# Special Issue on \"Recent Advances in Indoor Localization Systems and Technologies\"
###### Abstract  
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this special issue present recent advances and new developments in indoor localization systems and technologies, proposing novel or improved methods with increased performance, providing insight into various aspects of quality control, and also introducing some unorthodox positioning methods. indoor localization technologies; hybrid positioning; model based techniques; quality control 2021  
While outdoor localization solutions mainly depend on services offered by well-established Global Navigation Satellite Systems (e.g., Global Positioning System-GPS), indoor localization technologies have not yet reached this level of maturity. There has been enormous technical progress in the past decade, resulting in various competing techniques and technologies for sensing, positioning, and tracking. The two main directions of indoor localization methods contain reference-based and reference-free methods. For references, various types of anchor nodes (e.g., WiFi hotspots, light sources, or pseudo-satellites-pseudolites) [1, 2, 3], or various maps (e.g., WiFi or magnetic fingerprint maps of a building) [4, 5] can be used. Reference-free approaches do not use such external aids but rather estimate the position of the tracked entity from its measured inertial properties (e.g., Pedestrian Dead Reckoning-PDR) [6]. A wide range of technologies is used to create the reference system, including audio signals [7], magnetic signals [8], visible light [9], and radio communication techniques (e.g., WiFi, Bluetooth, Bluetooth Low Energy-BLE, Radio-Frequency Identification-RFID, and Ultra-wideband-UWB), [1, 10, 11, 12]. For sensing, various metrics have been proposed, including the Received Signal Strength (RSS), the Time of Flight (ToF) or Time Difference of Arrival (TDoA) of signals, the Angle of Arrival (AoA) or Angle Difference of Arrival (ADoA) of signals, or the Phase of Arrival (PoA) of signals [13]. From the measured signals' properties, the position is estimated using various analytical or iterative methods. To increase accuracy and performance of indoor localization systems, hybrid solutions have been proposed (e.g., PDR combined by WiFi [14]). Machine learning methods (Neural Networks and Deep Learning) and various model-based techniques are widely used (e.g., various Kalman Filters, Particle Filters) [15, 16]. Despite advances in technology, the main challenges in indoor localization are still present: Our current sensing systems are sensitive to the state of the environment (e.g., non-Line of Sight conditions, fading, multipath effects, reflections, noise) and also changes in the environment (e.g., the presence or absence of people, change of furniture), the detection and adaptation to such circumstances are essential in order to maintain a high quality of localization services. Energy efficiency and low cost are further enabling factors of successful localization technologies [14]. Papers in this special issue focus on recent advances and new developments in indoor localization systems and technologies. A collection of 23 papers has been selected, which address various problems of indoor positioning, propose novel or improved methods with increased performance, and also provide insights into various aspects of quality control. The papers of this special issue can be classified into the following four research areas:  
**Hybrid Positioning:** The papers in this section provide enhanced positioning performance by combining some of the available sensing modalities. In [17], WiFi RSS is used to provide both fingerprinting and trilateration, and PDR is used to enhance the position estimation accuracy. In [18], a genetic-particle filter (GPF) algorithm is proposed to integrate PDR and geomagnetic positioning.