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A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier st...
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Context 1
... of the radio environment were carried out over a period of one month in 5 of 8 rooms of a second-floor laboratory in central Paris, France, using the TELIT GM-862 modem [12]. The laboratory layout and the points where measurements were taken are indicated in fig. ...
Context 2
... the BSIC (Base Station Identity Code) is also returned when possible. In our data, 534 different carriers were detected, of which 234 were beacons. Carriers are detected only if their power is above a threshold of -108 dBm. Our database consists of 601 measurements, with an equal number of measurements in each of the 5 rooms indicated in the fig. 2. For this study, the measuring device was always placed at the same position in each room, indicated by the star symbols in the figure. Furthermore, to simplify the analysis, only the RXLEV values, and not the BSIC codes, were used as inputs to the localization system. ...
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Citations
... To achieve the desired goal of an IPS, two major predictors must be considered: (i) the type of signal features used to establish the fingerprint, such as received signal strength (RSS) [12], CSI [13], time of arrival (TOA) [14], time difference of arrival (TDOA) [15], angle of arrival (AOA) [16], and so on; and (ii) the underlying network technology that generates the signal features. Despite the fact that each technology and signal feature has its own trade-off that limits the scalability of its implementation, over the last two decades, various wireless positioning technologies, including but not limited to cellular networks (GSM) [17], WLAN-Wi-Fi [18], Bluetooth [19], RFID [20], ultra-wideband (UWB) [21], ZigBee [22], inertial navigation systems [23], geomagnetic [24], visible light communication (VLC) [25], etc. have been proposed and investigated. Regardless of the complexity of the indoor environment, the rapidly growing commercial interest in indoor locationbased services (ILBSs) still dictates an effective approach for positioning systems that considers both the cost and practicality of their implementation. ...
Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.
... Several works presented localisation system based on Global System for Mobile Communications (GSM) [2,3,1,10]. To sum up their results, it can be said that a horizontal localisation of a mobile phone can be done with relatively small error of a few meters. ...
One of challenging problems of indoor localisation based on GSM fingerprints is the detection of the current floor. We propose an off–line algorithm that labels fingerprints with the number of current floor. The algorithm uses one pass through the given route to learn the GSM fingerprints. After that the height on the testing passes of the same route can be estimated with high accuracy even for measures registered with various velocities and a month after the learning process. The two phase algorithm detects the points of a potential floor change. Next, the regression function normalises height of the change and calculates its direction. The obtained results are up to 40 % better than the results obtained by the pure regression.
... One of aspects of smart buildings is a indoor localisation system that allows the managers to localise people or objects in the building. Most popular localisation methods use Wi-Fi signal strengths [13,17,12,16] and cellular networks strengths [3,4,1,14]. ...
As a part of smart-buildings, indoor localisation systems – alternative to Global Positioning System localisation – bring constantly improving results. Several localisation methods works with a horizontal localisation error less than few meters. However, for small suburban houses, horizontal localisation is not as important as detection of the current floor, which in is still a challenge in multi-storey buildings. This paper compares several approaches that can be used in fingerprinting-based floor detection systems. The tests include the following fingerprints: pressure measures, Wi-Fi signals, and two generations of cellular networks signals. The tests have been done in the suburban 3-storey building with underdeveloped Wi-Fi and cellular infrastructure. Notwithstanding, the floor detection based on Received Signal Strength from both infrastructures reached from 98 to 100 %. Additionally, we showed that differences in the number of measures and differences in the number of received signals were not a major factor that influenced on accuracy.
... The problem of choosing a subset of relevant GSM carriers providing a good distinction between rooms is also examined in [25]. Measurements were also carried out over one month in 5 of 8 rooms of a 2nd-floor office, using a TELIT GM-862 modem that detected 534 different carriers. ...
Among the accurate indoor localisation systems that are using WiFi, Bluetooth, or infrared technologies, the ones that are based on the GSM rely on a stable external infrastructure that can be used even in an emergency. This paper presents an accurate GSM indoor localisation system that achieves a median error of 4.39 metres in horizontal coordinates and up to 64 percent accuracy in floor prediction (for 84 percent of cases the floor prediction is mistaken by not more than a single floor). The test and reference measurements were made inside a six-floor academic building, with an irregular shape, whose dimensions are around 50 metres by 70 metres. The localisation algorithm uses GSM signal readings from the 7 strongest cells available in the GSM standard (or fewer, if fewer than 7 are available). We estimate the location by a three-step method. Firstly, we propose a point localisation solution (i.e., localisation based on only one measurement). Then, by applying the central tendency filters and the Multilayer Perceptron, we build a localisation system that uses a sequence of estimations of current and past locations. We also discuss major accuracy factors such as the number of observed signals or the types of spaces in the building.
... In addition to such short-range signals, indoor localization based on fingerprints from wide-area radiotelephone networks, such as GSM and CDMA, have also been proposed [8][9][10][11][12][13]. The full coverage, near-ubiquity and relative stability of GSM networks may provide an attractive alternative for indoor localization. ...
... Recent results have suggested that accurate and efficient indoor localization can be achieved using RSS information acquired from very large numbers of GSM channels [10][11][12][13]. Those studies, however, did not represent a genuinely practical solution since the RSS scanning devices operated without user intervention only at a small number of representative points within each room. ...
Accurately localizing users in indoor environments remains an important and challenging task. The article presents new results on room-level indoor localization, using cellular Received Signal Strength fingerprints collected with a standard cellular handset programmed to perform fast scans of the 900 and 1800 Megahertz GSM bands as a user explores an indoor environment at a normal walking pace. Support Vector Machines are used to deal with the high dimensionality of the fingerprints. The study demonstrates that an appropriately programmed standard cellular handset can provide a simple, inexpensive solution for accurate room-level indoor localization.
... Solutions exploiting RSS measurements from radiotelephone networks such as GSM and CDMA, both for indoor and outdoor localization, have also been discussed in the literature1011121314. The near-ubiquity of cellular telephone networks allows in this case to imagine systems for which the required network infrastructure and maintenance are assured from the start, and recent experimental results151617 have furthermore suggested that efficient indoor localization may be achievable in a home environment using RSS measurements in the GSM band. The main contribution of the present article is to demonstrate conclusively that GSM can indeed provide an attractive alternative to WiFibased and other techniques for indoor localization, as long as the GSM RSS vectors used are allowed to include the entire GSM band. ...
A new approach to indoor localization is presented, based upon the use of Received Signal Strength (RSS) fingerprints containing data from very large numbers of cellular base stations--up to the entire GSM band of over 500 channels. Machine learning techniques are employed to extract good quality location information from these high-dimensionality input vectors. Experimental results in a domestic and an office setting are presented, in which data were accumulated over a 1-month period in order to assure time robustness. Room-level classification efficiencies approaching 100% were obtained, using Support Vector Machines in one-versus-one and one-versus-all configurations. Promising results using semi-supervised learning techniques, in which only a fraction of the training data is required to have a room label, are also presented. While indoor RSS localization using WiFi, as well as some rather mediocre results with low-carrier count GSM fingerprints, have been discussed elsewhere, this is to our knowledge the first study to demonstrate that good quality indoor localization information can be obtained, in diverse settings, by applying a machine learning strategy to RSS vectors that contain the entire GSM band.
Localization need is appeared with the evolution of mobile communication devices. Nowadays, smart phones are equipped with many sensors that allow position estimation. Based on localisation, many applications and values added services are developed to meet the increasing users need. In order to design localization system, it requires first to understand the fundamental localization principles and to identify existing methods. A compromise between cost, complexity and performance should be taken into account while choosing the best solution. This paper makes benchmark of existing solutions for RF indoor localization with a detailed presentation of performance and limitations related to the most promising solutions. It presents also the latest researches and stimulates new ways to develop new research in this field in particular for fingperpinting based solution and CSI used metric for WLAN network.
