Indoor Localization Algorithm Research Articles

Precise indoor localization with 3D facility scan data

AbstractVisual indoor localization for smart indoor services is a growing field of interest as cameras are now ubiquitously equipped on smartphones. In this study, a hierarchical indoor localization algorithm is designed and validated based on 3D facility scan data, which are originally collected for facility modeling purposes. The study has shown promising results in indoor localization. The study also demonstrated a scalable approach to generate high‐quality images with reference poses from laser scan data, opening doors to generate labeled images to train end‐to‐end pose regression model (i.e., PoseNet). In this regard, this study is the first attempt to leverage facility scan data, which are commonly collected for Building Information Modeling (BIM) purpose, for indoor localization. As more facilities are documented with laser scanners, our algorithm can unlock additional values of collected data for intelligent applications.

Experimental testbed evaluation of cell level indoor localization algorithm using Wi-Fi and LoRa protocols

Exact location of an object is not required in some scenarios of indoor localization and all we need to know is the area or a cell where the object is present in a room or a hall. For instance, the object to be located is large and we are interested in knowing the locations of the cell in which the object lies, or we want to find the quadrant of a room in which the object of interest lies. For such cases, in this paper, we propose a novel 2-phase cell localization algorithm based on trilateration. In the first phase we find the path loss exponent for each anchor node using RSSIs and in second phase we divide the available space into virtual cells and find the location of the cell in which the object is present. We have conducted real testbed experiments using Wi-Fi and LoRa with different transmission powers and calculated the error. Results show that the proposed algorithm successfully performs cell localization and Wi-Fi based localization is better than that of LoRa. Moreover, we have also studied how the accuracy of localization is affected by cell size in both of the wireless protocol.

Ble Based Indoor Positioning System and Minimal Zone Searching Algorithm (MZS) Applied to Visitor Trajectories within a Museum

Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. In this paper, we describe an experiment that held within the museum of natural history of La Rochelle with a class of high school volunteers. We will explain our systems that has been built to work in this specific case, and among other things formalize our algorithm for indoor localization that has not had an equivalent in the state of the art. The minimal zone searching algorithm (MZS) can compute in real time the position of a visitor, shaped as a zone with an average surface of 3 m2 when resources are limited and when the placement of nodes must respect the constraints imposed by the room’s layout. This method offered good results with data collected during the experimentation, such as a meaningful representation of the position of a visitor and most importantly a stable execution during the whole experience even when the subject was in tight spaces.

Indoor Localization Algorithm of Terminal Based on RSS Feature Extension and Spectral Regression Kernel Discriminant Analysis

Indoor Localization Algorithm of Terminal Based on RSS Feature Extension and Spectral Regression Kernel Discriminant Analysis

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

Autonomous 3D Indoor Localization Based on Crowdsourced Wi-Fi Fingerprinting and MEMS Sensors

Location-based services have become more and more important with the development of the Internet of Things (IoT) technology in recent years. Global Navigation Satellite System (GNSS) is widely used for positioning outdoors while it is still challenging to realize accurate and universal 3D indoor localization in complex indoor environments. Crowdsourcing-based positioning method is proposed aiming at autonomously constructing the navigation database based on the pedestrians’ daily-life data. This paper proposes an autonomous 3D indoor localization algorithm using the combination of crowdsourced Wi-Fi fingerprinting and Micro-Electro-Mechanical System sensors (3D-CSWS). An enhanced complementary filter is applied to provide accurate attitude information by integrating multi-sensor data with the detection of external acceleration and quasi-static magnetic field. In addition, the gradient descent (GD) algorithm is proposed to optimize the forward pedestrian dead reckoning and the optimized trajectories are weighted fused to construct the final navigation database after quality evaluation. In the on-line phase, the adaptive particle filter is used to integrate the results of Wi-Fi fingerprinting and multiple sensors to provide accurate and concrete 3D indoor localization performance. The real-world experimental results demonstrate that the proposed 3D-CSWS is proved to achieve autonomous and precise 3D indoor localization performance among complex indoor environments.

A Novel 3-D Indoor Localization Algorithm Based on BLE and Multiple Sensors

Indoor wireless localization using Bluetooth low energy (BLE) beacons has attracted considerable attention due to its extensive distribution and low cost properties. This article proposes a novel 3-D indoor localization algorithm which uses the combination of BLE and multiple sensors (3D-LBMS). The inertial navigation system (INS) and pedestrian dead reckoning (PDR) mechanizations are combined for accurate heading and speed estimation, which contains a multilevel constraints-based quasistatic magnetic field (QSMF) detection algorithm. In addition, dynamic-time-warping (DTW)-based BLE landmark detection algorithm is proposed to provide absolute 3-D location reference to multiple sensors-based positioning method, and the detected BLE landmark points are also used to calibrate the parameter of step-length calculation. Finally, the adaptive unscented Kalman filter (AUKF) is applied to fuse the results of INS/PDR mechanizations, QSMF and locations of detected BLE landmarks to achieve accurate and concrete multisource-based 3-D indoor localization performance. The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level 3-D altitude estimation accuracy in typical indoor environments.

RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is “closest” to the target node. Then the target node’s location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.

Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

Adaptive Indoor Localization System for Large-Scale Area

Generally, fingerprint-based indoor localization works inefficiently when deployed in a large-scale area. This is because it consumes massive resources and takes long processing time for searching the exact location in the large fingerprint database. Moreover, the changing environment can degrade overall performance. To tackle these problems, we propose an adaptive indoor localization system for a large-scale area. Our system consists of three main parts. First, our area classification algorithm is the key to overcome the problem caused by the large-scale area. It identifies an area of the user’s queries whether they are outdoor or located in a specific building. Specifically, the algorithm can filter out the queries sent from outdoor or out-of-scope areas. Then, the information of this part is sent to the next part. Second, our fingerprint-based indoor localization algorithm can utilize the information from the first part by searching only the fingerprint in the specific building. This can significantly reduce searching space and processing time in order to localize the exact location. Third, our missing-BSSID detector algorithm detects the missing Basic Service Set Identifiers (BSSIDs) in the incoming query and updates a sampling database. This part is for our system to quickly adapt to the changing environment. We evaluated and deployed our system in a large-scale exhibition including 37 multi-floor buildings, covering 486,000 m2 and generating approximately 600,000 records of queries from users. In addition, we created a simulation to evaluate our system in the critically-changing environment. Our proposed system achieves high accuracy. More importantly, our area classification algorithm can significantly reduce the overall processing time compared to the previous work. Also, we showed that when applying our missing-BSSID detector algorithm to our system as well as other existing systems, the overall system performance can be significantly improved.

UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

With the increasing application potential of indoor personnel positioning, ultra‐wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non‐Line‐Of‐Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non‐Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.

An Improved XGBoost Indoor Localization Algorithm

This paper proposes an improved XGBoost Wi-Fi indoor positioning algorithm aiming at the accuracy problem caused by the change of environment.The method first uses Extreme Gradient Boosting (XGBoost) algorithm to establish indoor positioning model, which can achieve indoor positioning. When the environment changes, further combine error compensation (EC) method to improve the initial positioning. In addition, the positioning trajectory is compared with the actual trajectory and the unimproved positioning trajectory to verify the stability of the algorithm. The experimental results show that the 80-th percentile of the achieved accuracy is 1.11m after the change of environment, which is significantly better than the unimproved positioning algorithms based on support vector machine, random forest and extreme gradient promotion, and the obtained positioning trajectory tends to converge with the actual trajectory.

Smartphones: 3D Indoor Localization Using Wi-Fi RTT

This letter proposes a 3D indoor localization algorithm for smartphones using wireless fidelity (WiFi) round-trip time (RTT). In the proposed algorithm, the weighted centroid (WC) algorithm is utilized to estimate the rough two-dimensional (2D) position due to its easy implementation and low complexity. The coarse target altitude is acquired according to pedestrian activity. Then, the coarse altitude and 2D position combine into a rough 3D position, which is regarded as the initial position of the standard particle swarm optimization (SPSO) algorithm. The SPSO algorithm aims to estimate a more accurate 3D location on the basis of the cursory 3D position of the smartphone. To reduce the computation, we use the density-based spatial clustering of applications with noise (DBSCAN) algorithm to assist in updating the SPSO particles. Experimental results show that the proposed positioning algorithm has better 3D accuracy than WC and Least-squares (LS) algorithms, with a 2D accuracy of 1.147 meters and an altitude precision of 0.305 meters.

An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

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.

Wireless Network Indoor Positioning Method Using Nonmetric Multidimensional Scaling and RSSI in the Internet of Things Environment

Aiming at the problem that the indoor target location algorithm based on received signal strength (RSSI) in the IoT environment is susceptible to interference and large fluctuations, an indoor localization algorithm combining RSSI and nonmetric multidimensional scaling (NMDS) is proposed (RSSI- NMDS). First, Gaussian filtering is performed on the received plurality of sets of RSSI signals to eliminate abnormal fluctuations of the RSSI. Then, based on the RSSI data, the dissimilarity matrix is constructed, and the relative coordinates of the nodes in the low-dimensional space are obtained by NMDS solution. Finally, according to the actual coordinates of the reference node, the coordinate transformation is performed by the planar four-parameter model, and the position of the node in the actual coordinate system is obtained. The simulation results show that the proposed method has strong anti-RSSI perturbation and high positioning accuracy.

Preserving Privacy in WiFi Localization With Plausible Dummy Locations

Benefiting from the development of wireless communication, WiFi localization plays a vital role in various mobile applications. However, users’ locations are breached by untrusted localization servers, thus disclosing users’ location privacy and other sensitive personal information. Existing works on WiFi localization preservation employed homomorphic encryption, which incurs extremely high overheads. On the other hand, lightweight methods like $k$ -anonymity and dummy-based approaches are hardly used for privacy preservation in WiFi localization scenarios due to their inherent drawbacks, i.e., $k$ -anonymity approaches rely on trusted third parties while dummy-based approaches are susceptible to spatio-temporal correlation attacks. To design an effective yet lightweight WiFi localization privacy algorithm, this paper proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This study presents a novel algorithm, referred to as Location Preservation Algorithm with Plausible Dummies (LPPD), which is the first attempt towards WiFi localization privacy preservation using dummy techniques. The benefits of the algorithm are three-fold: First, it is lightweight. Second, it does not rely on trusted third parties. Third, it can resist spatio-temporal correlation attacks. Moreover, this study implements a WiFi localization system that integrates the proposed privacy-preserving algorithm with a WiFi fingerprint based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.

Improved Global Localization and Resampling Techniques for Monte Carlo Localization Algorithm

Global indoor localization algorithms enable the robot to estimate its pose in pre-mapped environments using sensor measurements when its initial pose is unknown. The conventional Adaptive Monte Carlo Localization (AMCL) is a highly efficient localization algorithm that can successfully cope with global uncertainty. Since the global localization problem is paramount in mobile robots, we propose a novel approach that can significantly reduce the amount of time it takes for the algorithm to converge to true pose. Given the map and initial scan data, the proposed algorithm detects regions with high likelihood based on the observation model. As a result, the suggested sample distribution will expedite the process of localization. In this study, we also present an effective resampling strategy to deal with the kidnapped robot problem that enables the robot to recover quickly when the sample weights drop-down due to unmapped dynamic obstacles within the sensor’s field of view. The proposed approach distributes the random samples within a circular region centered around the robot’s pose by taking into account the prior knowledge about the most recent successful pose estimation. Since the samples are distributed over the region with high probabilities, it will take less time for the samples to converge to the actual pose. The percentage of improvement for the small sample set (500 samples) exceeded 90% over the large maps and played a big role in reducing computational resources. In general, the results demonstrate the localization efficacy of the proposed scheme, even with small sample sets. Consequently, the proposed scheme significantly increases the real-time performance of the algorithm by 85.12% on average in terms of decreasing the computational cost.

Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

Indoor localization algorithms in smart cities often use Wi‐Fi fingerprints as a database of Received Signal Strength (RSS) and its corresponding position coordinate for position estimation. However, the issue of fingerprinting is the use of different platform‐devices. To this end, we propose a Long Short‐Term Memory (LSTM)‐based novel indoor positioning mechanism in smart city environment. We used LSTM, a type of recurrent neural network to process sequential data of users trajectory in indoor buildings. The proposed approach first utilizes a database of normalizing fingerprint landmarks to calculate WiFi Access Points (WAPs) RSS values to mitigate the fluctuation issue and then apply the normalization parameters on the RSS values during the online phase. Afterwards, we constructed a transfer model to adapt the RSS values during the offline phase and then applying it on the RSS values from the different smartphones during the online phase. Thorough simulation results confirm that the proposed approach can obtain 1.5 to 2 meters positioning accuracy for indoor environments, which is 60% higher than traditional approaches.

Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization

A new method is proposed for selecting Access Points (APs) with propagation direction combination based on Eight-Diagram for indoor localization in indoor environments, which can not only improve positioning accuracy, but also reduce computational complexity by using fewer APs. With propagation direction combination based on eight basic directions, for instance, east, south, west, north, southeast, northeast, southwest and northwest, provided by the Eight-Diagram, the proposed AP selection algorithm can be applied to indoor scenarios where Wi-Fi RSSI is corrupted by multipath interference. Experiments were conducted and the results demonstrate that the proposed AP selection algorithm achieves an accuracy considerably better than the WKNN, MaxMean, InfoGain and PCA methods. Due to the use of fewer APs, the proposed algorithm has lower computational complexity than the MaxMean and InfoGain algorithms, and equivalent with the PCA algorithm.

A robust and accurate indoor localization system using deep auto-encoder combined with multi-feature fusion

Many existing indoor localization systems use RSS as fingerprints, but RSS is a coarse-grained data, which not only fluctuates over time but also is not unique for a specific location due to rich multi-path effects and shadow fading in indoor environments. In order to further improve the localization accuracy, a robust and accurate indoor localization algorithm based on deep auto-encoder network combine with multi-feature fusion. To extract deep features hidden in CSI data and reduce the computational complexity of localization, an improved method is designed to achieve dimension reduction and feature extraction, which can avoid explicit information extraction of available CSI characteristics on the basis of effective representation of CSI fingerprint difference between different locations. Then, the fingerprint database is constructed by combination of RSS and CSI coding. Moreover, the Naive Bayer Classifier is adopted to improve the localization accuracy and stability. In the simulation experiment, the positioning effect of the proposed algorithm under different fingerprint libraries and the positioning effect under different positioning methods are mainly carried out. Experimental results show that the proposed method can effectively perform indoor positioning and has good practicability in the actual production environment.