An Overview of Indoor Localization Techniques

Abstract

The need for indoor localization systems that can provide reliable access to location information in areas that are not serviced sufficiently by GPS has continued to grow. There are a wide variety of use cases for this localization data and increasing interest from industry, academia, and government agencies that has fueled research in this area. On the individual front, smartphones have permeated into every aspect of our lives and are a unique category of embedded platform packed with a wide variety of sensors. In this chapter, we explore the capabilities of these sensors with respect to indoor localization techniques and frameworks that combine two or more of these techniques. Many localization frameworks utilize a combination of complimentary methods to enhance accuracy and reliability and to decrease energy consumption of the overall system. Several of these localization frameworks are also explored. We lay special emphasis on the challenges associated with fingerprinting-based indoor localization frameworks that employ machine learning to achieve state-of-the-art performance. Lastly, we describe the major challenges that are being faced in current research on indoor localization with smartphones, as they are critical for charting the path for future advances in indoor localization.