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Performance Evaluation of Location Estimation System Using a Non Fixed Single Receiver
Performance Evaluation of Location Estimation System Using a Non Fixed Single Receiver
International Journal of Contents. 2014. Dec, 10(4): 69-74
Copyright © 2014, The Korea Contents Association
  • Received : October 28, 2014
  • Accepted : December 22, 2014
  • Published : December 28, 2014
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About the Authors
Enkhzaya Myagmar
Soon-Ryang Kwon
srkwon@tu.ac.kr

Abstract
General location aware systems are only applied to indoor and outdoor environments using more than three transmitters to estimate a fixed object location. Those kinds of systems have environmental restrictions that require an already established infrastructure. To solve this problem, an Object Location Estimation (OLE) algorithm based on PTP (Point To Point) communication has been proposed. However, the problem with this method is that deduction of performance parameters is not enough and location estimation is very difficult because of unknown restriction conditions. From experimental tests in this research, we determined that the performance parameters for restriction conditions are a maximum transmission distance of CSS communication and an optimum moving distance interval between personal locations. In this paper, a system applied OLE algorithm based on PTP communication is implemented using a CSS (Chirp Spread Spectrum) communication module. A maximum transmission distance for CSS communication and an optimum moving distance interval between personal locations are then deducted and studied to estimate a fixed object location for generalization.
Keywords
1. INTRODUCTION
Nowadays location estimation and navigation technologies are useful for a daily life. Especially, to estimate and find a location of the fixed object such as a car, in large size indoor and outdoor environments such as parking lots of mall, are necessary. The location estimation and navigation technologies may be classed into indoor technology and outdoor technology.
GPS (Global Positioning System) is used for wireless positioning technology outdoors [1] . Wireless communication technologies such as WLAN (Wireless Local Area Network), UWB (Ultra Wide Band), ZigBee, CSS (Chirp Spread Spectrum) and etc. are used for wireless positioning technology indoors [2] - [5] . Studies for location recognition algorithms, applied in indoor and outdoor environments, have been done. Those algorithms are TOA (Time Of Arrival), RSSI (Received Signal Strength Intensity), AOA (Angle Of Arrival) and etc. [6 - 7] . To estimate the location of an object, it is general to apply a triangulation method on an infrastructure composed of wireless modules and servers. But there is a problem that above method system can be only applied when an especial infrastructure is established.
To solve this problem, an object location estimation method using PTP communication has been proposed [8] - [10] . But there is a problem that deduction of performance parameters for the location estimation is not enough.
In this paper, our goal is to study OLE algorithm and to propose the condition to generalize the algorithm in wireless communication environment. To achieve this, we choose CSS communication as a wireless communication environment.
Then the location estimation system based on PTP communication is implemented. A maximum transmission distance of CSS communication and an optimum moving distance intervals between personal locations in indoor and outdoor environments are deducted by experimental results.
The remaining sections are structured as follows. Section 2 provides an overview of the related works in the area of CSS technology. Section 3 describes the main structure and implementation of the system. In Section 4 experiment environments are described. Then experiment results are analyzed in Section 5. Finally, we present our conclusions and future work in Section 6.
2. RELATED WORKS
- 2.1 Ranging by SDS-TWR Algorithm with CSS Technology
SDS-TWR (Symmetrical Double-Sided Two Way Ranging) algorithm is an advanced algorithm compared with the TWR algorithm. It is based on clock synchronization mechanism. Fig. 1 shows wireless sensor node infrastructure applied to SDS-TWR algorithm. The infrastructure consists of a server and sensor nodes.
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Wireless sensor node localization model
Fig. 2 shows a working procedure and a flow chart of the SDS-TWR algorithm
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Working procedure and flow chart of the SDS-TWR
As shown in Fig. 2 , SDS-TWR algorithm works as follows: Firstly, the anchor node A sends the ranging data to the un-known node B, and then starts a timer. When node B receives the ranging data from node A, node B sends ACK (acknowledgment frame) to node A. At this point, node B’s response time is recorded as T reply B. When node A receives the ACK which is sent by the node B, node A stops timer, and the time of node A is recorded as T reply A. Then, the node B sends the ranging data to the node A, node B starts a timer. When node A receives the ranging data from node B, node A sends acknowledgment frame to node B. At this point, node B’s response time is recorded as T reply B. When node B receives the ACK which is sent by the node A, node B stops timer, and the time of node B is recorded as T round B. T p , the propagation delay time of the ranging signal in the air, is established. According to the procedure of SDS-TWR, it can be derived by the following Eq. (1) [7] .
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- 2.2 Object Location Estimation (OLE) algorithm
OLE algorithm using PTP communication is shown in Fig. 3 .
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Object location estimation model at two points
The OLE algorithm has two states:
- 1) Initial setup state (Fig. 3a)
A distance (d i ) between a first location (P i ) of a person and a fixed object of i th (FO i ) is measured by Eq. (2). C is a transmission velocity of radio wave. After the person moves to second location (P i+1 ) by a distance interval (dist is a distance between Pi and P i+1 ), a distance (d i+1 ) is measured as shown in Fig. 3 a. If it is d i ˃= d i+1 , the fixed object is located in 1 quadrant or 2 quadrant. Ө i+1 , an angle of the received signal at P i+1 , is calculated using d i and d i+1 shown as Eq. (3):
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- 2) Progress state (Fig. 3b)
After state 1 is perfomed, a person moves to third location (P i+2 ) toward target along Ө i+1 by dist. Also, a distance (d i+2 ) between FO i and P i+2 is checked same as d i+2 ˂ d i+1 . Then state 2 is continuously doing until estimating the fixed object location.
3. STRUCTURE AND IMPLEMENTATION
The structure of the location system applied OLE algorithm for a fixed object is shown in Fig. 4 .
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Structure of location estimation system
In here, a person has user`s terminal. It consists of a notebook, AVR-ISP (AVR In-System Programming) programmer and a CSS receiver. OLE algorithm is run in notebook GUI. The GUI environment is made by Visual C++ in MS 2008.
Raw data (d i ) is derived from the fixed object to CSS receiver in CSS communication. Then the CSS receiver sends raw data to the notebook using AVR-ISP programmer. It converts UART communication to RS-232 communication. Then a measured distance is shown in GUI environment. An angle (Ө i+1 ) is calculated by the OLE algorithm implemented in GUI. It is shown in GUI environment.
4. EXPERIMENT ENVIRONMENT
In here, experiments` purpose is to derive a maximum transmission distance and an optimum moving distance interval in CSS communication.
- 4.1 Maximum transmission distance measurement
This experiment purpose is to derive a maximum transmission distance in CSS communication. The experiment is performed in outdoor environment (13.99m X 70m X 1m) as shown in Fig. 5 .
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Outdoor experiment environment to derive a maximum transmission distance
There are 14 fixed objects(FO n , where n=1, 2, ···, 14) which are located by 5m interval each other. A distance from personal locations (P 0 , P 1 or P 2 ) to target (FO n ) is measured in CSS communication by Eq. (1)-(2). Measurement trial times are 21 per a distance.
- 4.2 Experiment to derive an optimum distance interval on CSS communication
Below experiments purpose is to find an optimum moving distance interval from 3m, 6m and 9m when the OLE algorithm is applied in indoor and outdoor environments.
- 4.2.1 Indoor environment
The experiment is performed in indoor environment (13.42m X 30m X 1m) as shown in Fig. 6 .
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Indoor experiment environment
CSS communication can be performed well when it is located at 1m toward z coordinate. So CSS receiver and the fixed object are set at 1m from ground. Fig. 6 a shows 12 fixed objects that are set by 5m interval. Thus first start location is P 0 , P 1 , P 2 and P 3 which are location points moved by 0m, 3m, 6m and 9m from P 0 respectively. We can decide a quadrant decision by a relation of d 0 and d 1 . If d 0 ˃= d 1 , a fixed object location is supposed in quadrant 1. Otherwise a fixed object is located in quadrant 2. d0 (between P 0 and FO 1 ) is measured by 10 times and calculated by average value. It is discarded the calculated average distance when a distance is measured by -1. The average distance is shown in user`s terminal GUI environment. Then user moves P 1 and measure a distance (d 1 ) between P 1 and FO 1 . An average distance of d 1 is measured by same method as an average distance of d 0 . The d 0 and d 1 is used to calculate an angle (Ө i+1 ) at P 1 . Other case (P 2 and P 3 ) is performed same as above progresses.
- 4.2.2 Outdoor environment
The experiment is performed in outdoor environment (13.42m X 60m X 1m) as shown in Fig. 7 .
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Outdoor experiment environment
Fig. 7 a shows 24 fixed object coordinates that are set by 5m interval toward to z axis. First start location is P 0 , P 1 , P 2 and P 3 which are location points moved by 0m, 3m, 6m and 9m from P 0 . A distance from P 0 , P 1 , P 2 or P 3 to target is measured by the same methodology as indoor environment.
5. EXPERIMENT RESULT
- 5.1 Maximum transmission distance measurement
Table 1 shows a probability for each distance to measure (Prob_d_meas) between real distances (Real_d) and average measured distances (A_meas_d) from P 0 to FO n when a distance was measured by 21 times (Real_meas_time).
Probability for each distance to measure
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Probability for each distance to measure
Experiment measured time (Exp_meas_time) was expressed for measured distance time. We considered that the measured distance could be measured when Meas_prob_d was over 60%. In this case, for P 0 _FO n , Exp_meas_time and Meas_prob_d were shown that a maximum transmission distance measurement of CSS communication on PTP was measured by average 88.57% when the safety measured distance was within 55m except for P 0 _FO 4 (the real distance was 20m). When the probability of a measured distance was over 60%, a distance couldn`t be measured because of Pro_d_meas.
Fig. 8 shows about all case (P 0 _FOn, P 1 _FO n and P 2 _FO n ).
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A probability of distances from each P0_FOn, P1_FOn and P2_FOn
For P 1 _FO n , an average of maximum transmission distance measurement of CSS communication on PTP was 90.95% when a probability of measured distance was within 55m except for P 1 _FO 4 (the real distance was 20m). When the probability of a measured distance was over 60%, distances couldn`t be measured about 60m and 70m. But P 1 _FO 13 was measured by 66.66% when the real distance is 65m.
For P 2 _FO n , an average of maximum transmission distance measurement of CSS communication on PTP is 91.9% when the real distance was within 55m except for P 2 _FO 4 (the real distance was 20m). When the real distance was over 60, a distance couldn`t be measured about 60m and 70m. But P 2 _FO 13 was measured by 66.66% when the real distance was 65m.
Real distance was compared with absolute average errors in Fig. 9 for P 0 _FO n , P 1 _FO n and P 2 _FO n .
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Absolute distance errors for P0_FOn, P1_FOn and P2_FOn
When the real distance was 20m for all case, a distance couldn't be measured same as the measured distance probability. So we considered calculating absolute average distance error except for P 0 _FO 4 , P 1 _FO 4 and P 2 _FO 4 . Absolute average distance error of P 0 _FO n was 0.82m. Absolute average distance error of P 1 _FO n was 0.94m. Absolute average distance error of P 2 _FO n was 0.71m. Therefore absolute average distance error of all case was 0.82m. The safety maximum transmission distance was 55m. But we considered a maximum transmission distance was 60m because maximum transmission distances of P 1 and P 2 were more than 60% criteria.
- 5.2 Experiment to derive an optimum moving distance interval on CSS distance
- 5.2.1 Indoor
Table 2 shows that absolute average distance errors (a_d_err) at P 0 and P 1 were 1.88m and 1.9m when the moving distance interval between P 0 and P 1 was 3m.
Moving Distance interval is 3m in indoor
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Moving Distance interval is 3m in indoor
Absolute average angle error was 16.7 degree for all case. Quadrants for FO 8 and FO 12 in table 2 were not same as real quadrant (Real Quad) 1 because absolute distance error was made by multi path loss. Other quadrants were same as real quadrants.
When the moving distance interval was 6m, absolute average distance errors were 1.77m and 1.81m at P 0 and P 2 , respectively. Absolute average angle error was 14.4 degree. Quadrant of FO 1 was different with real quadrant 2. Other quadrants were same as real quadrants.
When the moving distance interval was 9m, absolute average distance errors were 1.75m and 2.2m at P 0 and P 3 , respectively. Absolute average angle error was 8.56 degree. Quadrant of FO 5 was different with real quadrant 2. Other quadrants were same as real quadrants. For all case, absolute average distance errors were 1.8m at P 0 and 1.97m at P 1 , P 2 and P 3 , respectively. Absolute average angle error was 13.2 degree.
- 5.2.2 Outdoor
Table 3 shows that absolute average distance errors were 3.06m and 3.13m at P 0 and P 1 , respectively when the moving distance interval between P 0 and P 1 was 3m.
Moving interval is 3m in outdoor
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Moving interval is 3m in outdoor
Absolute average angle error was 13.4 degree. Quadrants for FO 8 , FO 9 , FO 12 and FO 21 were not same as correspondent real quadrants because absolute distance errors were made by multi path loss. Other quadrants were same as correspondent real quadrants.
When the moving distance interval was 6m, absolute average distance errors were 3.74m and 3.74m at P 0 and P 2 , respectively. Absolute average angle error was 26 degree. Quadrants for FO 1 , FO 4 , FO 9 , FO 11 , FO 12 , FO 21 , FO 22 and FO 23 are different with correspondent correct quadrants. Other quadrants were same as real quadrants.
When the moving distance interval was 9m, absolute average distance errors were 2.16m and 2.9m at P 0 and P 3 , respectively. Absolute average angle error was 15.5 degree. Quadrants for FO 6 and FO 8 were different with correspondent correct quadrant. Other quadrants were same as real quadrants. For all case, absolute average distance errors were 2.99m at P 0 and 3.26m at P 1 , P 2 , and P 3 . Absolute average angle error was 18.3 degree.
The quadrant was correctly checked by 83.33% when the moving distance interval in indoor and outdoor environments was 3m. The quadrant was correctly checked by 91.6% for indoor environment and 66.66% for outdoor environment when the moving distance interval in indoor and outdoor environments was 6m.The quadrant was checked correctly by 91.6% when the moving distance interval in indoor and outdoor environments was 9m.
6. CONCLUSION AND FUTURE WORK
OLE algorithm based on PTP communication was studied indoors (10m X 16m X 1m) before. This paper was studied to generalize the OLE algorithm. For it, OLE algorithm was applied and implemented. Therefore, the maximum transmission distance and the optimum moving distance interval were deducted and used to estimate a fixed object location.
The maximum transmission distance was 55m at P 0 by experiment results. But we considered maximum transmission distance was 60m for finding the optimum moving distance interval because maximum transmission distances of P 1 and P2 were more than 60% criteria. Absolute average distance error of all case was 0.82m. The distance couldn`t be measured for P 0 _FO 4 , P 1 _FO 4 , and P 2 _FO 4 when the moving distance interval was 20m.
The quadrant was checked correctly by 83.33% and 91.6% when the moving distance interval in indoor and outdoor environments was 3m and 9m respectively. So optimum moving distances was 3m and 9m in indoor and outdoor environments respectively. When the OLE algorithm was verified in indoor (13.42m X 30m X 1m) for all case, absolute average distance errors were 1.8m at P0 and 1.97m about P 1 , P 2 and P 3 . Absolute average angle error was 13.2 degree. When OLE algorithm was verified in outdoor (13.42m X 60m X 1m) for all case, absolute average distance errors were 2.99m at P 0 and 3.26m at P 1 , P 2 and P 3 . Absolute average angle error was 18.3 degree.
In conclusion, OLE algorithm using CSS communication could be possible to estimate a fixed object location when the fixed object is located in indoor (30m) and outdoor (60m) environments. However, there was multi path loss. Future work is that OLE algorithm should be studied when a moving distance interval can be measured automatically and is not constant. Also, a compensation algorithm for a distance error should be studied.
Acknowledgements
This Research was supported by the Tongmyong University Research Grants 2012.
BIO
Enkhzaya Myagmar
She received the B.S. in department of electronic engineering from Huree Information Communication Technology Institute, Mongolia in 2007. She received the M.S. in department of information communication from Tongmyong University, Korea in 2010. Since then, she has been studying the Ph.D. degree in Tongmyong University, Korea. Her main research interests include RFID/USN, home network, RTLS, VLC communication, obstacle avoidance for robot.
Soon Ryang Kwon
He received the B.S. in department of electronic engineering from Donga University, Korea in 1982. He received the M.S. in department electronic engineering from Busan University, Korea in 1984. Then he received the Ph.D. from Chungnam University, Korea in 1999. He worked at ETRI(Electronics and Telecommunications Research Institute) as a senior researcher between 1984 to 1999. Since then, he has been working as a professor of electronic engineering department in Tongmyong University, Korea. His main research interests include RFID/USN, home network, RTLS, VLC communication, mobile communication system.
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