# Special Issue on \"Recent Advances in Indoor Localization Systems and Technologies\"
###### Abstract
In [19], common infrastructure elements and services, already present in smart environments (e.g., WiFi, access-control services, and BLE beaconing), are used to enhance the quality of indoor positioning. The solution proposed in [20] combines BLE and proximity sensors to increase coverage and accuracy in an Ambient and Assisted Living environment. The hybrid system of [21] combines PDR with occasional correctional steps using Visual Place Recognition, to eliminate the cumulative error of PDR and enhance its accuracy when vertical movement between different floors is possible. In [22], a closed-form localization algorithm is introduced for scenarios where multiple TDOA measurements and a single TOA measurement are also available. The review paper [23] introduces and compares the performance of various integrated remote sensing technologies, which combine, e.g., GPS, UWB, RFID, sensor networks, and digital imaging. **Techniques to enhance the performance of positioning systems:** In addition to the combination of various sensing modalities, other techniques have been proposed to improve the performance of indoor localization systems. Papers [24; 25; 26] propose enhancements to systems using RSS-based WiFi fingerprinting. In [24], a dimensionality reduction technique is proposed, where non-informative anchor nodes are eliminated to reduce computation time and also increase system performance. In [25], methods are proposed to enhance the performance of the feature extraction and the fingerprinting processes, and extensions are introduced to handle the unknown-map situation. In [26], Deep Learning methods are used to enhance the performance of WiFi fingerprinting: A stacked denoising auto-encoder is applied to extract robust features from noisy measurements and other model-based techniques (Kalman filters and Hidden Markov Models) are used to improve the estimate. In [27] an Extended Kalman Filter-based solution, utilizing Channel State Information, is proposed to enhance the accuracy of RSS-based ranging in challenging indoor environments. In [28], a unified state-estimation algorithm is proposed applying finite memory structure filtering and smoothing. The proposed unified approach has attractive properties, e.g., it provides robust, unbiased, and dead-beat state estimates (e.g., position, speed, and acceleration) in noise-free cases. To improve the accuracy of PDR systems, in [29], a novel adaptive Kalman filter-based heading estimation method is proposed. **Quality control:** The actual state of the environment may strongly influence the accuracy of measurements used for positioning, thus affect the quality of the positioning process as well. High precision localization techniques must monitor the state of both the environment and the measurement process, detect potential anomalies and adapt the estimation process accordingly. Papers in this section propose various solutions to problems emerging in laser- and radio-based positioning systems. Paper [30] investigates positioning systems using laser sensors, being sensitive to refracted and reflected beams when transparent objects are present. The authors propose a novel algorithm to detect the presence of transparent objects, by analyzing the pattern of the reflected noise. In [31], a novel method is proposed to identify the indoor/outdoor environment using GPS signals and machine learning classification techniques. Paper [32] proposes a method to ensure the accuracy and reliability of positioning results in indoor pseudolite systems: An adaptive fault-detection method is applied to find and exclude potentially faulty pseudolite measurements, influenced by multipath effects, clock drift, or noise. In papers [33; 34; 35], problems of line-of-sight (LOS) and non-line-of-sight (NLOS) situations are studied in radio-based systems, where accurate ranging is possible with LOS. These papers propose various NLOS identification methods, which help to eliminate the influence of NLOS errors on positioning accuracy.