Indoor Localization Algorithm Research Articles

Determination of an Indoor Target Position: An Accurate and Adaptable Approach Based on Differential Positioning

The growing demand for new products that rely on the accurate identification of a target’s location indoors, while remaining mindful of cost, continues to drive research in this important and challenging area. Researchers are actively pursuing algorithmic improvements to eliminate errors introduced from complex interference factors present in indoor, wireless communication environments. In this work, we adopt a differential signal strength method in the design of our new indoor localization algorithm. The proposed algorithm reduces errors in the time domain by smoothing out the wireless signal fluctuations, thus stabilizing the signal; a single exponential algorithm is applied to the signal strength parameters collected to accomplish this. The target’s position is then computed by utilizing both the plane geometric method and difference localization theory. This combination of techniques is reasonable for the environment under consideration (small scale, wireless), as the multipath effects for the signal are approximately equal under these conditions. In addition, the proposed approach is compatible with a wide variety of technologies (e.g., RFID and Bluetooth); it can be cost-effectively deployed by leveraging an existing hardware infrastructure. The proposed approach has been implemented and experimentally validated. The test results are very promising: they indicate that our algorithm improves the positioning accuracy by 70%–80% in comparison with the trilateration and LANDMARC positioning algorithms.

BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching.

Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.

WKNN indoor location algorithm based on zone partition by spatial features and restriction of former location

High-precision indoor location algorithm using fingerprint highly relies on the accuracy of database. This paper proposes a WKNN indoor location algorithm based on spatial characteristics partition and former location restriction. In this proposed system, target space of large area is divided into multiple partitions by its spatial characteristics, solving the problem that one fingerprint database cannot achieve total coverage. Also, the restricted relationship between the former and the present position are introduced to increase the quality of chosen candidate reference points, and thus improve the smoothness of the estimation results obviously. A large number of indoor positioning experiments show that this algorithm could effectively improve the indoor positioning accuracy when compared with the traditional WKNN.

Dictionary-Based Statistical Fingerprinting for Indoor Localization

Indoor localization is a challenging task as the signal propagation in indoor environments does not adhere to the classical path loss or other simple models. Modern high-accuracy indoor localization algorithms rely on fingerprinting methods that entail an offline phase of data collection and mapping phase. This work puts forth a dictionary learning framework for fingerprinting indoor locations using GSM, WiFi, and other sensor measurements. Different from the existing works, the proposed methods are model-free, can handle missing measurements, and fingerprints can be flexibly updated in an online fashion. The dictionary learning algorithm itself is appropriately modified to ensure incoherence between fingerprints of nearby locations and appropriate weighing of the sparse coefficients. A hidden markov model based tracking algorithm is also provided that considers the recent history while estimating the user location. Detailed experiments are carried out over four different maps to demonstrate the efficacy and usefulness of the proposed algorithm, in comparison to the state-of-the-art classification-based and model-based indoor localization algorithms.

Endpoints-Clipping CSI Amplitude for SVM-Based Indoor Localization.

With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique.

Cogent Machine Learning Algorithm for Indoor and Underwater Localization Using Visible Light Spectrum

From last few years indoor localization has become more popular for wireless devices. The major reason for its popularity is to access current location efficiently. On the other side visible light communication is attaining interest of researchers due to high growth of wireless communication and solid state lighting. This network structure can produce high accuracy for the resident positioning electromagnetic environment. The proposed approach is viable for both air and underwater communication based on the visible light spectrum. We evaluate the technique for other supervised machine learning algorithms to analyse an accuracy, error distance and computational time for indoor localization in visible light communication network. Experimental work was made by using star topology supported by visible personal area network system based simulator, which corresponds an attribute of PHY and MAC layer for IEEE 802.15.7 standard designed for short range optical wireless communication. The evaluation was carried out for an accuracy, error distance and computational time. The results show that the suggested methodology achieves overall computational accuracy and deliver an acceptable location estimation error.

An indoor location algorithm based on Kalman filter fusion of ultra-wide band and inertial measurement unit

Indoor positioning technology has been widely used in today’s life, but due to the influence of multipath effect, the positioning signal is attenuated or even interrupted seriously, resulting in obvious reduction or even failure of positioning accuracy. Therefore, the emerging multi-sensor joint positioning has become the general trend of the development of positioning technology, in which Ultra-Wide Band (UWB) and Inertial Measurement Unit (IMU) have their own features in positioning and navigation. So this paper combines the advantages of UWB and IMU to achieve accurate positioning in complex environment. Firstly, the signal transmission law in complex environment is obtained by distinguishing Line of Sight (LOS) from NLOS (Non Line of Sight) environment. Secondly, the maximum likelihood estimation algorithm is used to eliminate the influence of NLOS on the transmitted signal, and then the extended Kalman filter information fusion strategy is used. The ranging information of UWB and the angle information of IMU are fused to realize the accurate positioning of UWB in complex environment. Finally, the experimental results show that the performance of the joint positioning proposed in this paper is obviously better than that of a single sensor compared with single UWB and single IMU positioning. It provides more solutions for accurate indoor positioning of multi-sensor fusion.

Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi

Various research works have been proposed for Wi-Fi-based indoor localization, including Received Signal Strength Indicator (RSSI)-based fingerprint algorithm, Angle of Arrival (AoA)-based algorithm and so on. However, since RSSI value cannot accurately express the spatial features of emitted wireless signal, and the interfering noise in indoor environment makes the wireless signal distortion, RSSI-based localization algorithm cannot achieve an ideal accuracy. In this paper, we utilize Channel State Information (CSI) extracted from MIMO-OFDM PHY layer as fingerprint image to express the spatial and temporal features of Wi-Fi signal. At the same time, an indoor localization algorithm is also proposed, which is based on convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter, to achieve accurate position estimate with time-varying measurement noise and process noise in complex indoor environment. According to the simulation results, compared with existing methods, our proposed algorithm improves the positioning accuracy up to 51.8%. In the real indoor environment, our proposed algorithm improves the positioning accuracy up to 22% in LoS scenario, and 9.8% in NLoS scenario, respectively.

An Emergency Seamless Positioning Technique Based on ad hoc UWB Networking Using Robust EKF.

In this paper, a new emergency positioning technique is proposed based on ad hoc GNSS/UWB (Global Navigation Satellite System/Ultra-Wideband) network. The main innovations of the program are reflected in two aspects. First of all, a unified coordinate frame for indoor and outdoor environments is constructed dynamically with GNSS/UWB integration. In the outdoor environments, the high accuracy positioning can be achieved with GNSS/UWB equipment. The high-accuracy indoor coordinate is obtained by measuring the range observations between adjacent network nodes and outdoor GNSS/UWB nodes, and the range information of the UWB network is transmitted to the cloud server center. A network adjustment algorithm is proposed to improve the positioning accuracy of the UWB network. Secondly, a UWB indoor location algorithm based on robust EKF (Extended Kalman Filter) is proposed. By analyzing the transfer characteristics of gross error in EKF model, a new robust EKF model is established. The model is constructed based on the statistical characteristics of redundant observation components and prediction residual. The robust equivalent gain matrix is constructed, and the robust positioning solution of UWB is obtained with iteration. The global test is carried out first to further improve the real-time operation efficiency. Finally, a field indoor and outdoor seamless positioning experiment was carried out to verify the effectiveness of the proposed algorithm. The results show that the positioning accuracy of UWB emergency network nodes (anchors) can reach 0.35 m. Based on the network, the positioning accuracy of the tag can reach 0.38 m by applying the improved robust EKF positioning algorithm, which is improved by 20.83% and 73.43% compared with standard EKF and least square method, respectively.

Indoor localization algorithm based on behavior-driven predictive learning in crowdsourced Wi-Fi environments

The indoor localization algorithm based on the behavior-driven predictive learning (BDPLA) executes machine-learning predictions by computing the shortest path from a starting location to a destination. The proposed algorithm selects a set of reference points (RPs) to predict the shortest path using all available RPs from the crowdsourced Wi-Fi environment. In addition, the proposed algorithm utilizes the collected received signal strength indicator (RSSI) values to determine the error distance. Using principal component analysis (PCA), the existing crowdsourced RSSI data can be calibrated to help decrease the inconsistent RSSI values among all received signals by reconstructing the values. The average error distance of 3.68 m achieved better results compared with the traditional fingerprint map with an average result of 6.96 m.

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition

The localization accuracy of the existing methods for indoor Wi-Fi access points ranging-based localization depends on the accuracy of the received signal strength measured. Because the existing ranging-based methods are interfered by various indoor environmental factors, it is difficult to accurately measure the received signal strength, which leads to the problem of low localization accuracy of the indoor Wi-Fi access points. An indoor Wi-Fi access points localization algorithm based on improved path loss model parameter calculation method and recursive partition is proposed in this article. The algorithm recursively partitions the region where the target Wi-Fi access points are located according to the idea of quadtree partition, and partitions it into same sub-grids, which is sequentially performed until the sub-grids are smaller than the set threshold. The detection device is used at the detection location of the grids to measure the received signal strength, which is from the detection points to the target access point, the grid center point is used as the location of the candidate target access point, the parameters in the path loss model are calculated by using the signal strength differences between the detection points, and then the distances between the detection points and the target access point are calculated by using the signal strength values from the detection points to the target access point. Finally, the location of the target access point is estimated by executing a localization algorithm, and the location of the grid center point closest to the target access point is taken as the location of the target access point. The experimental results show that under the premise that the target access point can be found, the proposed algorithm reduces the use of the device and improves the localization accuracy compared with the typical localization method.

RSS ranging based indoor localization in ultra low power wireless network

Advances in ultra low power based wireless networks have enabled developments of a variety of applications including indoor localization systems. Considering the commercial 2.4 GHz wireless devices choose lower and lower power consumption strategy, it is challenging to design an ultra low power based real time indoor localization system. In ultra low power channel conditions, the rapid increase of received signal strength (RSS) measurement variabilities for the minimum RSS value ranges, the spatiotemporally different RSS attenuations, and the common non-line of sight (NLOS) problems make the indoor localization system significantly inefficient. Considering the practical dynamics of ultra low power indoor wireless channel environments, we propose the indoor localization algorithm which can accommodate the environmental dynamics automatically in real time.

Confidence-Interval-Fuzzy-Model-Based Indoor Localization

In this paper, an efficient indoor localization algorithm based on the confidence-interval fuzzy model is presented. The width of the confidence interval is essential within the proposed fingerprinting method for calculating weights, which are then taken into account while searching for the $K$ nearest neighbors in the database of fingerprints. For each beacon in the test room, a new confidence-interval fuzzy path-loss model composed of several local linear models is constructed. The map of fingerprints is then constructed of a set of confidence-interval fuzzy models. By their consideration, the localization accuracy is significantly improved in comparison with other commonly used path-loss models. The most important novelty of this paper is the introduction of the confidence interval within the fingerprinting method, which additionally improves localization results. The platform of the localization system is developed on the basis of a smartphone and Bluetooth beacons. Therefore, the localization algorithm has to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone.

IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario.

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms.

RSS-Distance Rationalization Procedure for Localization in an Indoor Environment

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor environment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better estimating RSS relationship with distance. By employing a unique form of rationalization of raw RSS with respect to distance using the proposed algorithm, it is possible to enhance the reliability of RSS when employed in indoor Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate position estimation in an indoor environment.

A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

With ever-increasing demands on location-based services in indoor environments, indoor localization technologies have attracted considerable attention in both industrial and academic communities. In this work, we propose a scalable indoor localization algorithm (SILA) consisting of two components, namely an annulus-based localization (ABL) component and a local search-based localization (LSL) component, with the objectives of enhancing localization accuracy and reducing online computational overhead. First, the ABL component is developed based on distance fitting using received signal strength indicator (RSSI) of Wi-Fi-based devices. In particular, a distance-RSSI fitting model is proposed based on multinomial function fitting, which is adopted to estimate the distance between the Wi-Fi access point (AP) and the mobile device. On this basis, an annulus construction scheme is proposed to confine the online searching space for possible locations of the mobile device. In addition, based on the observation of signal attenuation characteristics in different physical environments, we design a subarea division scheme, which not only enables the system to choose proper distance-RSSI fitting functions in different areas, but also reduces the overhead of distance fitting. Second, the LSL component is developed based on fingerprint mapping using RSSIs collected at APs. In particular, an RSSI distribution probability model is derived to better map the signal features of an online point (OP) with that of reference points (RPs). Then, an online localization algorithm is proposed, which selects a set of candidate RPs based on Bayes theorem and estimates the final location of an OP using K-nearest-neighbor (KNN) method. Finally, we implement the system prototype and compare the performance of SILA with two representative solutions in the literature. An extensive performance evaluation is conducted in real-world environments, and the results conclusively demonstrate the superiority of SILA in terms of both localization accuracy and system scalability.

Wireless Indoor Localization Using Passive RFID Tags

Location-based services (LBSs) have recently become a vital part of life. Global Navigation Satellite Systems, e.g., GPS, have been used in a wide range of applications. These systems face a common challenge: their performance can be significantly affected by the presence of obstacles. For instance, when the target object is close to walls, buildings, trees, or located in indoor environments, GPS based services may not work correctly. However, in reality, the presence of obstacles is inevitable in many critical scenarios, such as disaster response and rescue. To address this challenge, we propose an indoor localization system which uses radio frequency identification (RFID) tags to localize moving targets. Our indoor localization algorithm derives the accurate location of a target object using a set of RFID tags. For tag deployment, we have developed a greedy algorithm that uses a novel score function guide the deployment, aiming to guarantee maximum coverage using a limited number of tags. The experimental results show our approach is effective in localizing target objects with high accuracy.

Electronically steerable antenna array for indoor positioning system

This paper presents a 2.4 GHz electronically steerable antenna array (ESAA) using a phase shifter based on the 4×4 Butler matrix which is suitable for the wireless indoor positioning system (WIPS). The low cost and simplicity are advantages of the proposed ESAA. The main beam of the antenna can be steered at four positions in the elevation plane, from −37°, −12°, 12° to + 36° with the narrow beamwidth in order to increase localization accuracy and wide-angle coverage of 240° in the azimuth plane. The ESAA is demonstrated as a base station antenna in a single-anchor indoor localization system experimentation. Two indoor localization algorithms, including fingerprinting and direction-of-arrive (DoA), are implemented and discussed in this paper. The indoor measurement setups and localization results using the proposed ESAA are proven in this paper to have better results than other similar approaches.

Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.