The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS’s). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.

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A Survey of Machine Learning for Indoor Positioning

40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges

Electromagnetic wave is susceptible to the multipath effect, especially in complex indoor environment. As transmission of non-line of sight (NLOS), it leads to bigger measurement error, which makes positioning accuracy decline. In the paper, we aim to compensate the measurement error mainly affected by the NLOS to enhance the positioning accuracy. Conceptions of “virtual inertial point” and “environmental factor” are introduced, and moreover acceleration data of inertial measurement unit (IMU) are adopted. A novel and simple NLOS error compensation method for high accuracy indoor positioning system based UWB is proposed. Based on the principle of inertia, IMU can be used to fix motion trend of tag and to get coordinate of virtual inertial point. The measurement coordinate can be acquired by UWB system. We can calculate the distances between virtual inertial point and real measurement in same area, respectively under line of sight (LOS) and NLOS in advance. The ratio of two different distances can be defined as environmental factor, which is mainly used to reflect the influence of NLOS on positioning accuracy quantitatively relative to LOS in the paper. Environmental factor, as the weighted coefficient, is adopted to merge coordinates of the virtual inertial point and measurement to obtain the coordinate compensated under NLOS. It is a process of scaling measurement down to user-defined area for positioning accuracy requirement to reduce the impact of NLOS. At last, Kalman Filter is used to smooth the set of coordinate compensated under NLOS to further improve positioning accuracy. Experimental results show that our method can effectively compensate the NLOS error in the indoor positioning system based ultra-wide band (UWB) technology. The positioning accuracy is improved nearby 80% in NLOS area.

A Novel NLOS Error Compensation Method Based IMU for UWB Indoor Positioning System

Estimating the location of a target is essential for many applications such as asset tracking, navigation, and data communications. Time-difference-of-arrival (TDOA) based localization has the main advantage that it does not require synchronization between the transmitting and the receiving sides. Phase-difference-of-arrival (PDOA) provides additional information that can be leveraged to enhance localization performance. The combination of TDOA and PDOA for localization has not been reported in the literature. In this letter, we propose a novel approach that incorporates both TDOA and PDOA to achieve improved position estimation. In the proposed approach, an initial location estimate is obtained by optimizing a TDOA cost function. Next, a PDOA, or a hybrid TDOA-PDOA cost function is optimized using a particle swarm optimizer to obtain the final location estimate. Simulation results show that the proposed approach sufficiently, and justifiably, improves localization performance relative to pure TDOA methods.

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A Joint TDOA-PDOA Localization Approach Using Particle Swarm Optimization

Demands for indoor positioning based services (IPS) in commercial and military fields have spurred many positioning systems and techniques. Complex electromagnetic environments (CEEs) may, however, degenerate the accuracy and robustness of some existing single systems and techniques. To overcome this drawback, fusion-based positioning of multiple systems and/or techniques have been proposed to revamp the positioning performance in CEEs. In this paper, we survey the fusion-based indoor positioning techniques and systems from seminal works to elicit the state of the art within our proposed unified fusion-based positioning framework, which consists of three fusion characteristics: source, algorithm, and weight spaces. Different from other surveys, this survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics. Meanwhile, discussions in terms of lessons, challenges, and countermeasures are also presented. This survey is invaluable for researchers to acquire a clear concept of indoor fusion-based positioning systems and techniques and also to gain insights from this survey to further develop other advanced fusion-based positioning systems and techniques in the future.

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A Survey on Fusion-Based Indoor Positioning

The burden of fingerprints building and calibration is the main bottleneck in complex indoor environments. Crowdsourcing is an attractive strategy to alleviate the burden. However, most existing approaches of estimating crowdsourced samples' locations are based on shallow machine learning architecture, which cannot fully characterize the relationship between RSS samples and locations. Consider the ability of deep learning structure in extracting reliable features from the noisy fluctuating RSS signals, we propose a transductive semi-supervised deep learning model, i.e., the unlabeled crowdsourced data can aid the training process of the model, and then the trained model can further improve the localization accuracy of the unlabeled data. First, a labeled crowdsourced sample augmentation technique is proposed to overcome the overfitting of artificial neural network (ANN) caused by insufficient data. Then, augmented labeled samples and unlabeled crowdsourced samples are utilized to train the semi-supervised ANN to construct a robust estimator. Finally, unlabeled crowdsourced samples are input into the estimator to obtain estimated labels. Experimental results show that the proposed method effectively improves the accuracy of floor detection compared with existing conventional approaches using crowdsoucred unlabeled samples.

Yuan Shao

Papers

A Survey on Fusion-Based Indoor Positioning

A semi-supervised deep learning approach towards localization of crowdsourced data