When used within their associated limitations, nonlinear estimation algorithms hold enormous promise for addressing the most difficult navigation problems.
References
1 1 Kalman, R.E. (1960) A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering, 82 (Series D), 35–45.
2 2 Maybeck, P.S. (1979) Stochastic Models, Estimation, and Control, Vol. I, Academic Press, Inc., Orlando, Florida 32887.
3 3 Maybeck, P.S. (1979) Stochastic Models, Estimation, and Control, Vol II, Academic Press, Inc., Orlando, Florida 32887.
4 4 Gordon, N.J., Salmond, D.J., and Smith, A.F. (1993) Novel approach to nonlinear/non‐Gaussian Bayesian state estimation, in IEE Proceedings F‐Radar and Signal Processing, vol. 140, IET, vol. 140, pp. 107–113.
5 5 Doucet, A., De Freitas, N., and Gordon, N. (2001) Sequential Monte Carlo Methods in Practice. Series Statistics for Engineering and Information Science.
6 6 Ristic, B., Arulampalam, S., and Gordon, N. (2004) Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House.
7 7 Sheldon, S. and Maybeck, P. (1990) An optimizing design strategy for multiple model adaptive estimation and control, in Decision and Control, 1990, Proceedings of the 29th IEEE Conference on, pp. 3522–3527, Vol. 6.
8 8 Bar‐Shalom, Y., Li, X.R., and Kirubarajan, T. (2004) Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software, John Wiley & Sons.
9 9 Doucet, A., de Freitas, N., Murphy, K., and Russell, S. (2000) Rao‐Blackwellised particle filtering for dynamic Bayesian networks, in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, California, UAI’00, pp. 176–183. URL http://dl.acm.org/citation.cfm?id=2073946.2073968.
10 10 Mustiere, F., Bolic, M., and Bouchard, M. (2006) Rao‐Blackwellised particle filters: Examples of applications, in Electrical and Computer Engineering, 2006. CCECE ‘06. Canadian Conference on, pp. 1196–1200.
11 11 Papoulis, A. and Pillai, S.U. (2002) Probability, Random Variables and Stochastic Processes, McGraw‐Hill, New York.
37 Overview of Indoor Navigation Techniques
Sudeep Pasricha
Colorado State University, United States
37.1 Introduction
While localization and navigation in outdoor environments can make use of global navigation satellite systems (GNSSs) such as Global Positioning System (GPS), this is not a viable solution for indoor localization because signals from GPS satellites are too weak to penetrate through buildings, obstacles, and into underground environments. Consequently, precise localization inside a closed structure, such as shopping malls, hospitals, airports, subways, and university campus buildings, require the use of alternative localization technologies. But indoor environments present unique challenges, particularly due to a diverse array of obstacles such as walls, doors, furniture, electronic equipment, and stationary or moving humans, all of which give rise to multipath effects in wireless signals due to signal reflection, attenuation, and noise interference. As a result, accurate indoor localization with wireless communication signals is a very complex problem. Moreover, indoor locales also require much higher levels of accuracy than outdoor environments; for example, while a 4–6 m accuracy is acceptable outdoors for vehicle navigation, it may not be acceptable for localization in many indoor contexts, where 4–6 m may be the difference between one room and the next.
Enabling location services for indoor locales has many potential applications. Buildings with awareness of the location of occupants can use this knowledge to optimize heating, lighting, and other resources toward saving energy costs. In emergency scenarios such as earthquakes and hurricanes, location services can allow emergency responders to determine where people are located at any time, potentially expediting evacuations as well as search and rescue efforts. Location awareness can be used as a backbone for smarter workplaces by allowing telephone calls to be routed to the nearest device in the proximity of a person, allowing colleagues to find each other, and helping guests navigate new buildings to reach their desired location. Services that utilize indoor location systems can also enable smart dynamic locking of sensitive rooms and resources if an owner is not present, to improve overall safety. Ubiquitous localization already plays a central role in social networking, for instance, to locate friends for coordinating joint activities or check into restaurants and other indoor locales via various smartphone apps, and is expected to play an even bigger role in the future. Indoor position awareness is also an essential component of industrial applications, such as for robot motion guidance, robot cooperation, and smart factories (e.g. the ability to find tagged maintenance tools and equipment scattered all over a plant in production facilities). Localization for cargo management systems at airports, ports, and for rail traffic enables unprecedented opportunities for increasing their efficiency.
Many different techniques have been proposed to enable indoor localization and navigation. The interest in indoor navigation systems is peaking because the crucial sensors necessary for localization have become sufficiently small and inexpensive to enable practical tracking of individuals (who must carry them at all times). A prime example of this is the inertial sensors that are part of inertial measurement units (IMUs) found in smartphones that can aid with localization. However, activity trackers, smart cards, and various types of wearable sensors can also play a crucial role to enable indoor navigation. The challenge today is to exploit these available sensors to achieve indoor tracking with acceptable robustness levels, similar to that demonstrated by GNSS in outdoor locales.
This chapter provides an overview of the state of the‐art in the area of indoor localization and navigation. One can consider localization as an instantaneous process, providing the location of a user or object being tracked at a specific instance of time. In contrast, navigation can be considered as a form of continuous localization, where location estimates must be provided frequently and periodically over time to help a user while they navigate an indoor environment. Tracking can be considered to be similar to navigation, except that the location estimates of the mobile subject are provided not to the user but to some third party that is interested in the location information. For the sake of clarity and brevity, we will mainly use the term localization in the rest of the chapter to represent both instantaneous point estimates and continuous estimates (navigation or tracking), when discussing components and solutions that are relevant for indoor location estimation. We will use the term navigation sparingly, when necessary to discuss the unique aspects of continuous estimation of location.
The rest of the chapter is organized as follows. Section 37.2 discusses performance metrics that are necessary to understand, in order to compare and contrast the landscape of indoor localization approaches. Section 37.3 provides an easy reference to the key technical terms that are used throughout the rest of the chapter. Section 37.4 presents a review of the various signals that can be used to provide tracking in indoor locales, for the purpose of localization. Section 37.5 provides an overview of the vast landscape of solutions for indoor localization. Lastly, Section 37.6 discusses open research issues and challenges that still remain to be overcome for viable