN algorithm weights the reference points’ coordinates by the inverse of the RSSI difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of Wi-Fi RSSI. Experimental results show that the proposed weighted algorithm considerably outperforms the KNN, Euclidean-WKNN, Manhattan-WKNN, EWKNN, LiFS and GPR in terms of positioning accuracy which is defined as the cumulative distribution function of position error. The results also demonstrate that reference points in indoor area without linear constraints can be clustered automatically by the proposed clustering algorithm, and CDF of the proposed clustering algorithm outperforms KNN, WKNN, RPLC and SDC.

Indoor mapping is an essential element of indoor navigation systems. In this paper, we present a graph optimization-based method for indoor map construction, which can be used in buildings without prior knowledge. By using crowdsourcing data on mobile phone sensors, we can derive activity landmarks where people perform different activities (turning, taking the elevator, taking the escalator, and walking up/down the stairs). Our method uses graph optimization techniques to align crowdsourcing trajectories by their intersecting landmarks. After trajectory alignment, this method applies a pose graph optimization method to construct an indoor map. Finally, our method performs a transform from relative coordinates to an absolute coordinate with reference points and reduces the redundant segments using dynamic time warping. To evaluate the performance, we implement the proposed method in an office building and a shopping mall. Experiment results show that 80th percentile error of the mapping accuracy is about 1.7-3.5 m. Moreover, the proposed method can deal with the curved routes in a building and can also decrease the amount of required data.