Indoor Positioning System

Discuss about the Information Technology for Indoor Positioning.

In this research it is about the extent of the reliability of the multiple sensors fusion for the indoor positioning. The positioning systems are used in locating numerous objects in the world. One of the location objects is the GPS.  GPS is the system of the satellites radio transmitter which orbit on earth in great numbers; the main objective is to pinpoint the precise place to people or even virtually any the vessel which are built with a receiver transmitter which is within a small radius (Paul & Sato, 2017). It is therefore important to find an accurate as well as reliable system for the indoor positioning system.

Indoor Positioning System has become the major sources to the accurate location information in the world (Paul & Sato, 2017). Having reliable as well as accurate location information could assist in the emergence of the services, recreation, tracking, navigation as well as networking (Chen & Vadde, 2012). Several indoor positioning approaches have been suggested such as, radio frequency, fingerprinting approaches, motion sensor based pedestrian dead reckoning approaches and visual sensor (Chen & Vadde, 2012). Due to this aspect there are numerous supplementary techniques which have been utilized such as Bluetooth, cellular, wireless internet as well as Radio Frequency ID to provide positioning to the indoor setting where the GPS do not function (Galván-Tejada, Carrasco-Jimenez & Brena, 2013). Reliability specifies how often the system could attain the accuracy which is the range across multiple locations (Jiaxing, 2017).  As the positioning is the continuous real world process, the claimed accuracy is not all the time consistent to the actual accuracy at any given location or time. For instance, the performance of the performance of the consumer Grade GPS Receivers it could depends on the canopy cover as well as the availability that results to the variance in accuracy when it is in different setting (Jiaxing, 2017). According to the Skyhooks hybrid positioning system that combines the GPS, cellular signals  as well as WiFi highlights that the core engine has an accuracy of ten meters and compare this kind of accuracy level to the GPS and A-GPS at ten and thirty meters.

Reliability specifies how often the system could attain the accuracy which is the range across multiple locations (Jiaxing, 2017).  As the positioning is the continuous real world process, the claimed accuracy is not all the time consistent to the actual accuracy at any given location or time. For instance, the performance of the performance of the consumer Grade GPS Receivers it could depends on the canopy cover as well as the availability that results to the variance in accuracy when it is in different setting (Jiaxing, 2017). According to the Skyhooks hybrid positioning system that combines the GPS, cellular signals  as well as WiFi highlights that the core engine has an accuracy of ten meters and compare this kind of accuracy level to the GPS and A-GPS at ten and thirty meters.

Global Positioning System

 There is also aspect of accuracy which would be discussed in the research. Accuracy is regarded as the localization error distance. This is the distance which is between the actual and the calculated position (Li, Zhao, Ding, Gong, Liu & Zhao, 2012).  The accuracy of the positioning system could vary depending to the localization technique and algorithm which is used. When choosing the implementing these techniques as well as algorithms, average of the coverage, cost of implementation and the calculation accuracy should be balanced. To most of the positioning technique, any increase in the accuracy is related to the additional power of processing, equipment that is required or the increased system latency (Khoshelham & Zlatanova, 2016).

This research will discuss aspects related indoor positioning technique and algorithms which are involved in localization. Some of these techniques usually involve various signal system such as cellular, and Bluetooth. The most common indoor positioning systems rely to the Wireless Internet signals from the routers (WiFi). While different algorithms utilizes signals in calculating position in different ways to each of them have strengths, and unique variable which impact on the accuracy (Li, Zhao, Ding, Gong, Liu & Zhao, 2012). For this research it would focus on the Blue tooth, Wi-Fi and the Magnetic fingerprints.  First it is important to define various definitions to which are the main point of this paper these are reliability, and accuracy. To undertake this research has been motivated by the interest in the research on the indoor positioning technology. This field is diverse and not many individuals are away on some of these technologies despite them used in every day applications.

This research is of importance since it would add the knowledge based on the current indoor positioning techniques. Moreover, it would discuss on the issues which are related to the localization techniques. The significance to find accurate and more reliable systems for the indoor positioning system has been emerging from the shortage of the information, thus the research will help lessen on the gap and provide comprehensive information in relation to this field. 

A review to the existing literature is carried out to support on the research work which is undertaken in this research topic. The review is about the existed research papers which would examine on the current study to understand the aspect missing which would be filled by this research. The literature is on different aspects within the research. 

Literature Review

There are numerous researches that have been done on the GPS. GPS is the global navigation satellite system to which decides the position to any target through measuring the propagation holdup from the signals from satellites to GPS receiver (Xiao, Ni & Toh, 2011). In the recent past, researchers have tested on wide array of systems to make an effort to develop on the precise GPS signals to numerous applications that are under numerous traffic conditions. Lufeng Zhu et al (2011) did an analysis to acknowledge on the need to have more effective GPS data acquisition than the localized data collection that was generated from the traditional loop detectors. In this study they analyzed the conventional fast acquisition, and fast acquisition of GPS receiver aided and was offered INS information and signal was caught by spectrum zooming. On another research Kazuyuki & Ka Cheok (1998) they performed on the fuzzy logic Kalman filters sensor fusion technique. Within this study it was examined based on the theoretical background for the sensor fusion depending on the Kalman filtering and fuzzy logic scheme (Le Grand & Thrun, 2012). Validity to the technique was confirmed utilizing experimental data from a real automobile navigation around the urban areas. The leads to this pointed out approach to the automobile might be traced with high accuracy as well as repeatability despite limitations to GPS. However, this research has not combined on the Acquisition and tracking method to the GPS and the LIC signals to produce the accurate positioning system and through this research it would help fill that gap.

The wireless indoor positioning system are program used to locate the objects or even individuals who are inside the building through use of radio waves , acoustic signals , magnetic fields or maybe other sensory information that is gathered by the mobile devices (Chen & Vadde, 2012). According to Bahl (2000) in their approach to indoor positioning system he used WLAN signals to determine on the distance between the mobile device and the WLAN routers. This concept for the mobile device location is similar the positioning module which was used in the positioning algorithms (Khoshelham & Zlatanova, 2016). However, this method lacks the hybrid framework which measures on the signal strength to the WiFi to position on the location to the mobile device (Jiaxing, 2017). This research would help address on this issue through suggestion since hybrid requires wireless connections to work while the system is only making the usage of the Bluetooth connection to enhance on the precision.  Lassabe (2006) has helped to present an iterative technique to address on the issue of trilateration.   

There are many researchers who have used RSSI for the localization and they have discovered on some techniques to deal with such kind of issues (Kubrak, Le Gland, He & Oster, 2009). Mao, Fidan & Anderson, (2007) proposes some of the models which are used in optimizing the RSSI value such as statistical model, correction and the Gaussian According to Wu et.al on their research proposed on the probabilistic localization technique (Jiaxing, 2017). It highlighted on this paper RSSI is impacted by the hardware orientation and other factors. RSSI when it is used to create the location signatures are unreliable when it comes to multiple devices from the various vendors and physical constraints which are used. According to Kubrak, Le Gland, He & Oster (2009) to their research discusses that RSSI based on the localization is based on the loss model to free on the space propagation and logarithmic path loss to radio signal. The research however, does not address how the RSSI errors are measured.

According to the related work on this aspect there are authors who introduced RADAR system which relies on the Wi-Fi analysis. Xiao, Ni & Toh (2011) approach in their research has attracted a lot of effort due to the properties such as using current infrastructure and resilience to the multipath impacts compared to the traditional approaches (Galván-Tejada, Carrasco-Jimenez & Brena, 2013). There are many researches who have demonstrated  the positioning performance on the Wi-Fi suffers from the problem such as the fast fading  as a result to the interferences in case the characteristics to the radio wave propagation is utilized in locating the mobile device (Wang, Zhao, Luo & Lu, 2011). 

Different research has been done on this aspect. Kwiecie?, Ma?kowski, Kojder & Manczyk (2015) research on reliability of the Bluetooth technology for the indoor localization system on the overview of the technologies used for the localization (Röbesaat, Zhang, Abdelaal & Theel, 2017). They focused on the localization techniques to the cellular networks and the WLAN environments.  Nonetheless the research does not describe signal processing technique useful in the localization algorithm (Xie, Gu, Tao, Ye & Lu, 2016). This can be synethesized byresearch done by Mao, Fidan & Anderson 2007) and this has contributed extensively on this aspect. The body of literature will help provide comprehensive literature to the research gap.

The research has been done extensively by Yuntian, Tao & Andong (2006), on integrating magnetic Field for the fingerprinting based on the indoor positioning system in this subject. The authors have provide comprehensive research with this subject by emphasizing that magnetic field data as the fingerprints to the Smartphone the indoor positioning could become popular to the modern times ( Mao , Fidan & Anderson , 2007 ) . The filters could be utilized to develop on the accuracy. Moreover, they found out that existing particle filters depending on the methods might heavily be afflicted with the motion estimation errors which might result to the unreliable system which impose strong restrictions to the Smartphone. Bolat & Akcakoca ( 2017 ) research on hybrid indoor positioning depending on Magnetic field have also highlight that it could be used to create a fingerprint maps by using the field sensors (Paul & Sato, 2017). However, the technique has some drawback since a particular location could have similar distortions which are far away from the current location.

On the related literature in the hybrid system is the work done by Bolat & Akcakoca (2017) discusses how the hybrid system arises from the embedded control when the digital controllers, and the subsystems which is modeled as the finite state machine. The authors have combined numerous methods to the localization algorithms that are dependent on the hop count depending on the data between anchor nodes and the sensors (Liono, Qin & Salim, 2016). There is also work done by Li et. al (2012) on sensor stream in multi- activity recognition discusses the aspect of hybrid system in reliability point of view, but this research does not examine how it enhances on the accuracy to identity the distance between intermediate nodes. 

There are numerous literatures on the sensor fusion; one related work is that of Röbesaat, Zhang, Abdelaal & Theel (2017) on improving indoor localization. The authors have highlighted that to improve on the accuracy to pose estimation, there are numerous sensors which are available to the mobile robots which could measure variables which are associated to the motion are used to localize on the robots (Röbesaat, Zhang, Abdelaal & Theel, 2017).  The different measurement are usually combined in with an algorithm in order to take into the account various accuracy and level of the noise to each sensor. Varshney, Goel & Qadeer, (2016) research proposed that the most employed fusion approach is Kalman Filter or one of the variants for the nonlinear system. The authors have also highlighted that there are several illustration of KF that depend on the localization algorithms developed to different variation of mobile robots and sensors. Nonetheless, these literatures lack information on how to use the complex fusion schemes to the mobile robots and how it can enhance on this technology (Varshney, Goel & Qadeer, (2016). According to  King, Kopf & Effelsberg (2005), they recently published on the location system add as the third part to the sensor fusion which could combine on the position estimates which are obtained from the numerous locations determination algorithms as well as different sensors. This idea has seen results which are generated by the sensor fusion algorithm to be more precise than the position estimate which are provided one kind of the sensor (Khoshelham & Zlatanova, 2016). This has been an improvement to the past research particular where there was position estimate where there was use of one sensor (Khoshelham & Zlatanova, 2016). In this research literature, it would help highlights how sensor fusion has been a promising technique in solving the aspect of the position estimate conflict in which the authors in that research did not address. 

References

Chen, K., & Vadde, K. R. (2012). Design and evaluation of an indoor positioning system framework. UC Berkeley course project for CS262A. 

Galván-Tejada, C. E., Carrasco-Jimenez, J. C., & Brena, R. (2013). Location identification using a magnetic-field-based FFT signature. Procedia Computer Science, 19, 533-539. 

Jiaxing, L. (2017). The Design and Implementation of Indoor Localization System Using Magnetic Field Based on Smartphone. International Archives of the Photogrammetry,Remote Sensing & Spatial Information Sciences, 42. 

Khoshelham, K., & Zlatanova, S. (2016). Sensors for indoor mapping and navigation. 

King, T., Kopf, S., & Effelsberg, W. (2005). A Location System based on Sensor Fusion: Research Areas and Software Architecture. Informatik-Berichte, 324, 28-32. 

Kubrak, D., Le Gland, F., He, L., & Oster, Y. (2009, September). Multi?sensor fusion for localization. Concept and simulation results. In Proceedings of the 2009 ION Conference on Global Navigation Satellite Systems, Savannah 2009 (pp. 767-777). 

Le Grand, E., & Thrun, S. (2012, September). 3-axis magnetic field mapping and fusion for indoor localization. In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012 IEEE Conference on (pp. 358-364). IEEE. 

Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012, September). A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 421-430). ACM. 

Liono, J., Qin, A. K., & Salim, F. D. (2016, November). Optimal time window for temporal segmentation of sensor streams in multi-activity recognition. In Proceedings of the 13th   International Conference on Mobile and Ubiquitous Systems: Computing, Networking  and Services (pp. 10-19). ACM. 

Mao, G., Fidan, B., & Anderson, B. D. (2007). Wireless sensor network localization techniques.   Computer networks, 51(10), 2529-2553. 

Paul, A. K., & Sato, T. (2017). Localization in Wireless Sensor Networks: A Survey on  Algorithms, Measurement Techniques, Applications and Challenges. Journal of Sensor and Actuator Networks, 6(4), 24. 

Röbesaat, J., Zhang, P., Abdelaal, M., & Theel, O. (2017). An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study. Sensors, 17(5), 951. 

Varshney, V., Goel, R. K., & Qadeer, M. A. (2016, July). Indoor positioning system using Wi-Fi & Bluetooth Low Energy technology. In Wireless and Optical Communications Networks  (WOCN), 2016 Thirteenth International Conference on (pp. 1-6). IEEE. 

Wang, R., Zhao, F., Luo, H., Lu, B. and Lu, T., (2011), September. Fusion of wi-fi and bluetooth for indoor localization. In Proceedings of the 1st international workshop on Mobile  location-based service (pp. 63-66). ACM. 

Xiao, W., Ni, W., & Toh, Y. K. (2011, September). Integrated Wi-Fi fingerprinting and inertial sensing for indoor positioning. In Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on (pp. 1-6). IEEE. 

Xie, H., Gu, T., Tao, X., Ye, H., & Lu, J. (2016). A reliability-augmented particle filter for magnetic fingerprinting based indoor localization on smartphone. IEEE Transactions on Mobile Computing, 15(8), 1877-1892.