On-Line Ranging for Mobile Objects Using ZIGBEE RSSI Measurement

Halder, Sharly Joana; Choi, Tae‐Young; Park, Jin-Hyung; Kang, Sunghun; Yun, Sung Ho; Park, Joon Goo

doi:10.1109/icpca.2008.4783693

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…From the above findings, we can say that the proposed BOTH filtering algorithm could reduce more average error and maximum error distance than other existing algorithm [4], [12], [27], [28]. The average reduction of distance error deduced by using the proposed BOTH filtering is 56% and 68% average error and maximum error, respectively than the existing RSSI filtering [4] and Fusion filtering [27].…”

Section: Experimental Performancementioning

confidence: 71%

“…Reference nodes broadcast a message to inform their position at regular intervals. Unknown nodes receive the broadcast message from reference nodes and measure the strength of the received signal [4], [27], [28]. Localization errors for this method range from two to three meters at average, with indoor errors being larger than outdoor ones.…”

Section: Converting Signal Strength To Distancementioning

confidence: 99%

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Adaptive Filtering for Indoor Localization using ZIGBEE RSSI and LQI Measurement

Halder¹,

Park²,

Kim³

2011

Adaptive Filtering Applications

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Section: Experimental Performancementioning

confidence: 71%

Section: Converting Signal Strength To Distancementioning

confidence: 99%

Adaptive Filtering for Indoor Localization using ZIGBEE RSSI and LQI Measurement

Halder¹,

Park²,

Kim³

2011

Adaptive Filtering Applications

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“…From the above findings, we can say that the proposed BOTH filtering algorithm could reduce more average error and maximum error distance than other existing algorithm [8,13,18,19]. The average reduction of distance error deduced by using the proposed BOTH filtering is 56% and 68% average error and maximum error, respectively, than the existing RSSI filtering [18] and fusion filtering [13].…”

Section: Experimental Performancementioning

confidence: 83%

A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

Halder

Kim

2012

Journal of Computer Networks and Communications

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Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator-(RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

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“…Different sensors provide different range of accuracy from centimeters to room level. One of these is the physical means used for localization, for example, through the RF attenuation in the electromagnetic (EM) waves [4,9,10,14] or the time required to cover the distance between transmitter and receiver (ultra-wideband). If using ultrasonic pulses, one could also use the time of arrival or time difference between the arrivals of the waves [15].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…The aim of this paper is to achieve an efficient localization algorithm via integration of previous proposals [5][6][7][8][9][10][11][12][13]. In this paper, the proposed protocol intends to improve the existing algorithms using RSSI and link quality indicator (LQI) values.…”

Section: Introductionmentioning

confidence: 99%

Advanced Smoothing Approach of RSSI and LQI for Indoor Localization System

Halder

Giri

Kim

2015

International Journal of Distributed Sensor Networks

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Ubiquitous indoor environments often contain substantial amounts of metal and other similar reflective materials that affect the propagation of radio frequency signals in important ways, causing severe multipath effects, including noise and interference, when measuring the signal strength between sender and receiver. To minimize the noise level, this study proposes advanced fusion filter (AFF) and improved fusion filter (IFF) using received signal strength indicator (RSSI) and link quality indicator (LQI) by using feedback filter. The aim of this research was to provide a low cost, simple technique based on RSSI and LQI values, provided by ZigBee module without considering needs to change the system according to specific indoor environments. The proposed technique could efficiently decrease huge amount of noise level from the original signal. To check the performance of the proposed technique, this study applied median filter and Savitzky-Golay filter to compare the performance of AFF and IFF. Further, the statistical analysis technique of cross-correlation method was used to check the similarity between original signal and filtered signal. The simulation results demonstrate the efficiency of the proposed RF-based indoor location determination.

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On-Line Ranging for Mobile Objects Using ZIGBEE RSSI Measurement

Cited by 8 publications

References 3 publications

Adaptive Filtering for Indoor Localization using ZIGBEE RSSI and LQI Measurement

Adaptive Filtering for Indoor Localization using ZIGBEE RSSI and LQI Measurement

A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

Advanced Smoothing Approach of RSSI and LQI for Indoor Localization System

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