Scalability: At a system level, solutions for localization may require supporting requests from multiple entities. For instance, a system deployed in a shopping mall needs to be able to handle location queries from a few people, all the way up to thousands of people simultaneously. The ability to “scale up” and quickly respond to multiple location queries is of paramount importance in many indoor environments. Poor scalability can result in poor localization performance, necessitating the reengineering or duplication of systems, which can increase deployment overheads.
Integrity: The confidence that can be placed in the output of a localization solution can be termed its integrity. A solution with low integrity has a high probability that a malfunction will lead to an estimated position that differs from the required position by more than an acceptable amount and that the user will not be informed within a specified period of time about the malfunction. While regulatory bodies have studied and defined integrity performance parameters in some sectors such as civil aviation, for indoor localization it is more difficult to find well‐quantified integrity parameters. At the very least, an indoor localization solution must provide an indication of some integrity parameters that are related to safety of life, economic factors, or convenience factors; thereby allowing consumers of the solution to understand its limits and capabilities under different usage scenarios.
Cost: An indoor localization system has costs associated with it that must be as low as possible, to incentivize widespread adoption and ease deployment overheads. These costs may include installation of localization solution‐specific hardware and site survey time during the deployment period. If a positioning system can reuse an existing communication infrastructure (e.g. Wi‐Fi APs already deployed in a building), some part of the infrastructure, equipment, and bandwidth costs can be saved. In addition to the infrastructure, there may also be costs associated with the mobile devices carried by the subject being tracked. For instance, such costs could represent monetary costs of the smartphone and any externally connected hardware sensors. However, the cost could also be calculated by considering other aspects, such as lifetime, weight, and energy consumption. For example, some mobile devices, such as electronic article surveillance (EAS) tags and passive radio frequency identification (RFID) tags, are energy passive (i.e. they only respond to external fields) and thus, can have an unlimited lifetime; however other mobile devices (e.g. smartphones with rechargeable battery) have a limited lifetime of several hours without recharging.
Complexity: Indoor localization solutions inevitably require hardware and software components that can have different complexities. Solutions may differ in the sophistication required from their associated signal processing software and hardware. While some techniques may involve very simple hardware (e.g. inertial sensors) and software (e.g. to implement simple filtering techniques), other techniques may require more complex custom hardware (e.g. for specialized digital signal processing) and complex software (e.g. sophisticated machine learning techniques). Also, if the computation of the localization algorithm is performed on a centralized server, the localization can be quickly estimated due to the powerful processing capability and the sufficient power supply; however if it is carried out on a mobile device, the effects of complexity can be much more apparent. Inevitably, complexity impacts the cost of the solution, and thus it is common practice to trade off the complexity with the other (non‐cost) metrics.
37.4 Indoor Localization Signal Classification
GPS is the most popular wireless‐signal‐based positioning system in use today, and is extremely useful in outdoor environments [5]. GPS satellites broadcast microwave signals to enable GPS receivers on or near Earth's surface to determine location, velocity, and time. The GPS system itself is operated by the US Department of Defense (DoD) for use by both the military and the general public. Unfortunately, GPS signals cannot penetrate into indoor environments due to obstacles that spread and attenuate the electromagnetic radio signals [6]. Thus, GPS cannot be used for localization in indoor environments. Fortunately, there are many other signals available in indoor locales that can be leveraged by solutions intended for indoor localization. This section reviews some of the more relevant signals that can be used for indoor localization. Figure 37.1 shows a taxonomy of the signals that are covered in more detail in the rest of this section.
37.4.1 Infrared Radiation (IR) and Visible Light
Electromagnetic radiation at wavelengths within the visible range, which extends approximately between 380 and 750 nm, as well as in its lower or upper vicinity, known as ultraviolet (UV) and infrared (IR) light, are part of some of the most common indoor positioning systems that use wireless technology.
Visible light systems typically utilize general‐purpose cameras and have been adopted particularly for indoor localization of robots. One common approach is to have a robot carry a camera to capture images of the environment that can then be processed to infer location with respect to the environment [7]. Other approaches deploy cameras in fixed locations across the environment, and if the salient features of the object to be tracked appear in the field of view of the camera, the location of the object can be calculated with respect to the camera's fixed position [8]. A key challenge is how location can be estimated in a 3D world when the primary observations are 2D, from an image sensor. Depth information can be obtained by making use of the motion of a camera. In such an approach – known as synthetic stereo vision – the scene is observed sequentially from different locations by the same camera (or by multiple fixed coordinated cameras) and image depths can be estimated in a manner similar to the well‐known stereo‐vision approach. Alternatively, distances can be directly measured with additional sensors, such as with laser scanners or range imaging cameras. The latter returns a distance value for every pixel of an image at a specific frame rate.
Figure 37.1 Taxonomy of signals for indoor localization.
All visible‐light‐based localization approaches require some form of image processing, which is time consuming and can be particularly error prone in some dynamic environments, for example, due to illumination variability [8]. In the case of laser based‐solutions, only class 1 laser devices should be used, which are classified as “eye‐safe” by the IEC 60825‐1 standard [9]. Another challenge arises due to occlusions caused by dynamic elements of the environment (e.g., moving objects or people). One way of reducing occlusion is to deploy sensors with overlapping coverage areas [8]. However, clinical settings and public indoor areas such as shopping malls are often densely populated, and therefore occlusion conditions can arise frequently even with ceiling‐mounted sensors.
IR‐based localization systems are also very popular, relying on a LOS communication mode between the transmitter and receiver. For instance, [10] presents an IR‐based localization system for museums with IR emitters installed in the ceiling of the door frames of every room. Each emitter transmits a unique ID using the Infrared Data Association (IrDA) protocol. Visitors carry a personal digital assistant (PDA) with an