In this work a real-time indoor localisation system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy: i.e., the environment of the object that is being tracked and an adjustable maximum speed. The developed algorithm was verified by simulations and with experiments in a building-wide testbed for sensor and WiFi experiments. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by respectively 26.4% and 63.9%

Joseph, Wout

Martens, Luc

Plets, David

Trogh, Jens

English

Ghent University Academic Bibliography

Advanced Indoor Localisation Based on the ViterbiAlgorithm and Semantic DataJens Trogh∗, David Plets∗, Luc Martens∗ and Wout Joseph∗∗ Department of Information Technology, Ghent University/iMinds, Belgium, jens.trogh@intec.ugent.beAbstract—In this work a real-time indoor localisation systembased on the Viterbi algorithm is developed. This Viterbi prin-ciple is used in combination with semantic data to improve theaccuracy: i.e., the environment of the object that is being trackedand an adjustable maximum speed. The developed algorithmwas verified by simulations and with experiments in a building-wide testbed for sensor and WiFi experiments. Compared to areference algorithm without Viterbi or semantic data, the resultsindicated a significant improvement: the mean accuracy andstandard deviation improved by respectively 26.4% and 63.9%.Index Terms—Localisation, Viterbi Algorithm, Semantic Data,Wireless Networks, Indoor EnvironmentI. INTRODUCTIONIndoor localisation systems have applications in many do-mains, think of the healthcare sector, agricultural sector, indus-trial sector, cultural sector, etc. Examples of these applicationsare: tracking of elderly, monitoring of animals, equipmenttracking and museum guidance. To locate an object, most lo-calisation systems use a mobile node and a fixed infrastructure,which consists of static nodes. These static nodes are con-nected and form the wireless network. The mobile and fixednodes exchange signals and the characteristics of these signalsare used to estimate the position of the mobile node. Dueto the complexity of many indoor environments, localisationsystems are often not sufficiently accurate. Current state-of-the-art localisation systems try to improve the accuracy byusing new technologies like Ultra Wideband (UWB) [1]. Thevery large bandwidth enables highly accurate localisation butthe typically smaller ranging coverage makes it more suitedfor short-range applications. Other state-of-the-art systems relyon user interaction or route prior knowledge but this is notalways wanted or even possible [2]. In this paper, an advancedindoor localisation algorithm for tracking a person in real-time through a building, is presented. To avoid expensivehardware costs or a time-consuming measurement campaign,the existing WiFi or ZigBee infrastructure and an advancednetwork planner are used.II. METHODOLOGYA. Localisation algorithmThe localisation algorithm is based on the Viterbi algo-rithm [3]. This dynamic programming algorithm is used todetermine the most likely sequence of hidden states, calledthe Viterbi path, resulting in the sequence of observed events.To apply this technique on a localisation algorithm, the stateshave to be interpreted as real locations on a floor plan. Then,this principle comes down to determining the most likelysequence of positions instead of only the most likely currentposition. All possible trajectories are kept in memory and eachtrajectory has an associated cost. This cost is the sum of MeanSquare Errors (MSE) between measurements and referencevalues (see Section II-C) and is used as decision metric.To apply the Viterbi principle in a useful manner (improvethe accuracy), we have to restrict the number of allowedtransitions between two consecutive locations. Therefore, weuse semantic data: the environment of the object that is beingtracked and an adjustable maximum speed. In this way it isassured that no walls are crossed (doors are used to leavea room) and no unrealistically large distances are crossedwithin a given time frame. Overall, this leads to realistic andphysically possible trajectories. To the best of the author’sknowledge this is the first localisation algorithm that uses thiscombination of techniques.B. Start positionBecause the most likely sequence of positions is determined,the developed localisation algorithm is sensitive to a wrongstart position. One could start off in the wrong room, whichimplies a certain recovery time before predictions can beaccurate again, because walls cannot be crossed. To counteractthis, additional start positions are added as soon as the trackingbegins. These additional start positions lie on circles aroundthe best initial prediction. In this way the algorithm can easilycorrect itself by switching to another trajectory when newmeasurements suggest being located inside a different room.By using this technique also the previous positions will be setright.C. RSSI fingerprinting using heuristic indoor network plannerThe developed localisation algorithm relies on a ReceivedSignal Strength Indicator (RSSI) fingerprinting technique toestimate the most likely position by comparing the measure-ments with reference values from a radio map. This radiomap (also known as fingerprint database) contains the pathlosses to all fixed nodes, for each possible position (gridpoint) on the floor plan where the localisation takes place.The size of the fingerprint database will depend on the sizeof the floor plan, the resolution of the possible positions (gridsize) and the number of access points (APs). The path lossescan be calculated with a theoretical model or obtained viaa measurement campaign. Because the latter is an expensiveand time consuming process, a network planner was used(WHIPP [4]). This approach results in a slightly reducedaccuracy but allows an immediate deployment. The networkplanner uses an advanced heuristic model to predict the pathloss between an AP and a certain location. Three contributionsare taken into account to calculate the total path loss: the sumof the distance loss along the path, the total wall loss alongthe path, and the interaction loss along the path.To estimate the improvement of the developed localisationalgorithm, a basic algorithm is used for comparison. This basiclocalisation algorithm relies only on the RSSI fingerprintingtechnique without the added intelligence (i.e. Viterbi principleand semantic data).D. ConfigurationThe experiments are conducted on the third floor of an officebuilding in Ghent (w-iLab.t office testbed [5]), which measures90 m by 17 m and consists of several computer classes, officesand meeting rooms. In Figure 1, a part of the floor plan isshown: the core consists of concrete walls (gray walls), theinner structure is movable and made of layered drywall (brownwalls) and the doors are made of wood (yellow walls). Thewireless network consists of 57 fixed nodes that were installedon a height of 2.5 m (blue dots in Figure 1). When usinga grid size of 1 m this results in 1573 grid points or 89661reference path loss values (1573·57 = 89661), which results ina fingerprint database of 350 KB (if we reserve 32 bit for eachvalue). Nine realistic test trajectories with an average length of87 m were outlined on the floor plan. Figure 1 shows one suchtest trajectory (red) and its reconstruction (black), the startingpoint is marked with a black dot.Figure 1. Part of the floor plan with a test trajectory and its reconstructionIII. EVALUATIONA. SimulationFor the simulations, the reference values from the fingerprintdatabase are used as input for the localisation algorithm (i.e.,path loss values from the positions along one of the nine testtrajectories). To simulate real measurements a certain amountof Gaussian noise is added to these reference values. Next,the accuracy of the reconstructed trajectories is calculatedfor an increasing level of added noise: 0 - 20 dB (i.e., thestandard deviation of the added Gaussian noise with zeromean). Two localisation systems are used to reconstruct thetrajectories: the developed localisation algorithm and the basicfingerprinting technique. These experiments are repeated tentimes for averaging. In Fig. 2, the mean and standard deviationof this accuracy are plotted as a function of the added levelof noise.Figure 2. Accuracy as a function of the added noise (simulation)Fig. 2 shows that the developed Viterbi algorithm alwaysoutperforms the basic one, in terms of both mean accuracy andstandard deviation. Compared to the basic algorithm the resultswere on average 2.78 m more accurate (3.88 m versus 6.66 m)with 2.92 m less standard deviation (3.04 m versus 5.96 m),for the worst test case. The improvement in accuracy can beexplained by the added intelligence in general. The smallerstandard deviation can be explained by the combination oftaking the previous positions into account and a maximumspeed which makes the localisation algorithm more robust tomeasurement deviations and outliers.B. Experimental validationThis time, the test trajectories are conducted by a humanwho hand-carried a mobile node with an antenna gain of 5 dBi.Two wireless technologies were tested: ZigBee and WiFi. Thesame two localisation algorithms were used to reconstruct thesame nine trajectories. The results can be found in Table I.Table IMEAN ACCURACY (M) AND STANDARD DEVIATION (S)Algorithm→ Basic Viterbi ImprovementMobile node ↓ M [m] S [m] M [m] S [m] M [%] S [%]ZigBee 2.96 3.63 2.18 1.31 26.4 63.9WiFi 3.38 3.13 2.2 1.46 34.9 49.4Again the developed localisation algorithm was more robustand achieved a better mean accuracy with lower standarddeviations than the basic algorithm. The relative improvementin mean accuracy and standard deviation is 26.4% and 63.9%for the ZigBee node and 34.9% and 49.4% for the Wifi node,respectively.C. Sensitivity AnalysisIn this section a sensitivity analysis is conducted, basedon the measurements from the experimental validation withthe mobile ZigBee node. This is important to estimate theinfluence of the localisation algorithm parameters on theperformance.The majority of testbeds used for testing a new localizationsystem have a very high node density. In a typical environmentthis node density is typically much lower. When using only10 out of the 57 fixed nodes, we obtain a mean accuracyof 6.58 m with the basic algorithm and 3.26 m with thedeveloped algorithm. As expected the accuracy is a bit lowerbut the relative improvement compared to the basic algorithmis bigger: 50.5% when 10 fixed nodes are used versus 26.4%when all nodes are used.The average time needed to calculate one location updatewill determine the ability for real-time usage and will dependon the available computational power but also on the used gridsize and number of paths retained in memory. When using agrid size of 1 m and retaining only the 100 best paths withevery location update, we obtain a calculation time of 1.5 mswhilst having no loss in accuracy (compared to the situationwhen all paths were retained in memory). This experiment wasconducted on a desktop computer with an Intel Core i7 3.40GHz processor and 8.00 GB DDR3-SDRAM.IV. CONCLUSIONSIn this work, a real-time localisation system based on theViterbi algorithm and semantic data was developed. The ad-vantage of this system is the independence of the used wirelesstechnology or specific indoor environment. The intelligenceis added to the localisation algorithm itself, so there is noneed for extra hardware costs. A network planner was usedto construct the fingerprint database, so there was also noneed for an extensive measurement campaign. Compared toa basic algorithm, the predictions were more accurate andthere was a huge improvement in standard deviation. Moreconcrete: the mean accuracy and standard deviation improvedby 26.4% and 63.9%, respectively. In a sensitivity analysis itwas shown that the advantage of the developed localisationalgorithm compared to the basic algorithm was even bigger inlower node densities and that it is able to work in real-time.In general, it can be embedded in both existing and futurelocalisation algorithms to further improve the accuracy.REFERENCES[1] Richa Bharadwaj, Akram Alomainy, and Clive Parini. Localisation ofbody-worn sensors applying ultra wideband technology. In Antennasand Propagation (APCAP), 2012 IEEE Asia-Pacific Conference on, pages106–107. IEEE, 2012.[2] Akis Kokkinis, Marios Raspopoulos, Loizos Kanaris, Antonio Liotta,and Stavros Stavrou. Map-aided fingerprint-based indoor positioning.In Personal Indoor and Mobile Radio Communications (PIMRC), 2013IEEE 24th International Symposium on, pages 270–274. IEEE, 2013.[3] Andrew J Viterbi. Error bounds for convolutional codes and anasymptotically optimum decoding algorithm. Information Theory, IEEETransactions on, 13(2):260–269, 1967.[4] David Plets, Wout Joseph, Kris Vanhecke, Emmeric Tanghe, and LucMartens. Coverage prediction and optimization algorithms for indoorenvironments. EURASIP Journal on Wireless Communications andNetworking, 2012(1):1–23, 2012.[5] Stefan Bouckaert, Wim Vandenberghe, Bart Jooris, Ingrid Moerman,and Piet Demeester. The w-ilab. t testbed. In Testbeds and ResearchInfrastructures. Development of Networks and Communities, pages 145–154. Springer, 2011.

Advanced indoor localisation based on the Viterbi algorithm and semantic data

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Advanced indoor localisation based on the Viterbi algorithm and semantic data

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