Indoor Positioning in Bluetooth Networks using Fingerprinting and Lateration approach Fazli Subhan Dept. of Computer and Information Science Univeristi Teknologi PETRONAS 31750 Tronoh, Perak, Malaysia
[email protected]
Halabi Hasbullah Dept. of Computer and Information Science Univeristi Teknologi PETRONAS 31750 Tronoh, Perak, Malaysia
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Azat Rozyyev Dept. of Computer and Information Science Univeristi Teknologi PETRONAS 31750 Tronoh, Perak, Malaysia
[email protected]
Sheikh Tahir Bakhsh Dept. of Computer and Information Science Univeristi Teknologi PETRONAS 31750 Tronoh, Perak, Malaysia
[email protected]
Abstract— Global Positioning System (GPS) is a well known navigation system for outdoor applications; however this technology does not work in indoor environments. In order to overcome this limitation, Bluetooth technology can be employed. Furthermore, Bluetooth technology provides an accurate and low cost solution for short range wireless communication. Most of the digital devices provide the Bluetooth functionality which also makes it a good candidate for indoor positioning. In this paper, we use Bluetooth devices for indoor positioning and use signal based parameters such as received power level for position estimation. The accuracy of indoor positioning system is greatly dependant on the parameters selected for estimation and the measurements obtained from the environment. However, the measurements are corrupted by various environmental conditions such as temperature, reflection, presence of obstacles, human body and other communication signals. Therefore, we need to filter the measurements. This paper presents an experimental relationship between the received power level and distance using the standard radio propagation model. The idea behind this study is to provide an accurate distance estimate for Trilateration approach. Based on the experiments performed, the average error is minimized from 5.87 meters to 2.67 meters using gradient filter. The use of gradient filter improves the accuracy by 45 %.
explored to select a parameter for distance related measurements. The parameters defined in Bluetooth specification are Receiver Signal Strength Indicator (RSSI), Link Quality (LQ), and Transmitted Power Control (TPL) [2]. This paper presents RF based indoor positioning techniques which determine object position, based on the signal strength received. Fingerprinting is the most accurate and popular methods which can be used for indoor object tracking [5]. The fingerprinting based positioning systems are carried out in two phases. First one is the off-line phase, the location fingerprints are collected by dividing the location in rectangular grids, and Multiple Access points are fixed to collect the RSSI at each grid location. The vector obtained of the RSSI values at a point is called the location fingerprint of that point. The second phase is called the online phase. The accuracy obtained by this method is more than the other RF based localization techniques. However it has a serious shortcoming that it is extremely time consuming. The alternative approach is the radio propagation model based method. This model is a simple mathematical expression which represents the relationship between RSSI and the distance. However the RSSI is affected by various environmental conditions which affect the accuracy. This paper proposes fingerprinting based indoor positioning using lateration approach. The novel idea behind this study is to combine the online phase of fingerprinting method together with the Trilateration approach in order to combine the accuracy of fingerprinting and lateration method. Furthermore RSSI readings obtained during data collection process will be filtered in order to remove the inconsistencies which occur due to the environmental conditions. Trilateration is trigonometric approach for tracking mobile objects considering the concept of triangles [4, 19]. GPS is a well known navigation system available now a day’s using the concept of Trilateration. The accuracy of Trilateration approach depends on the signal received and the environmental conditions. The accuracy can be improved by using Kalman Filter or extended Kalman filter in order to consider the dynamic behaviour of the moving object [6].
Index Terms— GPS, Bluetooth, Received Power, Received Power, Gradient Filter, Lateration. I. INTRODUCTION Global Positioning System (GPS) is a well known navigation system for outdoor applications; however this technology does not work in indoor environments. In order to overcome this limitation, Bluetooth technology can be employed. In Bluetooth networks there is no standard protocol defined for any kind of distance based measurements [1]. The specification provides a list of signal based parameters which can be used for distance based measurements, including object tracking, handover decision etc. In this paper signal parameters are experimentally
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Currently Bluetooth specification does not provide any proper support for indoor positioning or any kind of distance estimation. The specification only provides the list of parameters which can be used for distance based measurements which are RSSI, Link Quality (LQ), and Transmitted Power Control (TPL). According to our previous study which provides a detailed discussion on the parameter selection for distance estimation [18], the parameter chosen based on the experimental observation is RX-power level. The parameter identified in our previous work was based on hcitool, i.e. connection oriented approach. In this paper the RSSI values are obtained directly using Inquiry command with RSSI, which is provided in latest Bluetooth versions. The advantage of tracking RSSI values using Inquiry with RSSI is to return the RX-power level directly; there is no need of conversion of RSSI to RXpower level [6]. In this paper a relationship between RXPower levels with distance is established and used Gradient Filter for smoothing RSSI values. The advantage of using Gradient filter [14, 15] is to remove the inconsistencies occurring due to the environmental conditions, which strongly affects the accuracy level. The average mean square error obtained from the experiments is 2.67 m. The average mean square error can be further minimized by implementing fingerprinting online phase with the lateration techniques. According to our literature survey, fingerprinting is the most accurate indoor positioning technique which provides sufficient level of accuracy greater than Trilateration. However as discussed earlier that the offline phase of fingerprinting is very time consuming. Therefore a combination of fingerprinting online phase with the Trilateration approach is presented. This will combine the accuracy of fingerprinting technique and easiness of Trilateration technique. Although the great source of error is the random nature of RSSI values [8]. In order to improve the accuracy, we need to filter the measurements obtained and use the parameters which are suitable for specific environment. There are various techniques available for smoothing RSSI values in order to produce good results in terms of accuracy. Therefore the accuracy of the indoor positioning algorithms can be minimized up to the acceptable level by proper use of smoothing filters. The remainder of this paper is structured as follows. Section II discusses related work. In section III indoor positioning techniques are presented, Section IV discusses signal parameters in Bluetooth networks, Section V presents proposed indoor positioning technique, Section VI discusses experimental setup, Section VII discusses results and discussions and finally Section VIII discusses Conclusion and Future work. II.
RELATED WORK
Various signal based indoor positioning techniques are proposed by the researchers. This section summarizes only Bluetooth based indoor positioning techniques. Kotanen [6] presented local positioning system based on Received power level. The accuracy obtained using extended Kalman filter was 3.76 meters using the radio propagation model. Thapa [3] presented indoor positioning in Bluetooth networks using signal based parameters and estimated distance using the standard radio propagation model. Sheng Zhou and Pollard [8] in 2006 also presented
RX –power level based indoor positioning within a single cell. Another useful contribution from Mahtab [7] in 2007 presented signal based parameters in detail and experimentally verified various parameters. The author concluded that RX-power level correlates nicely with distance. The author compares RX-power level with other parameters such as RSSI, Link quality and TPL. In [12] the author used inquiry response rate as a parameter for indoor based positioning. The main idea presented in this approach is fingerprinting based indoor positioning together with inquiry response rate. In [17] the authors presented Bluetooth based indoor positioning Trilateration technique. The accuracy obtained at room level is about 4.56 meter. In [5], the author extended the idea of Kotanen [6] to Wireless Local Area Networks using Extended Kalman Filter. The average error using KNN approach is 3.37 meter and 2.11 meter using extended Kalman filter. The author compares Trilateration and Fingerprinting techniques in order to obtain good results compare to the techniques already presented. Based on the literature survey [9, 10, and 11] it is noticed that fingerprinting algorithm provides good accuracy, while the lateration provides efficient results compared to the time consumption. Therefore fingerprinting together with the lateration approach is presented in order to obtain high accuracy. III.
INDOOR POSITIONING TECHNIQUES
In this indoor positioning techniques are mainly classified in to two categories [5], the first one is geometric or trigonometric based and the second one is statistical based indoor localization techniques applicable to Bluetooth networks. A. Geometric Approach These techniques use the trigonometric behaviour of the triangles. A.1 Received Signal Strength Indicator (RSSI) RSSI is a geometrical or trigonometric approach of estimating object location; this technique is based on measurement of signal attenuation, the decrease of the signal strength relative to its original intensity. The amount of signal power received at the receiver antenna depends on the distance measured based on the fact that power transmitted reduces with respect to distance travelled by the signal within a complicated channel. Equations or functions describing the expected decrease in signal strength given the distance like the Friis equation can be used to estimate location relative to the source of the signal [6]. A.2 Trilateration Algorithm In this algorithm the distance of the mobile node from three fixed nodes whose positions are already known is first calculated using the radio propagation model. Figure 1 shows diagrammatic representation of Trilateration approach. Consider the case if the positions of three fixed nodes and their radii say r1, r2, r3 from the three fixed base stations say P1, P2, P3 are known, and we also know in advance their coordinates say (X0, Y0, Z0), (X1,Y1, Z1), and (X2,Y2, Z2), then the coordinates of the mobile node B can be estimated using Trilateration [5]. Consider the coordinates
of the mobile nodes are (x, y, z) then the radii Ri can be expressed by the following equation. In order to compute an object in three coordinate system we need to arrange four base stations. We can obtain the linear equation using the following equation. 1 1
1,2,3 … …
1. (2)
After simplification and applying Minimum Mean Square Error (MMSE) the equation becomes [5]. 3
Figure 1: Trilateration algorithm [3]
A.3 Time Difference of Arrival (TDOA) In this technique the difference between the signals received to the signal transmitted is calculated. This technique eliminates the tight synchronization of the AOA. This technique requires the accurate timing synchronization, which leads to the high position estimation accuracy. Distance is calculated by multiply the propagation time with the speed (v). This process is carried out for multiple signals arrived at different time of interval. The calculation of distance from the transmitter can also be calculated by using the technique triangulation or Trilateration. Figure 2 depicts TDOA, in which the receiver multiple signals from the transmitted using accurate time synchronization. Due to the accurate time synchronization required, this technique is rarely used in Bluetooth short range networks, because Bluetooth is the low cost personal area network [ 9, and 10].
________________________________________ 1.
www.cs.unc.edu/~tracker/.../SIGGRAPH2001_CoursePack_08.pdf
Figure 2: Position Estimation using Multilateration (TDOA) algorithm
A.4
K-nearest neighbors
K-NN is a Fingerprinting based method for position estimation which consists of two phases. The first one is called an offline phase in which we build a look-up table from collected RSSIs. The entire area is divided into a rectangular grid of square blocks called candidate blocks. At each of these blocks, we measure the RSSIs 100 times in order to predict good measurements. Each row of the table is an ordered pair of coordinate and a list of RSSIs. A coordinate is an ordered pair of integers (x, y) representing the coordinates of a candidate point. In the second phase of KNN algorithm on-line phase, the corresponding base station collects the RSSIs values which the user receives at the current position and compares the result with the stored values collected in offline phase. The program then examines the stored values in a look up table and finds the nearest neighbor point which returns the user's current position. If K equals to 2 or 3, then it will find the two or three closest candidate points and return the average of their coordinates as the user's current location. The advantage of this technique is the higher accuracy ratio but due to the offline phase which is very time consuming. B. Statistical Approach B.1 Kalman Filter Algorithm (KF) Kalman filter is a group of mathematical equations which estimates the object position based on the previous input and at the same time predicts the future state of the object. The KF process works statistically in two parts: time update equations and the measurement update equations. The time update equations are responsible for projecting forward (in time) the current state and error covariance estimates to obtain the priori estimates for the next time step. The predicted state estimate is known as the a priori state estimate because it does not include observation information from the current time-step. The measurement update equations are responsible for the feedback in incorporating a new measurement into the a priori estimate that is, the current a priori prediction is combined with current observation information to refine the state estimate to obtain an improved a posteriori estimate [16]. Figure 3 illustrate the flow of Kalman Filter1.
Applying formula to all the remaining RSSI values becomes ,
,
,………
6
Gradient of RSSI at each instant of is: (7)
∆
Where ∆ Taking average with respect to time interval t is:
Figure 3: Flow of Kalman Filter [16]
B.2 Extended Kalman Filter Algorithm (EKF) Kalman filter is normally applicable to the linear data, while EKF algorithm uses a statistical approach for location estimation. This algorithm works in a situation where the process data or system is non-linear, in order to linearizes the data. The non-linearity of the system can be associated either with the process model, observation model or with both. The linearization ability of this filter is the major difference between this algorithm and KF algorithm . B.3 Gradient Filter In order to design an algorithm for indoor positioning which produce good results in terms of accuracy, RSSI values obtained in measurements needs to be filtered in order to remove noise. There are various filters designed and tested to remove the inconsistencies occurring in RSSI readings due to the various environmental effects. In this paper gradient filter is used to filter RSSI readings obtained during experiments. This filter was designed for removing the inconsistencies that occur when the mobile device is moving out of coverage area or due to some environmental conditions, non line sight effects disturb the communication range and the signal is broken down. Therefore this filter specially predicts the RSSI readings when the communication is blocked or out of range. The main reason of using this Gradient filter [14, 15] is to remove the deficiency caused by Kalman filter when there is a communication hole. Following are the formulation of Gradient filter for smoothing RSSI values [14, 15]. Consider the scenario depicted in the experimental setup, in which a mobile node is moving inside a coverage area, whose RSSI is continuously monitored from a fixed station. The main idea behind filtering RSSI data is to estimate the distance between fixed station and mobile terminal. Let us consider the collected values of RSSI at constant time are ,
,
…………….,
(4)
Here it is assumed that the mobile node is moving with constant speed at 1 m/sec, which is equal to step size of 1 m for each instant of RSSI. Let us consider the Gradient of RSSI at any time is (5)
8 ∆ Where ∆ To calculate
9
if it is unknown ∆ (10)
To find the value of
at time
∆ (11) After simplification the predicted value of
is obtained: 12
The error between the predicted value and previous measured value is calculated by taking the standard deviation as 13 1 , 2 Applying standard deviation to the above equation the formula for Gradient filter is ,
(14)
.
C. Radio Propagation Model In this research distance measurements using the RXpower level is based on the radio propagation model. This model gives us the relationship between the signal received and distance. Furthermore this model indicates that the received power decreases logarithmically with distance [8]. 10 log
15
Where P(d) is the signal received , P(do) is the signal strength at some reference point, and γ is the path loss exponent normally represented by n. The value of n lies from 1 to 4 for indoor measurements. IV.
Algorithm 1: Bluetooth Device Discovery and Connection. BEGIN /* Open and initialize Bluetooth devices */
SIGNAL PARAMETERS IN BLUETOOTH NETWORKS
In Bluetooth specification there are three different types of signal parameters which are used for distance related measurements. These parameters are RSSI, Link Quality (LQ), and Transmitted Power Link (TPL). RSSI is an 8-bit signed integer value ranging from +128 to -127. According to the Bluetooth specification, when the received (RX power level) is within or above/below the Golden Receiver Power Range (GRPR), it is considered as an ideal RX power level. Positive RSSI values indicate that RX-Power level is above the Golden Receiver Power Range (GRPR) and negative values indicate that it is below the GRPR, while the zero implies that the RX power level is within the golden range. Therefore RSSI is taken as a parameter for RX power level. Or in simple words it is the power received by the antenna. On the other hand RX-power level is obtained indirectly from the measured RSSI values. These values are then converted to RX power level using the radio propagation model. According to the experiments performed, RX power level is best suited with distance. RSSI can be converted to RX power level only if the Upper and Lower threshold values of the GRPR are known, therefore RX-power is chosen as a parameter for distance based measurements and also for position estimation. In order to measure RX-power level, there is a command Inquiry result with RSSI which measures the Received power of the current inquiry response. This parameter does not require any connection. The radio layer monitors the RX power level of the current inquiry response from the discovered new device and gives the correspondence RSSI level. The devices which are used in experiments support this parameter. Link Quality (LQ) and Transmitted Power Level (TPL) are also considered as signal parameters, but according to the detailed study performed in [7] suggested that these parameters poorly correlates with distance. Based on the results presented in [7], further experiments were performed using class 2 devices in order to validate the results using simple Master/Slave relationship. After complete analysis it was confirmed that RX-power level is the only parameter which correlates much better with distance compared to RSSI and other parameters [18]. During analysis the hcitool and hciconfig command line utilities. In order to measure and compare all the parameters relation with distance the following algorithms are used. Algorithm 1 is used to discover all the active devices and display their unique addresses. Algorithm 2 is used to measure the signal parameters. The algorithms are implemented in BlueZ command line utilities under fedora 10. For all the three parameters active connections are required. The command line utility works only for active connections. Algorithm 3 performs the conversion process. Once if the RSSI measurements are obtained, then with the help of radio propagation model RSSI measurements obtained from the environments are converted to distance estimates. Algorithm 3; step 2, converters RSSI readings obtained from hcitool to RX-power level.
hciconfig hci0 up /* Check for Bluetooth local device */ hcitool dev hcitool scan /* scan and automatically connect Bluetooth devices While [ 1] hcitool scan rfcomm connect all end END Algorithm 2: Bluetooth Signal Parameters. BEGIN /* check if the device are already connected If rfcomm connect = 1 While [1] /* Read RSSI, LQ and TPL of the connected Bluetooth device HCL_ Read _RSSI ( device address) HCL_Read_LQ (device address) HCL_Read_Transmit_Power –level(device address) End elseif While [ 1] hcitool scan rfcomm connect all end END Algorithm 3: Distance Estimation in Bluetooth Network BEGIN /* Step 1: Conversion of RSSI to RX for i = 1 : length (rssi) if (rssi (i) > 0) RX (i) = rssi (i) + GRPR (upper range) elseif RX (i) = rssi – GRPR (lower range) elseif if (rssi (i) = 0) GPRR(upper) ≤ RX ≥ GPRR (lower) End /* Step 2: Distance calculation using Free space Propagation Model for i = 1: length (RX ) distance (i) = 10 ^ [Ptx - RX (i) + G - 20 * log (c/4πf)] / 10 * n end END
V.
PROPOSED POSITONING TECHNIQUE
This section discusses an integration of fingerprinting and lateration approach. The main idea behind this technique is to design an efficient and simple approach for object tracking. In this hybrid approach the concept of fingerprinting is used. This algorithm works in two phases, offline phase and online phase. First of all the whole area is divided in many square shape grids, and their location in terms of XY coordinate is stored in the database. After that fixed nodes are installed at the known location. The program collects RSSI measurements at each square grid many times and stores it in the database together with the location coordinate. This process is considered as offline phase and also a very time consuming job. After this step the positioning program starts monitoring the movement of the mobile device and gathers RSSI samples. The algorithm matches the fingerprints obtained at online phase with the stored values of RSSI. If the measures obtained matches with the stored fingerprints then the location is displayed. The algorithm calculates the nearest neighbors, if the measurements corresponds to the two coordinates then the algorithm calculates the two closest candidates points and display the average of their coordinates and display the user location based on the fingerprints.
From the figure it is clear that fingerprints from the offline phase will be consider as online fingerprints for Trilateration approach. In this hybrid approach the fixed stations will continuously monitor the signal strength of the mobile device. Fingerprints will be collected at each grid and stored in the database. Distance from the target object and fixed nodes will be measured using the radio propagation model and finally the Trilateration algorithm will display the coordinate of the user location. The performance of the integration approach is yet to be validated using the real experiments. The quantity of error will be compare with the stored fingerprints and finally the results obtained from Trilateration approach and fingerprinting approach will be provided as input to the filter for minimizing the error.
In order to provide more simple and accurate solution, three or more than three fixed nodes are installed and the fingerprints are calculated in the same way as in fingerprinting. Distance between the mobile device and the fixed node whose location is already known is calculated using radio propagation model. Figure 4, presents the system diagram of the proposed indoor positioning system. Here an additional component is added for smoothing the measurements. Once we filter the readings then accuracy level increases.
2
Figure 5: Integration of Trilateration and fingerprinting approach.
VI.
Figure 4: Block diagram of the proposed indoor positioning algorithm
Figure 5 shows integration of fingerprinting and Trilateration approach. According to the initial results obtained in the experiments it is assumed that the accuracy of the integration algorithm will increase as compare to the accuracy of the individual approach of both the algorithms.
2.
Wireless Network Application - Indoor Positioning by Dr Pu Chuan Chin, .ppt slides,
EXPERIMENTAL SETUP
This section discusses the experimental setup for establishing a relationship between RX-power level and distance. For this purpose the detail experiments were been conducted in the department of information sciences, wireless communication lab [18, 20]. The dimensions of the lab were 10 * 12, having area 120meter2. To make it more accurate, different specification of Bluetooth USB dongles are used. The devices which gave good results were selected for data collection. The devices which were used in the experiments are Bluetooth USB dongles having class 2 specifications, Nokia 5130 as mobile device. The Bluetooth USB dongles were fixed and the Nokia mobile device was used for data collection in real time piconet structure. The algorithms for Bluetooth device discovery and signal parameter calculation were written in BlueZ, [13] latest version installed on Fedora 10. In this research RSSI readings are collected using two different techniques. The first is connection oriented technique which used hcitool, and hciconfig command based utilities for signal measurements. While the second method for tracking RSSI readings is Inquiry with RSSI, which is considered, as RX-power level. In first method we calculated RSSI, LQ, and TPL. All the three parameters were calculated by moving the mobile device away from the master device. The step size was 30 cm in the first method and kept 1 m for the second method i.e. Inquiry with RSSI. At each step size of 30 cm and 1m more than 50 samples of data were been taken for all the parameters. The average values obtained at each step size
were selected for data analysis. In the second experimental setup RSSI readings using Inquiry results with RSSI were being collected, which is equal to the RX-power level. In the second method there is no need of conversion process. Therefore collecting large quantity of data requires Inquiry Result with RSSI. Table 1 contains data obtained using connection method, while table 2 contains average values of inquiry result with RSSI. TABLE 1: RSSI readings using hcitool.
Distance (m) 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3
RSSI (dBm) 0 -4.5 -10 -15 -20 -20.5 -26.5 -26.5 -27 -28 -28.5 -27.5
Distance (m) 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7 6.0 6.3 6.6 6.9
RSSI (dBm) -26.5 -27.4 -28.4 -28 -28 -27 -29 -28 -28.5 -29.5 -30.5 Disconnected
TABLE II RSSI readings using Inquiry result with RSSI
Distance (m) 0.1 1 2 3 4 5 6 7 8 9 VII.
RSSI (dBm) -37.50 -69.64 -76.24 -82.50 -85.42 -86.64 -87.24 -87.56 -87.10 -87.48
Figure 6: Comparisons of Experimental and Model
In order to predict good results RX-power level is simulated with different values of n, which is propagation constant. Figure 7 shows the comparisons among the known values of n. The value of n is required to be calculated in order to precisely estimate the distance between transmitter and receiver. Therefore 4 different types of n values were selected. For indoor environment the value of n starts from 1 to 4. For the value of n = 1.50 RX power level correlates better with distance than other values of n. However this value is also dependent on the selected parameter value of A, value which is the power level at fixed distance 1 meter. In this case the value of A is 47. 42. The proper combination of n and A gives us the good estimates.
RESULTS AND DISCUSSIONS
This section discusses the experimental results that were performed in order to find out the relationship of RX-power level with distance using the standard radio propagation model. The idea behind this relationship is to determine the error and to check which filter is most suitable for smoothing RSSI values. The radio propagation model is used to establish a relationship between distance and RXpower level. Algorithm 3 is used, for distance estimation, which is based on the radio propagation model discussed in section II. Figure 6 shows the comparisons between real RX-power level and propagation mode. These RSSI values are obtained using hcitool command. This utility is available in Blue-z latest version. The values obtained using hcitool needs further conversion to RX-power level. The relation between this kind of conversion is addressed in algorithm 3 A. The conversion is only possible if we already knew the upper and lower bounds for GRPR.
Figure 7: Comparisons of distance and RX for different values of n.
Figure 8: shows the experimental comparison of RXpower level obtained during measurement with the Gradient filter, which is used to smoothen the RSSI values. Kalman filter were tested at the measurement stage as well in order to use it for RSSI in measurement stage, but the results obtained compared to the error between actual experimental values and propagation model values are greater. Therefore Gradient filter is used in the measurement stage [15]. The average mean square error occurred between actual readings and the readings provided after applying Gradient filter is 4.29 m.
The difference between Filtered RSSI readings and radio propagation model shows that RX-power level is suited best with distance. In order to calculate the coordinate of the mobile node an integration of fingerprinting and Trilateration based hybrid approach are proposed. The combination of this hybrid algorithm together with the radio propagation model will further improve the position accuracy. VIII.
Figure 8: Comparison between RX-power levels with Gradient filter.
Figure 9 shows the results obtained from radio propagation model, actual experiments and Gradient filtered data. The results show an average approximated mean error between filtered data and radio propagation model is 2.67 m which is acceptable for indoor position estimation. As discussed earlier, the main source of error in position estimation is the uncertain values of RX-power level. These values greatly affect the accuracy of any indoor position system. Therefore Gradient filtered values are used for comparisons with distance. The value of n was chosen based on the simulation performed in figure 6. Gradient filter gives us two kinds of benefits, one is smoothing RSSI values and the other is giving us the value of A which is always selected at distance of 1 m. The relationship between distance and RX-power level can be used in selecting the optimal filter for smoothing RSSI values.
CONCLUSION AND FUTURE WORK
This paper presented the concept of indoor positioning using low cost Bluetooth networks. The paper also discussed various indoor positioning techniques and highlighted the problem of error which occurred due to various environmental conditions. Furthermore this paper discussed signal parameters for Bluetooth networks and based on the previous research performed in [18], RXpower level is considered as parameter for distance measurement and indoor positioning. The novel contribution in this paper is the use of Gradient filter for removing the inconsistencies which occurred due to noise from the environment in collecting RX-power level using inquiry mode. Conversion of RX-power level to distance estimates using the standard radio propagation model results in an average error of 5.87 meter while the use of gradient filter in the measurement stage minimized the average error to 2.67 meter, which is 45 % improvement. Therefore based on the experiments performed and proper use of environmental variables gradient filter improves the accuracy up to 45 % which can be used as base for coordinate measurements. The idea behind using filtered measurements from the environments is to improve the accuracy and to provide better distance estimate for Trilateration and fingerprinting approach. Therefore based on the results obtained from the experiments performed, it is proposed that Trilateration together with fingerprinting approach will work better in order to obtain high accuracy in terms of object tracking. Our future work is to implement lateration approach together with fingerprinting online phase and taking benefits from the use of gradient filter in the measurement stage to minimize the error. ACKNOWLEDGMENT The authors would like to thank Dr Kholoude Atalah at University of Las Palmas de Gran Canaria, Spain, for her useful suggestions and providing help in formulating the gradient filter.
Figure 9: Comparisons of Actual RSSI, Propagation Model and Gradient Filter.
Table III summarizes the average mean square error occurred. TABLE III. Error analysis Comparison results Average mean error Actual RX-power level and 5.87 m Propagation Model RX power level Actual RX and Filtered RX 4.29 m Filtered RX and Propagation Model RX
2.67 m
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