hopping to avoid interference. Apple Inc. implements this technology to its trademark technology iBeacon which derive proximities between the beacon and receiver from relative signal strength indicator (RSSI) (Mubaloo Ltd, 2014).
While this thesis cannot cover all the applications that are available in both research or on the market, this short introduction gives an idea of how many different technologies and applications can be applied for indoor positioning. A reason to why so many technologies can be found is that there is no single solution that could solve the problem in every indoor positioning scenario. There are both pros and cons to applying each different signal and method. However, users constantly look for systems that could adapt easily in different environments and provide seamless positioning even when situations change. Therefore, recent works start looking at how different methods could be integrated to achieve better positioning in various different environments.
2.2
Collaborative positioning
2.2.1
Basic concepts
The complexity of indoor positioning comes from the fact that, unlike outdoors, the indoor environment are very different from each other in terms of available signals. The previous section provides a background knowledge on sensors and systems that can be used in different indoor po- sitioning situations independently. While GNSS is able to provide accurate positioning in all weather and all year round in outdoor open areas, it is almost impossible to use GNSS in any indoor environment. With signal power as low as -150dBw, its weak signal makes it very hard to penetrate not just buildings walls but foliage as well, which is why forests are also considered as “indoor positioning” problems (Borre, 2007; Petovello and Joseph, 2010). While so many indoor positioning techniques have been proposed, each technique relies on different signals which are suitable in different environments. Therefore, different indoor positioning methods must be tailored to suit the specific conditions of an indoor environment.
While Wi-Fi fingerprinting provides absolute positioning results, wire- less signals naturally fluctuate and signal strength are easily disturbed by interference, obstruction and environmental factors which makes its positioning accuracy unstable (Tarrio et al., 2011; Fahed and Liu, 2013; Luo et al., 2013). Inertial navigation can achieve reliable relative position- ing based on consecutive inertial measurements which works in almost
any environment. However, the major disadvantage is that heading drift accumulates very quickly and must be constrained by some kind of other measurement.
Another problem that often occurs in indoor positioning is the biased measurements reaching the receiver simply caused by the disturbance at the location of the receiver. While the measurements obtained by a single user could be restricted by its location, multiple measurements from a number of users could eliminate some the error and bias.
The idea of collaborative positioning (CP) is introduced here which integrates a selection of different sensors and information from different users to minimise individual system limitations and enhance overall po- sitioning performance. CP enable users to share and utilise the location information among its surroundings and neighbours over communication links. It initially extends the positioning network boundary as it implements signals and data that cannot be acquired directly to assist the determina- tion of positioning solutions that would not have been possible otherwise. Further work on CP also suggests that it is able to increase positioning and navigation accuracy and robustness (Patwari et al., 2005; Chan et al., 2006; Alsindi and Pahlavan, 2008; Thompson and Buehrer, 2012; Nilsson et al., 2013, 2014). CP benefits from opportunistic navigation which takes advantage of any environmental features and measurements available to the system, e.g. broadcasting signals, mobile signals, visual landmarks, magnetic anomalies, light, sound, temperature, etc (Groves et al., 2014). The concept of signals of opportunity (SOOP) has been introduced as part of opportunistic navigation in (Yang et al., 2009) which utilises available signals that were not originally intended for positioning. The collaboration of signals is enhanced through multiple users within the CP network that can share data amongst each other. This data can be information of the surrounding environment, clock data, mapping information or relative ranging measurements (Groves, 2013a,b).
Positioning based on collaboration of nodes (users and transmitters) within a network is fairly new among all methods of positioning and navigation. This is mainly because the concept of collaboration between nodes among the network relying on direct communication between each node rather than an infrastructure has only been introduced in recent years (Aspnes et al., 2006). CP only started emerging since then. Collaborative positioning was first applied in intelligent transport systems where roadside beacons and vehicle clusters helped to maintain reliable positioning when
2.2. Collaborative positioning
the vehicle could not receive sufficient satellite signals (Alam et al., 2011; Yao et al., 2011; Tang et al., 2012; Amini et al., 2014; Tsai et al., 2014). CP improves navigation performance through correcting GNSS observations and positioning errors are reduced by vehicle-to-vehicle ranging.
This thesis mainly discusses collaborative positioning from two aspects: integration of multi-sensors to provide positioning for a single system and integration of multi-users to enhance the positioning accuracy among the whole network. Multi-sensor systems have been discussed in literature as it is considered as the future trend to provide robust ubiquitous positioning (Hide et al., 2007; Groves, 2014). However, the characteristics of a multi- user system is still relative new and lacks comprehensive understanding.
As ranging measurements between the nodes within the network is an important piece of information in collaborative positioning, it is also re- ferred as peer-to-peer (P2P) positioning in some literature (Groves, 2013a; Garello, Presti, Corazza and Samson, 2012). However, because the more broader aspect of CP discussed in this thesis, P2P will only be used when referring to the relative ranging scenarios here.
2.2.2
Network optimisation
The next generation of CP aims to bring together a range of different sensors and environmental information to provide more robust solution which potentially overcomes interference and enables seamless navigation when moving between indoor and outdoor environments. To achieve such solutions, appropriate information should be selected for integration so that the system has enough measurements while not been burdened with too much information. Moreover, not all information is essential to improving positioning performance. Yang and Soloviev (2014) have investigated the spatial and temporal effects of collaborative positioning and find that there is an equalising point which marks out the number of users when the inclusion of more measurements begin to improve performance. The optimisation of collaborative network performance is also explored among various works based on geometric positions and lower bound estimations (Jia and Buehrer, 2010; Lei, 2014).
In this thesis, we look at the critical point where CP performance improvement begins to reduce when increasing measurements are being included. We try to find the balance point where enough information is integrated to achieve accurate positioning while also taking care not to
reduce efficiency.